By the correlation of Tb/Yb normalized to chondrite –C1 [39], that make less than 1.8 (except some of the trials of Stanolir complex) it can be stated that, spinel peridotites were the mag‐ ma-forming substratum for the formation of the andesite of the volcanic complex. By all that the stage of the melt of the substratum of the spinel peridotite was decreasing from the vol‐ canites of Poyarka complex to the volcanites of Stanolir complex (Fig. 32). The coefficient of REE = 2.5-4.3. (KREE = 0.1La/Yb+Ho/Yb+(Dy+Ho)/(Yb+Lu) by [40], elements normalized to chondrite [41]). Such values confirm the presence of pyroxene in the melting substrata. By the ratio Ni/Co [42] the rocks of Poyarka (completely), Stanolir (mostly) and Burunda (subvolcanic) complexes are derivatives from the melts of the mantle. The derivatives of the melt of the lower crust formations are the lavas of Burunda complex and (rarely) Stanolir com‐ plex. Thus, the rocks of the three complexes can be partly examined as primary. It is con‐ firmed by the absence (or a weak presence) of Eu anomalies, that is one of the criteria of the primary nature of magmas [43]. By the correlation of the incoherent elements: Nb/Ta - U/Nb (Fig. 7 b) the formations of the complexes are comparable with the rocks of the subduction type of the Sredinnii mountain ridge of Kamchatka. By the correlations Th/Yb-Ta/Yb (Fig. 7а) the volcanites of Stanolir complex are displaced to the side of the enriched mantle. The relations of the coherent elements (Ce/La, Zr/La, Nb/La, Th/La, Yb/La) are not only close to the constant values, but they also correlate with each other. This confirms the belonging of the rocks of the three complexes to a singular magmatic stage. The derivatives of the stage underwent the evolution because of the decay of the subduction processes in the frames of the researched region. Many authors connect the lowering of the concentration of Sr and growth of the concentration of Ce and Th with the "decay" of the subduction [44 - 49, 36]. It can be seen in the geochemical characteristics of the rocks of the researched complexes in the direction from the volcanites of Poyarka to the volcanites of Stanolir complex: Sr – from 1029 ppm to 153 ppm, Ce – from 28.52 ppm to 75.07 ppm, Th – from 1.7 ppm to 15.89 ppm. They belong to the singular magmatic process that confirms the ratio of Zr/Nb - Nb/Th. Accord‐ ing to the ratio all this formations were melted from the source that is close to the source type EN [50] with the presence of a component of a depleted source. Series of the geochemi‐ cal indicators (Nb/La, La/Ta, Ta/Th, et al.) show that magmas of the volcanites were also un‐ derwent by the contaminations of the crust material. According to the ratio Ce/Y (less than 4) and La/Nb (less than 3.5) the formation of the rocks of the researched complexes occurred at the expense of the mixture with the crust of the product of a partly melt of the spinel peri‐ dotite of the mantle [51].

**1.** By the ratio (Nb/La)n –(Nb/Th)n [52] the rocks of Burunda complex correspond with island arc lavas - the contaminated formations of the continental crust. The af‐ fection of the crust contamination is confirmed by the high values of the ratio

The Role of the Andesitic Volcanism in the Understanding of Late Mesozoic Tectonic Events of Bureya-Jziamysi

Superterrain, Russian Far East http://dx.doi.org/10.5772/51908 107

**2.** Formations of Burunda complex are inconsistently laying on the terrigene deposits of Ogodzha suite of Aptian – Albian age. The Ogodzha deposits are traced in the mining excavations under the formations of Burunda rock strata (Fig. 3). They are stated in sin‐ gular tectonic blocks in southern direction. That points to a more wide development of the rocks as in primary variant. The coal-bearing deposits of the suite lay on the borders of Paleozoic and Early Mesozoic (Triassic) age of the superterrain transgressively. They form a flat monocline with the dip from 8 to 30о. Tuff material is present in the composi‐ tion of the suite. It points at the parallel volcanic activity during the Ogodzha sediment accumulation. The following intensification of the volcanic activity (111-105 Ma) leads to the formation of Burunda volcanic complex. It can be proposed that Burunda volcan‐ ites are a fragment of an island arc that was formed on the edge of Bureya-Jziamysi su‐ perterrain and the deposits of Ogodzha suite are part of a sediment complex formed in

**3.** The volcanic activity completes in the frames of the researched territory by the forma‐ tion of the acid – alkali volcano-plutonic complex at 101 – 99 Ma. The formations of the complex are characterized as intraplate origin. Their geochemical signatures indicate of its source is close to an enriched mantle in the primary melt of the source region of

**4. Geodynamic situations of the formation of the Late Mesozoic volcanic**

The first introductions about possible geodynamical situations in the frames of the re‐ searched region were made L.P. Sonenshein with co-authors (1990), who thought that Meso‐ zoic magmatic formations could be a product of activity of a subduction-related volcanism or a "hot spot" activity. Farther the same variants were elaborated by V.V., Yarmoluk and B.I. Kovalenko [53, 54], I.V. Gordienko [55], V.G. Moiseenko [56]. B.A. Natalin proposed both subductional and collisional situations [57]. Chinese geologists [58] researched a model of the formation of Mesozoic magmatism in the situation of a transformed continental edge. Analogical point of view was presented by A.I. Khanchuk and V.V. Ivanov [59]. The high precision values about the age and the volcanites geochemical composition of the rocks of the volcano-plutonic complexes of the region were absent during the composition of the ge‐ odynamical reconstructions listed above. The values are partly derived at the present time. All the suggested geodymanical reconstructions include the interdependence of North-

Asian and Sino-Korean cratons and platforms of the oceanic crust of the Pacific.

Northern border of Bureya-Jziamysi superterrain is a southern border of Mongol-Okhotsk orogenic belt. Along the belt the Late Mesozoic formations of the bimodal series are devel‐

206Pb/204Pb (389.6) [23].

a behind-the-arc basin.

**complexes of Bureya-Jziamysi superterrain**

melts [11].

**Figure 10.** The location of the rocks of the andesite volcanic complexes on the diagram (Ce/Yb)n-(Ce)n (conditional marks are listed on the fig. 8). The model trends of melting by [38].

Based on the comparison of the formation time, petro- and geochemical characteristics, be‐ longing to the singular magmatic process of the rocks of the andesite formation of the north‐ ern flank of Bureya-Jziamysi superterrain of Poyarka, Burunda and Stanolir complexes, the followings can be stated:

**1.** By the ratio (Nb/La)n –(Nb/Th)n [52] the rocks of Burunda complex correspond with island arc lavas - the contaminated formations of the continental crust. The af‐ fection of the crust contamination is confirmed by the high values of the ratio 206Pb/204Pb (389.6) [23].

primary nature of magmas [43]. By the correlation of the incoherent elements: Nb/Ta - U/Nb (Fig. 7 b) the formations of the complexes are comparable with the rocks of the subduction type of the Sredinnii mountain ridge of Kamchatka. By the correlations Th/Yb-Ta/Yb (Fig. 7а) the volcanites of Stanolir complex are displaced to the side of the enriched mantle. The relations of the coherent elements (Ce/La, Zr/La, Nb/La, Th/La, Yb/La) are not only close to the constant values, but they also correlate with each other. This confirms the belonging of the rocks of the three complexes to a singular magmatic stage. The derivatives of the stage underwent the evolution because of the decay of the subduction processes in the frames of the researched region. Many authors connect the lowering of the concentration of Sr and growth of the concentration of Ce and Th with the "decay" of the subduction [44 - 49, 36]. It can be seen in the geochemical characteristics of the rocks of the researched complexes in the direction from the volcanites of Poyarka to the volcanites of Stanolir complex: Sr – from 1029 ppm to 153 ppm, Ce – from 28.52 ppm to 75.07 ppm, Th – from 1.7 ppm to 15.89 ppm. They belong to the singular magmatic process that confirms the ratio of Zr/Nb - Nb/Th. Accord‐ ing to the ratio all this formations were melted from the source that is close to the source type EN [50] with the presence of a component of a depleted source. Series of the geochemi‐ cal indicators (Nb/La, La/Ta, Ta/Th, et al.) show that magmas of the volcanites were also un‐ derwent by the contaminations of the crust material. According to the ratio Ce/Y (less than 4) and La/Nb (less than 3.5) the formation of the rocks of the researched complexes occurred at the expense of the mixture with the crust of the product of a partly melt of the spinel peri‐

106 Updates in Volcanology - New Advances in Understanding Volcanic Systems

**Figure 10.** The location of the rocks of the andesite volcanic complexes on the diagram (Ce/Yb)n-(Ce)n (conditional

Based on the comparison of the formation time, petro- and geochemical characteristics, be‐ longing to the singular magmatic process of the rocks of the andesite formation of the north‐ ern flank of Bureya-Jziamysi superterrain of Poyarka, Burunda and Stanolir complexes, the

marks are listed on the fig. 8). The model trends of melting by [38].

dotite of the mantle [51].

followings can be stated:

### **4. Geodynamic situations of the formation of the Late Mesozoic volcanic complexes of Bureya-Jziamysi superterrain**

The first introductions about possible geodynamical situations in the frames of the re‐ searched region were made L.P. Sonenshein with co-authors (1990), who thought that Meso‐ zoic magmatic formations could be a product of activity of a subduction-related volcanism or a "hot spot" activity. Farther the same variants were elaborated by V.V., Yarmoluk and B.I. Kovalenko [53, 54], I.V. Gordienko [55], V.G. Moiseenko [56]. B.A. Natalin proposed both subductional and collisional situations [57]. Chinese geologists [58] researched a model of the formation of Mesozoic magmatism in the situation of a transformed continental edge. Analogical point of view was presented by A.I. Khanchuk and V.V. Ivanov [59]. The high precision values about the age and the volcanites geochemical composition of the rocks of the volcano-plutonic complexes of the region were absent during the composition of the ge‐ odynamical reconstructions listed above. The values are partly derived at the present time.

All the suggested geodymanical reconstructions include the interdependence of North-Asian and Sino-Korean cratons and platforms of the oceanic crust of the Pacific.

Northern border of Bureya-Jziamysi superterrain is a southern border of Mongol-Okhotsk orogenic belt. Along the belt the Late Mesozoic formations of the bimodal series are devel‐ oped [12, 13]. The formation of the complexes occurred under conditions of collisional com‐ pression, agreed by the approaching of North-Asian and Sino-Korean cratons and a possible influence of plume on the area that is under conditions of the collisional compression. The bimodal complexes have a lineal separation in the frames of Mongol Okhotsk belt. But on the East their separation is framed by the structures of Bureya-Jziamysi superterrain. It might be proposed that the given theory had not suffered such processes. The Chinese geol‐ ogists worked out a scheme of tectonic development for the territory of Bureya-Jziamysi su‐ perterrain on the results based on a seismic transect Manchzhuria – Suifankhe laid transversely the Songliao depression [58, 60]. It is stated that the extension, provoked by the changes of the Izanaga plate movement dominated in Late Jurassic – Early Cretaceous in the Songliao basin [58, 55]. According to the data [61] a sharp change of the direction (on 50о) and speed (from 5.3 to 30 cm per year) of the subduction of Izanaga plate under the eastern edge of Bureya-Jziamysi superterrain took place at about 135 Ma. This provoked the forma‐ tion of series of the left displacements СВ and С-СВ extension and formation of the rift-like structures [58]. The structures were field with coal-bearing terrigenous sediments and vol‐ canic formations of acid composition. During the period a complex of acid volcanites aged 136 Ma is forming in the frames of the studied territory.

Farther than 136 – 120 Ma the territory became as a passive continental edge. The temporal stage of the formation of Poyarka, Burunda and Stanolir volcanic complexes relates to the moment when the Izanaga plate changed its movement direction from northern to northwestern. With that, the angle of the turn of the plate was almost 30о [61]. During the period there was a flat subduction of the oceanic plate under the eastern edge of Asia with a speed more than 20 cm per year [61]. That's why the formation of the rocks on the continental crust under the conditions of the subduction seems to be possible.

**Figure 11.** Palinspatic reconstruction of the location of the main tectonic units of the south of the Far East of Russia in Jurassic (a) and Neogene (b) by U.S. Bretshtein and A.V. Klimov [6]. SP – Siberian plate NChP – North-Chinese plate, BJ – Bureya-Jziamysi superterrain (by U.S. Bratshtein and A.B. Klimov – Khingan – Bureya plate), MO – Mongol-Okhotsk ter‐

The Role of the Andesitic Volcanism in the Understanding of Late Mesozoic Tectonic Events of Bureya-Jziamysi

Superterrain, Russian Far East http://dx.doi.org/10.5772/51908 109

The magmatic processes decay completely in the interval of 105–101 Ma on the territory of Bureya-Jziamysi superterrain. The situation of the continental "riftogenesis" or the situation of a transforming continental edge begins to appear 101 Ma [59, 63], what was reflected on the formation of the acid – alkaline rocks of the intraplate volcano-plutonic complex. As the most possible tectonic scenario by the formation of the volcano-plutonic complex the author examines the collision of Bureya-Jziamysi and Badzhalsky terrains [11] which is confirmed

On the base of the confrontation of the formation time, petro- and geochemical characteris‐ tics, belonging to a singular magmatic focus of the rocks of andesite formation of Bureya-Jziamysi superterrain, the Poyarka, the Bureya and the Stanolir volcanic complexes, it might be stated that their formation happened more or less simultaneously (with a leading at the formation of Poyarka complex at the beginning). All the formations of the studied volcanic complexes have similar characteristics and are related to subductional volcanites of calc al‐ kali series. The changes of the content of major- and minor element composition of the vol‐ canic complexes may be explained by the mixture of the mantle source, fluids at the partial melt of the lower continental crust and a subducting plate at its contact with the mantle. The last fact is confirmed by the presence of "adakite component" – the shows of melt of the oce‐ anic plate in the rocks of Poyarka and Burunda complexes: the presence of magnesial ande‐

rain, BD – Badzhal terrain

**5. Conclusion**

by the paleomagnetic data (Fig. 10).

Paleomagnetic data were obtained by U.S. Bretshtein and A.V. Klimov [6] for the main tec‐ tonic units of the southern part of the Far East of Russia (Fig. 11). According to the data, dur‐ ing the Jurassic the Bureya-Jziamysi superterrain was at large distance from the North-Korean plate. The distance was about a few thousands of kilometers.

140 Ma the superterrain (Bureya block dew to [62]) was located much more on the north from it's nowadays dislocation according to the data of geological and geophysical includ‐ ing GPS data [62].

Thus, it might be proposed, that during the period 120 – 105 Ma there was a volcanic activi‐ ty on the territory of Bureya-Jziamysi superterrain which was controlled by the subduction processes. During the period the volcanic formations were loosing their typical subduction petro-chemical characters, for instance the composition of Sr decreased while the amount of Nb, Ta, Rb, К, Ti increased over time. Such values in the composition of the rocks show the attenuation of the active subduction processes. The temporal stage of the formation of the rocks of the three complexes correlates to the stage of the flat subduction of the oceanic plate Izanaga under the edge of Bureya-Jziamysi superterrain. The biggest magmatic activity took place during the period of the change in the movement direction of the oceanic plate from almost northern to north-western with the growth of speed till 23.5 cm per year [61].

The Role of the Andesitic Volcanism in the Understanding of Late Mesozoic Tectonic Events of Bureya-Jziamysi Superterrain, Russian Far East http://dx.doi.org/10.5772/51908 109

**Figure 11.** Palinspatic reconstruction of the location of the main tectonic units of the south of the Far East of Russia in Jurassic (a) and Neogene (b) by U.S. Bretshtein and A.V. Klimov [6]. SP – Siberian plate NChP – North-Chinese plate, BJ – Bureya-Jziamysi superterrain (by U.S. Bratshtein and A.B. Klimov – Khingan – Bureya plate), MO – Mongol-Okhotsk ter‐ rain, BD – Badzhal terrain

The magmatic processes decay completely in the interval of 105–101 Ma on the territory of Bureya-Jziamysi superterrain. The situation of the continental "riftogenesis" or the situation of a transforming continental edge begins to appear 101 Ma [59, 63], what was reflected on the formation of the acid – alkaline rocks of the intraplate volcano-plutonic complex. As the most possible tectonic scenario by the formation of the volcano-plutonic complex the author examines the collision of Bureya-Jziamysi and Badzhalsky terrains [11] which is confirmed by the paleomagnetic data (Fig. 10).

### **5. Conclusion**

oped [12, 13]. The formation of the complexes occurred under conditions of collisional com‐ pression, agreed by the approaching of North-Asian and Sino-Korean cratons and a possible influence of plume on the area that is under conditions of the collisional compression. The bimodal complexes have a lineal separation in the frames of Mongol Okhotsk belt. But on the East their separation is framed by the structures of Bureya-Jziamysi superterrain. It might be proposed that the given theory had not suffered such processes. The Chinese geol‐ ogists worked out a scheme of tectonic development for the territory of Bureya-Jziamysi su‐ perterrain on the results based on a seismic transect Manchzhuria – Suifankhe laid transversely the Songliao depression [58, 60]. It is stated that the extension, provoked by the changes of the Izanaga plate movement dominated in Late Jurassic – Early Cretaceous in the Songliao basin [58, 55]. According to the data [61] a sharp change of the direction (on 50о) and speed (from 5.3 to 30 cm per year) of the subduction of Izanaga plate under the eastern edge of Bureya-Jziamysi superterrain took place at about 135 Ma. This provoked the forma‐ tion of series of the left displacements СВ and С-СВ extension and formation of the rift-like structures [58]. The structures were field with coal-bearing terrigenous sediments and vol‐ canic formations of acid composition. During the period a complex of acid volcanites aged

Farther than 136 – 120 Ma the territory became as a passive continental edge. The temporal stage of the formation of Poyarka, Burunda and Stanolir volcanic complexes relates to the moment when the Izanaga plate changed its movement direction from northern to northwestern. With that, the angle of the turn of the plate was almost 30о [61]. During the period there was a flat subduction of the oceanic plate under the eastern edge of Asia with a speed more than 20 cm per year [61]. That's why the formation of the rocks on the continental

Paleomagnetic data were obtained by U.S. Bretshtein and A.V. Klimov [6] for the main tec‐ tonic units of the southern part of the Far East of Russia (Fig. 11). According to the data, dur‐ ing the Jurassic the Bureya-Jziamysi superterrain was at large distance from the North-

140 Ma the superterrain (Bureya block dew to [62]) was located much more on the north from it's nowadays dislocation according to the data of geological and geophysical includ‐

Thus, it might be proposed, that during the period 120 – 105 Ma there was a volcanic activi‐ ty on the territory of Bureya-Jziamysi superterrain which was controlled by the subduction processes. During the period the volcanic formations were loosing their typical subduction petro-chemical characters, for instance the composition of Sr decreased while the amount of Nb, Ta, Rb, К, Ti increased over time. Such values in the composition of the rocks show the attenuation of the active subduction processes. The temporal stage of the formation of the rocks of the three complexes correlates to the stage of the flat subduction of the oceanic plate Izanaga under the edge of Bureya-Jziamysi superterrain. The biggest magmatic activity took place during the period of the change in the movement direction of the oceanic plate from

almost northern to north-western with the growth of speed till 23.5 cm per year [61].

136 Ma is forming in the frames of the studied territory.

108 Updates in Volcanology - New Advances in Understanding Volcanic Systems

crust under the conditions of the subduction seems to be possible.

Korean plate. The distance was about a few thousands of kilometers.

ing GPS data [62].

On the base of the confrontation of the formation time, petro- and geochemical characteris‐ tics, belonging to a singular magmatic focus of the rocks of andesite formation of Bureya-Jziamysi superterrain, the Poyarka, the Bureya and the Stanolir volcanic complexes, it might be stated that their formation happened more or less simultaneously (with a leading at the formation of Poyarka complex at the beginning). All the formations of the studied volcanic complexes have similar characteristics and are related to subductional volcanites of calc al‐ kali series. The changes of the content of major- and minor element composition of the vol‐ canic complexes may be explained by the mixture of the mantle source, fluids at the partial melt of the lower continental crust and a subducting plate at its contact with the mantle. The last fact is confirmed by the presence of "adakite component" – the shows of melt of the oce‐ anic plate in the rocks of Poyarka and Burunda complexes: the presence of magnesial ande‐ sites and andesites, high concentrations of Sr and Ba, low concentrations of HREE with the high ratios of La/Yb and low ratios of K/La. Thus, it might be proposed that the existence of a simultaneous volcanic activity during 120 – 105 Ma on the territory of Bureya-Jziamysi su‐ perterrain, conditioned by the subductional processes. During the period, the volcanic for‐ mations loose their typical subductional signatures as reflected by the lower Sr concentration of the rocks, the increase in concentration of Nb, Ta, Rb, К, Ti, what is inferred to be connected to the decay of the active subduction processes.

[6] Khanchuk A.I. Geodynamics, magmatism and metallogeny of the Russian East, Vla‐

The Role of the Andesitic Volcanism in the Understanding of Late Mesozoic Tectonic Events of Bureya-Jziamysi

Superterrain, Russian Far East http://dx.doi.org/10.5772/51908 111

[8] Sharov, V. N., Fefelov, N. I., Jablonovsky, B. V., et al. (1992). Dating of the low Proter‐ ozoic stratificated formations of Patomsky plateau Pb/Pb method, Reports of the

[9] Pavlov, Ju. A., & Parfenov, L. M. The abyssal structure of the Eastern Sayan and Southern Aldan frames of the Siberian plate, Novosibirsk: Nauka, (1973). p.

[10] Parfenov, L. M., Bulgatov, A. N., & Gordienko, I. V. (1996). Terrains and the forma‐ tion of the orogenic belts of Transbaikal, Russian Journal of Pacific Geology, July-Au‐

[11] Derbeko, I.M(2012a). Later Mesozoic volcanism of Mongol-Okhotsk belt (eastern end and the southern framing of eastern member of the belt), Saarbruken: LFMBERT

[12] Bogatikov, O. A., & Kovalenko, V. I. (2006). Types of magma and their sources in the history of the Earth, Moscow: Institute of Geology of ore deposits Russian Academy

[13] Derbeko, I. M. (2012). Bimodal volcano-plutonic complexes in the frames of Eastern member of Mongol-Okhotsk orogenic belt, as a proof of the time of final closure of Mongol-Okhotsk basin, In: Updates in volcanology- A Comprehensive Approach to

[14] Geologycal map of Priamurie and neighbouring territories.Scale 1:2 500 000 ((1999). Explanatory note, Editors L.I. Krasny, A.S Volsky, I.A. Vasilev, Pen Yunbiao, Suy Yancyan, Van In, St. Petersburg- Blagoveschensk- Harbin: Ministry of nature resour‐ ces of Russian Federation, Ministry of geology and mineral resources of China, 135 p.

[15] Sorokin, A. A., Sorokin, A. P., Ponomarchuk, V. A., Travin, A. V., Kotov, A. B., & Melnikova, O. V. (2008). Basaltic andesites of Amur-Zeja depression in Aptian: new geochemical and 40Ar/39Ar- geochronological data, Reports on Earth Science, April

[16] Derbeko, I. M., Sorokin, A. A., Ponomarchuk, V. A., & Sorokin, A. P. (2004). Timing of Mesozoic magmatism in Khingan-Okhotsk volcano-plutonic belt (Russian Far

[17] Sorokin, A. A., Ponomarchuk, V. A., Derbeko, I. M., & Sorokin, A. P. (2004). New da‐ ta on the geochronology of the magmatic associations of Khingan-Olonoy volcanic zone (Far East of Russia), Russian Journal of Pacific Geology, ISSN-0207-4028., 23(2),

[18] Sorokin, A. A., Ponomarchuk, V. A., Derbeko, I. M., & Sorokin, A. P. (2005). Geochro‐ nology and geochemical peculiarities of Mesozoic associations of Khingan-Olonoisk

East), Geochim. et Cosmochim. Acta. S. 1, 0016-7037, 68, A226.

Volcanological Problems. Chapter 5. InTech, 978-9-53307-434-4, 99-124.

[7] Geology of BAM zone. Editor L.I. Krasny, Leningrad: Nedra,(1988). p

Earth Science, April 1992), 0102-8334X, 324(5), 1081-1084.

Academic Publishing GmbH&Co.KG, 97 p. 978-3-84734-060-7

divostok: Dalnauka,(2006). 580-4-40634-557-2p.

gust 1996), ISSN-1819-7140., 15(4), 3-15.

of Science, 588-9-18013-428-0p.

2010), 0102-8334X, 421(4), 525-529.

52-62.

The dislocation and the geochemical characteristics of the rocks of the complexes show the dislocation of the moving of the subducted oceanic plate. Its northern territory was pointing to the side of the ocean at that moment. It might be also proposed that Bureya-Jziamysi su‐ perterrain was not a component of Amur microcontinent during the period of the formation of the three complexes, but it was an independent geological object. Its annexation to the Amur microcontinent occurred much later than Albian.

### **Author details**

### I.M. Derbeko

Institute of Geology and Nature Management FEB RAS, Blagoveschensk, Russia

### **References**

sites and andesites, high concentrations of Sr and Ba, low concentrations of HREE with the high ratios of La/Yb and low ratios of K/La. Thus, it might be proposed that the existence of a simultaneous volcanic activity during 120 – 105 Ma on the territory of Bureya-Jziamysi su‐ perterrain, conditioned by the subductional processes. During the period, the volcanic for‐ mations loose their typical subductional signatures as reflected by the lower Sr concentration of the rocks, the increase in concentration of Nb, Ta, Rb, К, Ti, what is inferred

The dislocation and the geochemical characteristics of the rocks of the complexes show the dislocation of the moving of the subducted oceanic plate. Its northern territory was pointing to the side of the ocean at that moment. It might be also proposed that Bureya-Jziamysi su‐ perterrain was not a component of Amur microcontinent during the period of the formation of the three complexes, but it was an independent geological object. Its annexation to the

Institute of Geology and Nature Management FEB RAS, Blagoveschensk, Russia

plates on the territory of USSR. Moscow: Nedra, 328 p., 1

[1] Parfenov, L. M., Popeko, L. I., & Tomurtogoo, O. (1999). Problems of tectonic of Mon‐ gol-Okhotsk orogenic belt, Russian Journal of Pacific Geology, September- October

[2] Sonenshein, L. P., Kuzmin, M. N., & Natapov, L. M. (1990). Tectonics of lithosphere

[3] Ashurkov, S. V., San'kov, A. I., Miroshnichenko, A. I., Lukhnev, A. V., Sorokin, A. P., Serov, M. A., & Byzov, L. M. (2011). GPS geodetic constraints on the kinematics of the Amurian plate, Geology & geophysics, February 2011), 0016-7886, 52(2), 299-311.

[4] Gatinsky, Yu. G., & Rundquist, D. V. (2004). Geodynamics of Eurasia- Plate tectonics and block tectonics, Geotectonics, January-February 2004), 0016-8521, 38(1), 3-20.

[5] Parfenov, L. M., Berezin, N. A., Khanchuk, A. I., Badarh, G., Belichenko, V. G., Bulga‐ tov, A. N., Drill, S. I., Kirillova, G. L., Kuzmin, M. I., Nokleberg, U., Prokopiev, A. V., Timofeev, V. F., Tomurtogoo, O., & Yan', H. (2003). The model of the formation of the orogenic belts of Central and Northern-Eastern Asia, Russian Journal of Pacific Geology,Pacific geology, November- December 2003), ISSN-1819-7140., 22(6), 7-41.

to be connected to the decay of the active subduction processes.

110 Updates in Volcanology - New Advances in Understanding Volcanic Systems

Amur microcontinent occurred much later than Albian.

1999), ISSN-1819-7140., 18(5), 24-43.

**Author details**

I.M. Derbeko

**References**

volcanic zone (the Far East of Russia), Stratigraphy and geological correlation, ISSN-0869-5938., 13(3), 63-78.

[31] Piskunov, B. I. (1987). Geologo-petrological specifics of the island arcs volcanism.

The Role of the Andesitic Volcanism in the Understanding of Late Mesozoic Tectonic Events of Bureya-Jziamysi

Superterrain, Russian Far East http://dx.doi.org/10.5772/51908 113

[32] Churikova, T., Dorendorf, F., & Woerner, G. (2001). Sources and fluids in mantle wedge below Kamchatka, evidence from across-arc geochemical variation, Journal of

[33] Kirillova, G. L. (2005). Late Mesozoic- Cenozoic sedimental basins of the continental edge of the south-eastern Russia: geodynamical evolution, coal- and oil-gas-bearing,

[34] Rollinson, H. R. (1995). Using Geohemical Data: Evalution, Presentation, Interpreta‐

[35] Tatsumi, Y., Hamilton, D. L., & Nesbitt, R. W. (1986). Chemical characteristics of fluid phase realeased from a subducted lithosphere and origin of are magmas: Evidence from high-pressure experiments and natural rocks, Journal of Volcanology and Geo‐

[36] Volynec, A. O., Antipin, V. S., Perepelov, A. B., & Anoshin, G. N. (1990). Geochemis‐ try of the volcanic series of the island arc system in application to geodynamics

[37] Pusankov, Ju. M., Volinec, O. N., Seliverstov, V. A., et al. (1990). Geochemical typifi‐ cation of magmatic and metamorphic rocks of Kamchatka, Novosibirsk: IGG SB AS

[38] Gill, J. B. (1981). Orogenic andesites and plate tectonic, New York,

[39] Wang, K., Plank, T., Walker, J. D., & Smith, E. I. (2002). A mantle melting profile the Basin and Range, SW USA, Journal of Geophysical Research, 0148-0227, 107(B1)

[40] Troshin, U. P., Grebenschikova, V. I., & Boiko, S. M. (1983). Geochemistry and petrol‐

[41] Sun, S. S., & Mc Donough, W. F. (1989). Chemical and isotopic systematics of oceanic basalts: implications for mantle composition and processes, In: Magmatism in the ocean basins (Editors: Saunders A.D., Norry M.J.), Special Publications of the Geolog‐

[42] Kogarko, L. I. (1973). Relation of Ni/Co- indicator of the mantle origin of magmas,

[44] Mc Kenzi, D. E., & Chappel, B. W. (1972). Shoshonitic and calc-alkaline lavas from the Highlands of Papua New Guinea, Contributions to mineralogy and petrology,

[43] Balashov, Ju. A. (1976). Geokhimija of the rare elements, Moscow: Nauka, 267 p.

ogy of the rare-earth plumaezite granites, Novosibirsk: Nauka, 183 p.

Petrology, August 2001), 0022-3530, 42(8), 1567-1593.

Geotektonika, October-November 2005), 0001-6853X.(5), 62-82.

thermal Research, September 1986), 0377-0273, 29(1-4), 293-303.

(Kamchatka), Geology & geophysics, 0016-7886(5), 3-13.

Moscow: Nauka, 236 p.

tion, London, 352 p.

RF, 1990, 259 p.

354-0-10666-939-0p.

ical Society, London, , 42, 313-345.

0010-7999, 35(1), 50-63.

Geokhimiya, October 1973), 0016-7525(10), 1446-1449.

[31] Piskunov, B. I. (1987). Geologo-petrological specifics of the island arcs volcanism. Moscow: Nauka, 236 p.

volcanic zone (the Far East of Russia), Stratigraphy and geological correlation,

[19] Martinuk, M. V., Riamov, S. A., & Kondratieva, V. A. (1990). Explanatory report to the scheme of dismemberment and correlation of magmatic complexes of Khabarov‐ sky region and Amur region, Khabarovsk, Russia: Industrial geological organization,

[20] Le Bas, M., Le Maitre, R. W., Streckeisen, A., & Zanettin, B. (1986). A chemical classi‐ fication of volcanic rocks based on the total-silica diagram, Journal of Petrology, 27,

[21] Irvine, T. N., & Baragar, W. R. (1971). A guide to the chemical classification of the

[22] Resolutions of IV interdepartmental regional stratygraphic conference about Cambri‐ an and Phanerozoic of the South of the Far East and East of Transbaikal, Scheme 35,

[23] Agaphonenko, S. G. Explanatory note for state geological map of Russian Federation. Scale 1: 200 000 (Second edition). Tugur series. Page N-XXVI, St.- Petersburg: VSGEI,

[24] Derbeko, I. M., Agafonenko, S. G., Kozyrev, S. K., & Vyunov, D. L. (2010). The Umle‐ kan-Ogodzha volcanic belt (the problem bodily separation), Lithosphere, May-June

[25] Rasskasov, S. V., Ivanov, A. V., Travin, A. V., Brandt, I. S., & Brandt, S. B. (2003). Ar-39Ar and K-Ar dating of the volcano rocks of Albian of Priamuria and Transbai‐ kal, In: Isotopic geochronology in solving problems of geodynamics and ore genesis,

[26] Evtushenko, V. A. (1978). Stratigraphy and geochronology of the Cretaceous forma‐ tions Small Khingan, In: Stratigraphy of the Far East, Vladivostok: FEGI, , 152-153.

[27] Gonevchuk, V. G. (2002). Tin-bearing systems of the Far East: magmatism and ore

[28] Kirjanova, V. V. (2000). New in stratigraphy of the Cretaceous South of the Amur re‐ gion. In: Correlation of Mesozoic continental formations of the Far East and East of

[29] Wang, P. J., Liu, W. Z., Wang, S. X., & Song, W. H. (2002). Ar/39Ar and K/Ar dating on the volcanic rocks in the Songliao basin, NE China; constraints on stratigraphy and basin dynamics, International Journal of Earth Science, 0167-4487X., 91, 331-340.

[30] Velicoslavinsky, S. D., & Glebovicky, V. A. (2005). New discrimination diagram for classification of the island arc and continental basalts on the base of petrochemical

data, Reports of Akademii Nauk, March 2005), 0869-5652, 401(2), 213-216.

St.-Petersburg: Center of information culture, 2003, , 410-413.

genesis, Vladivostok: Dalnauka, 580440251p.

Trans-Baikal region, Chita: GGUP, , 49-52.

common volcanic rocks, Canadian Journal Earth Science, , 8, 523-548.

(1993). Khabarovsk: Khabarovsk state mining-geological enterprise, 22 p.

ISSN-0869-5938., 13(3), 63-78.

112 Updates in Volcanology - New Advances in Understanding Volcanic Systems

215 p.

0022-3530, 745-750.

(2002). , 53.

2010), 1681-9004(3), 70-77.

[45] Whitford, D. J., Nicholls, J. A., & Taylor, S. R. (1979). Spatial variations in the geo‐ chemistry of Quaternary lavas across the Sunda arc in Java and Bali, Contributions to mineralogy and petrology, 0010-7999, 70(3), 341-356.

fenhe geoscience transekt region. In: M-SGT geological research group ed. Geological Research on Litosphere Structure and its Evolution of Mongholui- Suifenhe Geosci‐ ence Transect Region of China, Beijing: Seismic Publishing House. in Chinese).,

The Role of the Andesitic Volcanism in the Understanding of Late Mesozoic Tectonic Events of Bureya-Jziamysi

Superterrain, Russian Far East http://dx.doi.org/10.5772/51908 115

[59] Khanchuk, A. I., & Ivanov, V. V. (1999). Mezo-Cenozoic geodynamical situations and a golden ore formation of the Far East, Geology & Geophysics, November 1999),

[60] Yang Baojun, Liu Cai, Zhou Yang, Liang Tiecheng, Tang Dayi.Study of the crust structure in the Anda-Zhaozhou-Harbin transect region using deep reflecthion meth‐ od. In: M-SGT geophusical research group ed. Research on geophusical field and deep structural characteristics of Manzhouli- Suifenhe geoscience transekt region of China. Beijing: Seismic Publishing House, (1995). In Chinese with Engl. Abstr.).,

[61] Maruyama, S., & Seno, T. (1986). Orogeny and relative plate motions: example of the Japanese Islands, Tectonophysics, 127, 3-4, 1, (August 1986), 0040-1951, 305-329.

[62] Pisarevsky, S. A. (2005). New edition of the Global Paleomagnetic Database. EOS

[63] Khanchuk, A. I. (2001). Pre-Neogene tectonics of the Sea-of-Japan region: A view

Transactions American Geophysical Union, 0096-3941, 86(17), 170.

from the Russian, Earth Science, 1674, 55(5), 275-291.

14-25.

100-113.

0016-7886, 40(11), 1635-1645.

fenhe geoscience transekt region. In: M-SGT geological research group ed. Geological Research on Litosphere Structure and its Evolution of Mongholui- Suifenhe Geosci‐ ence Transect Region of China, Beijing: Seismic Publishing House. in Chinese)., 14-25.

[45] Whitford, D. J., Nicholls, J. A., & Taylor, S. R. (1979). Spatial variations in the geo‐ chemistry of Quaternary lavas across the Sunda arc in Java and Bali, Contributions to

[46] Riou, R., Dupuy, C., & Dostal, J. (1981). Geochemistry of coexisting alkaline and calcalkaline volcanic rocks from Northern Azerbaijan (NW Iran), Journal of Volcanology

[47] Allan, J. F., & Garmichael, J. S. E. (1984). Lamprophyric lavas in the Colima graben, SW Mexico, Contributions to mineralogy and petrolog, 0010-7999, 88(3), 203-216.

[48] Mitropoulos, P., Tarney, J., Saunders, A. D., & Marsh, N. G. (1987). Petrogenesis of Cenozoic volcanic rocks from the Aegean Island arc, Journal of Volcanology and Ge‐

[49] Saunders, A. D., Rogers, D., & Marriner, G. F. (1987). Geochemistry of Cenozoic vol‐ canic rocks, Baja California, Mexico: implications for the petrogenesis of post-sub‐ duction magmas, Journal of Volcanology and Geothermal Research, ISSN-0377-0273.,

[50] Genshaft, Yu. S., Grachev, A. F., Saltykovsky, A., & Ya, . (2006). Geochemistry of Cenozoic basalts of Mongolia: the problem of genesis of mantle sources, Geology &

[51] Hoffman, A. W. (1997). Mantle geochemistry: the message from oceanic volcanism,

[52] Puchtel, I. S., Hofmann, A. W., Mezger, K., Jochum, K. P., Shchipansky, A. A., & Sam‐ sonov, A. V. (1998). Oceanic plateau model for continental crustal growth in the Archaean: A case study from the Kostomuksha greenstone belt, NW Baltic Shield,

[53] Yarmoluk, V. V., & Kovalenko, V. I. (1991). Riftogene magmatism of the active conti‐

[54] Yarmoluk, V. V., Kovalenko, V. I., & Kuzmin, M. I. (2000). North-Asian superplume in Phanerozoic: magmatism and abyssal geodynamics, Geotectonics, September- No‐

[55] Gordienco, V. I., Klimuk, V. S., & Cuan, Khen. (2000). Upper Amur vulkano-plutonic

[56] Moiseenco, V. G., & Sahno, V. G. (2000). Plum magmatism and mineralogy of Amur

[57] Natalin, B. A. (1991). Mezozoic accretion and collision tectonics of the Far East sout of the USSR, Russian Journal of Pacific Geology, ISSN-1819-7140.(5), 3-23.

[58] Liu Zhaojun, Wang Xiaolin, Lui Wanghu, Xue Fang, Zhao(1994). Mapping Forma‐ tional mechanism of the Songliao and Hailaer Mesozoic basins of Mongholui- Sui‐

belt of East Asia, Geology & Geophysics, 0016-7886, 41(12), 1655-1669.

Earth and Planetary Science Letters, 1998, 155, 0001-2821X., 57-74.

nental margins and their ore content. Moscow: Nauka, 263 p.

megastructure, Blagoveschensk: AmurKSRI, 160 p.

mineralogy and petrology, 0010-7999, 70(3), 341-356.

114 Updates in Volcanology - New Advances in Understanding Volcanic Systems

othermal Research, 32, SSN-0377-0273.(1), 177-194.

Geophysics, March 2006), 0016-7886, 47(3), 377-389.

Nature, 1752-0894, 385, 219-229.

vember 2000), 0016-8521(5), 3-29.

32(1), 223-246.

and Geothermal Research, ISSN-0377-0273., 11(2), 253-276.

**Section 2**

**Volcanic Hazards**

**Section 2**

### **Volcanic Hazards**

**Chapter 3**

**Application of the Bayesian Approach to Incorporate**

Geological hazard assessments based on established statistical techniques are now commonly used as a basis to make decisions that may affect society over the long-term (0.1 – 1 Ma).

(1) The probability of a 'volcanic event' occurring such as a dike intrusion or a new strato- or

A challenge with the long-term probabilistic assessment of future volcanism in relation to the siting of, for example geological repositories is that because new volcano formation is rare, uncertainties in models are inherently large [10]. Sites for nuclear facilities in particular must be located in areas of very low geologic risk [11]. Recent studies have been carried out looking at the hazard posed by volcanoes to nuclear power plants in Armenia [e.g. 9, 12] and Java, Indonesia [e.g. 13]. Here the focus was more on the consequences of an eruption at an existing volcano on the safety of an operating nuclear power plant. In the case of a geological repository for high and/or low level radioactive waste, the emphasis is on the consequences of new igneous activity such as a dike that may intrude the repository [e.g. 14] and transport the waste to the surface. In this case, the probability of a new volcano forming in the first place is very low (typically < 10-7/a) since by definition such facilities should be located away from existing Quaternary volcanoes. However the lack of volcano 'data' implies that addition information on the processes that control future long-term spatio-temporal distribution of volcanism are

> © 2013 Martin et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2013 Martin et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and reproduction in any medium, provided the original work is properly cited.

**Helium Isotope Ratios in Long-Term Probabilistic**

**Volcanic Hazard Assessments in Tohoku, Japan**

Andrew James Martin, Koji Umeda and

Additional information is available at the end of the chapter

(2) The consequences of the volcanic event e.g. [8 - 9].

Tsuneari Ishimaru

**1. Introduction**

http://dx.doi.org/10.5772/51859

Volcanic risk essentially consists of:

caldera volcano forming e.g. [1- 7]

## **Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic Hazard Assessments in Tohoku, Japan**

Andrew James Martin, Koji Umeda and Tsuneari Ishimaru

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51859

### **1. Introduction**

Geological hazard assessments based on established statistical techniques are now commonly used as a basis to make decisions that may affect society over the long-term (0.1 – 1 Ma). Volcanic risk essentially consists of:

(1) The probability of a 'volcanic event' occurring such as a dike intrusion or a new strato- or caldera volcano forming e.g. [1- 7]

(2) The consequences of the volcanic event e.g. [8 - 9].

A challenge with the long-term probabilistic assessment of future volcanism in relation to the siting of, for example geological repositories is that because new volcano formation is rare, uncertainties in models are inherently large [10]. Sites for nuclear facilities in particular must be located in areas of very low geologic risk [11]. Recent studies have been carried out looking at the hazard posed by volcanoes to nuclear power plants in Armenia [e.g. 9, 12] and Java, Indonesia [e.g. 13]. Here the focus was more on the consequences of an eruption at an existing volcano on the safety of an operating nuclear power plant. In the case of a geological repository for high and/or low level radioactive waste, the emphasis is on the consequences of new igneous activity such as a dike that may intrude the repository [e.g. 14] and transport the waste to the surface. In this case, the probability of a new volcano forming in the first place is very low (typically < 10-7/a) since by definition such facilities should be located away from existing Quaternary volcanoes. However the lack of volcano 'data' implies that addition information on the processes that control future long-term spatio-temporal distribution of volcanism are

© 2013 Martin et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Martin et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

needed. This has motivated several investigators to incorporate datasets in addition to the distribution and timing of past volcanic activity in volcanic probabilistic analyses [e.g. 15]. Bayesian inference has been used to combine geophysical datasets to probability distributions constructed from known historic volcano locations in order to estimate the location of future volcanism over a regional scale [1]. More recently, [16] used Bayesian inference to merge prior information and past data to construct a probability map of vent opening at the Campi Flegrei caldera in Italy.

Here we revisit the Bayesian approach developed by [1] where seismic tomographs and geo‐ thermal gradients were incorporated into probabilistic assessments by Bayesian inference in Tohoku. We apply the same Bayesian technique in the same study area to incorporate recently acquired helium isotopes into probabilistic hazard assessments; such noble gases have been shown to be excellent natural tracers for mantle-crust interaction owing to their inert chemical properties which means they are not altered by complex chemical processes. Moreover heli‐ um isotopes provide evidence for the presence of mantle derived materials in the crust, owing to the distinct isotopic compositions between the crust and the upper mantle [e.g. 17, 18]. We examine the link between volcanism and 3 He/4 He ratios that may infer possible regions of mag‐ ma generation and hence volcano formation. Such links between magmatism and elevated 3 He/4 He ratios have been proposed [e.g. 19, 20], but the link has not been examined quantita‐ tively in probabilistic based models. Finally we discuss the Bayesian method in developed by [1] in the context of recent approaches to incorporate multiple datasets [e.g. 21, 22].

### **2. Japan and the Tohoku region**

Japan is one of the most tectonically active regions in the world. Due to the dynamics of four plates, Quaternary volcanoes have formed along distinct volcanic fronts in east and west Japan (Figure 1).

The Tohoku region (Figure 2) is arguably one of the most extensively studied volcanic arcs in the world, particularly regarding the relationship between volcanism and tectonics. Moreover there have been numerous geological and geophysical investigations yielding high-quality datasets e.g., [23 - 29].

**3. Defining the volcanic event**

and Eurasian plates relative to the North American plate.

What do we mean here by 'volcano'? In developing probabilistic based models, one of the most difficult and challenging tasks is defining the 'volcanic event'. This is because the volcanic event defined has to be simple and consistent enough for the probabilistic based models to handle. To a certain extent the degree of consistency that can be realistically included in a model is largely constrained by the size of the study area and by the amount and quality of geological, geochemical and/or geophysical data available. The volcanic event could range from a single eruption to a series of eruptions. It could be defined as the existence of a relatively young cinder cone, spatter mound, maar, tuff ring, tuff cone, pyroclastic fall, lava flow or even a large composite volcano. On the other hand older edifices may have been eroded and/or covered by sedimentary deposits such as alluvium and thus be more difficult to locate and/or

**Figure 1.** Map showing the tectonic setting of Japan (Figure modified from [1]). The four main islands that make up Japan are located on or near the boundaries of four plates. Black triangles denote Quaternary volcanoes and the red lines depict the main volcanic fronts. The thin contour lines denote the depth of the subducting Pacific Plate beneath Japan. The velocities and arrows indicate the subduction rates and directions respectively of the Pacific, Philippine Sea

**PHILIPPINE SEA**

**2.5 cm/yr**

**8.5 cm/yr**

Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 121

**8.5 cm/yr**

**PLATE**

**0.9 cm/yr**

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Tohoku is a mature double volcanic arc with a back-arc marginal sea basin located on a convergent plate boundary of the subducting Pacific plate and the North American plate (Figure 1). The location and orientation of the volcanic front (grey line in Figure 2) has been linked to the opening of the Sea of Japan and subduction angle of the Pacific plate [e.g., 26, 33]. From 60 Ma up until about 10 Ma the volcanic front migrated east and west several times, however, it has been relatively static during the last 8 Ma [26].

Presently there are 15 known historically active volcanoes in the Tohoku region and a total 170 volcanoes that formed during the Quaternary [30]. Volcanism has gradually become more clustered and localized over a period from 14 Ma to present [34], thus volcano clustering is a characteristic feature in Tohoku.

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 121

**Figure 1.** Map showing the tectonic setting of Japan (Figure modified from [1]). The four main islands that make up Japan are located on or near the boundaries of four plates. Black triangles denote Quaternary volcanoes and the red lines depict the main volcanic fronts. The thin contour lines denote the depth of the subducting Pacific Plate beneath Japan. The velocities and arrows indicate the subduction rates and directions respectively of the Pacific, Philippine Sea and Eurasian plates relative to the North American plate.

### **3. Defining the volcanic event**

needed. This has motivated several investigators to incorporate datasets in addition to the distribution and timing of past volcanic activity in volcanic probabilistic analyses [e.g. 15]. Bayesian inference has been used to combine geophysical datasets to probability distributions constructed from known historic volcano locations in order to estimate the location of future volcanism over a regional scale [1]. More recently, [16] used Bayesian inference to merge prior information and past data to construct a probability map of vent opening at the Campi Flegrei

Here we revisit the Bayesian approach developed by [1] where seismic tomographs and geo‐ thermal gradients were incorporated into probabilistic assessments by Bayesian inference in Tohoku. We apply the same Bayesian technique in the same study area to incorporate recently acquired helium isotopes into probabilistic hazard assessments; such noble gases have been shown to be excellent natural tracers for mantle-crust interaction owing to their inert chemical properties which means they are not altered by complex chemical processes. Moreover heli‐ um isotopes provide evidence for the presence of mantle derived materials in the crust, owing to the distinct isotopic compositions between the crust and the upper mantle [e.g. 17, 18]. We

He/4

[1] in the context of recent approaches to incorporate multiple datasets [e.g. 21, 22].

ma generation and hence volcano formation. Such links between magmatism and elevated

Japan is one of the most tectonically active regions in the world. Due to the dynamics of four plates, Quaternary volcanoes have formed along distinct volcanic fronts in east and west Japan

The Tohoku region (Figure 2) is arguably one of the most extensively studied volcanic arcs in the world, particularly regarding the relationship between volcanism and tectonics. Moreover there have been numerous geological and geophysical investigations yielding high-quality

Tohoku is a mature double volcanic arc with a back-arc marginal sea basin located on a convergent plate boundary of the subducting Pacific plate and the North American plate (Figure 1). The location and orientation of the volcanic front (grey line in Figure 2) has been linked to the opening of the Sea of Japan and subduction angle of the Pacific plate [e.g., 26, 33]. From 60 Ma up until about 10 Ma the volcanic front migrated east and west several times,

Presently there are 15 known historically active volcanoes in the Tohoku region and a total 170 volcanoes that formed during the Quaternary [30]. Volcanism has gradually become more clustered and localized over a period from 14 Ma to present [34], thus volcano clustering is a

however, it has been relatively static during the last 8 Ma [26].

He ratios have been proposed [e.g. 19, 20], but the link has not been examined quantita‐ tively in probabilistic based models. Finally we discuss the Bayesian method in developed by

He ratios that may infer possible regions of mag‐

caldera in Italy.

3 He/4

(Figure 1).

datasets e.g., [23 - 29].

characteristic feature in Tohoku.

examine the link between volcanism and 3

120 Updates in Volcanology - New Advances in Understanding Volcanic Systems

**2. Japan and the Tohoku region**

What do we mean here by 'volcano'? In developing probabilistic based models, one of the most difficult and challenging tasks is defining the 'volcanic event'. This is because the volcanic event defined has to be simple and consistent enough for the probabilistic based models to handle. To a certain extent the degree of consistency that can be realistically included in a model is largely constrained by the size of the study area and by the amount and quality of geological, geochemical and/or geophysical data available. The volcanic event could range from a single eruption to a series of eruptions. It could be defined as the existence of a relatively young cinder cone, spatter mound, maar, tuff ring, tuff cone, pyroclastic fall, lava flow or even a large composite volcano. On the other hand older edifices may have been eroded and/or covered by sedimentary deposits such as alluvium and thus be more difficult to locate and/or

Several aligned edifices with the same eruption age may also be considered as a single volcanic event. Such vent alignments typically developed simultaneously as a result of magma supply from a single dike. For example the vent alignments in the Higashi-Izu monogenetic volcanic group [e.g. 35], could well be classified as a single volcanic event temporally but spatially are multiple. Where age data has been limited, some authors have implemented a condition whereby a cone or cones can only be defined as a volcanic event if they are associated with a

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 123

Many of the advances made on modelling future spatial or spatio-temporal patterns of volcanism where carried out in monogenetic volcano fields due to the apparent relative ease of defining such volcanoes as point processes [e.g., 37, 38]. However as composite or estab‐ lished polygenetic volcanoes represent multiple eruptions from the same conduit occurring over several tens to hundreds of thousands of years, defining the volcanic event is not so easy if the focus is on single eruption episodes as the type of eruption can evolve significantly during the lifetime of the composite volcano. In fact the temporal definition of a monogenetic volcano appears to be not so straightforward either as this can range from several days to a few weeks or longer. For example the Ukinrek maar in Alaska formed in about eight days [39] and the 1913 eruption forming the Ambrym Volcano, Vanuatu in the south west pacific in just a few days [40]. Moreover, [41] argued that monogenetic volcanoes can be both spatially and temporarily more complex than a single eruptive event. In other words so called 'monogenetic' volcanoes can also be 'polygenetic' albeit smaller scaled than large volume complex strato or caldera volcanoes. Based on this there could be a case to look again at the volcanic event definition used in earlier probabilistic assessments carried out in monogenetic volcanic fields

In Tohoku, new volcanoes forming at new locations typically evolve into large complex strato and/or caldera volcanoes containing multiple vents e.g. Akitakomagatake volcano [44]. Such large polygenetic volcanoes in Tohoku have been sub-grouped into unstable types where the eruptive centre has migrated more than 1.5 km within 10 ka and stable types were the vents

The volcanic event definition requires information on both the temporal and spatial aspects; the temporal definition relates to the recurrence rate, *λt*(number of volcanic events per unit time), and spatial definition to the intensity or spatial recurrence rate *λx*,*y* (number of volcanic events per unit area). *λt*and *λx*,*y* can also be combined as a spatio-temporal recurrence rate

The temporal definition of a volcanic event could range from a single eruption occurring in one day or less, to an eruption cycle in which active periods of eruptions occur between dormant periods. The time scale of an active period may vary from several years to thousands of years. In previous volcanic hazard analyses carried out on complex, large-volume strato and/or caldera volcanoes, volcanologists have typically defined volcanic events as single eruptions or several eruptions within some defined time period separated by periods in which there is no activity e.g. [4]. This is because the focus at such established volcanoes is not on the

are more concentrated around the geographic centre of the volcano [44].

*λx*,*y*,*t* (number of volcanic events per unit area per unit time) [42].

single linear dike or a dike system with more complex geometry [e.g. 36].

[e.g. 42, 43].

**Figure 2.** The volcanic arc in Tohoku consists of approximately 170 Quaternary edifices [28, 30] (modified from [1]). The highest density of volcanoes in Tohoku is the cluster in the Sengan region. Other notable volcanoes are the Towa‐ da volcano which has been the site of late Quaternary large-volume felsic eruptions resulting in large caldera forma‐ tion [e.g., 27, 31], and the Iwaki and Chokai [32] volcanoes which are active andesitic volcanoes on the back-arc side of Tohoku. The grey line denotes the present day volcanic front.

are easily overlooked. Results of magnetic and gravity data have been used as evidence for locating such hidden volcanic events which in turn had an impact on resulting probabilities at given locations [e.g. 15].

If we were carrying out a hazard assessment on a single volcano we may be interested in defining the event as a series of pyroclastic flows or surges or eruptions that generate lava flows that exceed a certain volume [e.g. 9]. This is particular relevant to volcanic hazard assessments carried out at volcanoes near densely urbanized areas such as the Campi Flegrei caldera in southern Italy [16].

Several aligned edifices with the same eruption age may also be considered as a single volcanic event. Such vent alignments typically developed simultaneously as a result of magma supply from a single dike. For example the vent alignments in the Higashi-Izu monogenetic volcanic group [e.g. 35], could well be classified as a single volcanic event temporally but spatially are multiple. Where age data has been limited, some authors have implemented a condition whereby a cone or cones can only be defined as a volcanic event if they are associated with a single linear dike or a dike system with more complex geometry [e.g. 36].

Many of the advances made on modelling future spatial or spatio-temporal patterns of volcanism where carried out in monogenetic volcano fields due to the apparent relative ease of defining such volcanoes as point processes [e.g., 37, 38]. However as composite or estab‐ lished polygenetic volcanoes represent multiple eruptions from the same conduit occurring over several tens to hundreds of thousands of years, defining the volcanic event is not so easy if the focus is on single eruption episodes as the type of eruption can evolve significantly during the lifetime of the composite volcano. In fact the temporal definition of a monogenetic volcano appears to be not so straightforward either as this can range from several days to a few weeks or longer. For example the Ukinrek maar in Alaska formed in about eight days [39] and the 1913 eruption forming the Ambrym Volcano, Vanuatu in the south west pacific in just a few days [40]. Moreover, [41] argued that monogenetic volcanoes can be both spatially and temporarily more complex than a single eruptive event. In other words so called 'monogenetic' volcanoes can also be 'polygenetic' albeit smaller scaled than large volume complex strato or caldera volcanoes. Based on this there could be a case to look again at the volcanic event definition used in earlier probabilistic assessments carried out in monogenetic volcanic fields [e.g. 42, 43].

In Tohoku, new volcanoes forming at new locations typically evolve into large complex strato and/or caldera volcanoes containing multiple vents e.g. Akitakomagatake volcano [44]. Such large polygenetic volcanoes in Tohoku have been sub-grouped into unstable types where the eruptive centre has migrated more than 1.5 km within 10 ka and stable types were the vents are more concentrated around the geographic centre of the volcano [44].

The volcanic event definition requires information on both the temporal and spatial aspects; the temporal definition relates to the recurrence rate, *λt*(number of volcanic events per unit time), and spatial definition to the intensity or spatial recurrence rate *λx*,*y* (number of volcanic events per unit area). *λt*and *λx*,*y* can also be combined as a spatio-temporal recurrence rate *λx*,*y*,*t* (number of volcanic events per unit area per unit time) [42].

are easily overlooked. Results of magnetic and gravity data have been used as evidence for locating such hidden volcanic events which in turn had an impact on resulting probabilities

**Figure 2.** The volcanic arc in Tohoku consists of approximately 170 Quaternary edifices [28, 30] (modified from [1]). The highest density of volcanoes in Tohoku is the cluster in the Sengan region. Other notable volcanoes are the Towa‐ da volcano which has been the site of late Quaternary large-volume felsic eruptions resulting in large caldera forma‐ tion [e.g., 27, 31], and the Iwaki and Chokai [32] volcanoes which are active andesitic volcanoes on the back-arc side of

If we were carrying out a hazard assessment on a single volcano we may be interested in defining the event as a series of pyroclastic flows or surges or eruptions that generate lava flows that exceed a certain volume [e.g. 9]. This is particular relevant to volcanic hazard assessments carried out at volcanoes near densely urbanized areas such as the Campi Flegrei

at given locations [e.g. 15].

Tohoku. The grey line denotes the present day volcanic front.

122 Updates in Volcanology - New Advances in Understanding Volcanic Systems

caldera in southern Italy [16].

The temporal definition of a volcanic event could range from a single eruption occurring in one day or less, to an eruption cycle in which active periods of eruptions occur between dormant periods. The time scale of an active period may vary from several years to thousands of years. In previous volcanic hazard analyses carried out on complex, large-volume strato and/or caldera volcanoes, volcanologists have typically defined volcanic events as single eruptions or several eruptions within some defined time period separated by periods in which there is no activity e.g. [4]. This is because the focus at such established volcanoes is not on the probability of a new volcano forming in the vicinity of the existing volcano but rather on the probability of the next eruption or eruption phase.

**Volcano Complex Volcanic event**

Yasemori/Magarisaki-

Yasemori/Magarisaki-

yama

yama

**Location Age (Ma) Dating**

Towada Towada Hachinohe 40.468 140.888 0.013 14C 26.9 Towada Post-caldera cones 40.457 140.913 0.013 - 0 Strat. 14.4 Nanashigure Nanashigure 40.068 141.112 1.06 - 0.72 K-Ar 55.5 Moriyoshi Moriyoshi 39.973 140.547 1.07 - 0.78 K-Ar 18.1 Bunamori Bunamori 39.967 140.717 1.2 K-Ar 0.1 Akita-Yakeyama Akita-Yakeyama 39.963 140.763 0.5 - 0 K-Ar 9.9 Nishimori/Maemori NIshimori/Maemori 39.973 140.962 0.5 - 0.3 K-Ar 2.6 Hachimantai/Chausu Hachimantai 39.953 140.857 1 - 0.7 K-Ar 5.5 Hachimantai/Chausu Chausu-dake 39.948 140.902 0.85 - 0.75 K-Ar 13.7 Hachimantai/Chausu Fukenoyu 39.953 140.857 ca.0.7 Strat. 0.2 Hachimantai/Chausu Gentamri 39.956 140.878 0.2

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Kensomori/Morobidake Kensomori 39.897 140.871 ca.0.8 Strat. 0.8 Kensomori/Morobidake Morobi-dake 39.919 140.862 1 - 0.8 Strat. 2.5 Kensomori/Morobidake 1470m Mt. lava 39.909 140.872 0.1 Kensomori/Morobidake Mokko-dake 39.953 140.857 ca.1.0 Strat. 0.5 Tamagawa Welded Tuff Tamagawa Welded Tuffs R4 39.963 140.763 ca.2.0 K-Ar 83.2 Tamagawa Welded Tuff Tamagawa Welded Tuffs D 39.963 140.763 ca.1.0 K-Ar 32.0 Nakakura/Shimokura Obuka-dake 39.878 140.883 0.8 - 0.7 K-Ar 2.9 Nakakura/Shimokura Shimokura-yama 39.889 140.933 0.4 Nakakura/Shimokura Nakakura-yama 39.888 140.910 0.4 Matsukawa Matsukawa andesite 39.850 140.900 2.6 - 1.29 K-Ar 11.6 Iwate/Amihari Iwate 39.847 141.004 0.2 - 0 K-Ar 25.1 Iwate/Amihari Amihari 39.842 140.958 0.3 - 0.1 K-Ar 10.6 Iwate/Amihari Omatsukura-yama 39.841 140.919 0.7 - 0.6 K-Ar 3.3 Iwate/Amihari Kurikigahara 39.849 140.882 0.2 Iwate/Amihari Mitsuishi-yama 39.848 140.900 0.46 K-Ar 0.6 Shizukuishi/Takakura Marumori 39.775 140.877 0.4 - 0.3 K-Ar 2.4 Shizukuishi/Takakura Shizukuishi-Takakura-yama 39.783 140.893 0.5 - 0.4 Strat. 5.2 Shizukuishi/Takakura Older Kotakakura-yama 39.800 140.900 1.4 K-Ar 2.7 Shizukuishi/Takakura North Mikado-yama 39.800 140.875 0.3 Shizukuishi/Takakura Kotakakura-yama 39.797 140.907 0.6 - 0.5 K-Ar 1.8 Shizukuishi/Takakura Mikado-yama 39.788 140.870 ca.0.3 0.2

Magarisaki-yama 39.878 140.803 1.9 - 1.52 K-Ar 0.3

Yasemori 39.883 140.828 1.8 K-Ar 0.9

**Latitude Longitude ( km3,**

**Method**

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**DRE )**

### **3.1. Tohoku volcanic event definition**

In the context of siting of a geological repository, the main concern is the formation of a new volcano in a region where volcanoes do not already exist. Thus the distinction between monogenetic (simple or complex) and polygenetic (complex strato and/or caldera) volcanism is not relevant for the definition of volcanic event here. Table 1 is a compilation of all Quater‐ nary volcanoes in the Tohoku volcanic arc modified from the Catalog of Quaternary Volcanoes in Japan [1, 30]. Volcano complexes refer to magma systems that have evolved over the longterm (order of 0.1 Ma) which appear as regional scale clusters. In this chapter we use the same definition of volcanic event as [1] taking into account eruption volumes. This is depicted as a white triangle in Figure 3 and is the average geographic location of the vents (white dots). The eruption products released from the vents are represented by the dark grey regions in Figure 3. The lighter grey areas in Figure 3a are the eruption products of a separate volcanic event. Each volcanic event typically has a time gap of more than 10 ka, and/or is differentiated from other volcanic events according to geochemistry.

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probability of a new volcano forming in the vicinity of the existing volcano but rather on the

In the context of siting of a geological repository, the main concern is the formation of a new volcano in a region where volcanoes do not already exist. Thus the distinction between monogenetic (simple or complex) and polygenetic (complex strato and/or caldera) volcanism is not relevant for the definition of volcanic event here. Table 1 is a compilation of all Quater‐ nary volcanoes in the Tohoku volcanic arc modified from the Catalog of Quaternary Volcanoes in Japan [1, 30]. Volcano complexes refer to magma systems that have evolved over the longterm (order of 0.1 Ma) which appear as regional scale clusters. In this chapter we use the same definition of volcanic event as [1] taking into account eruption volumes. This is depicted as a white triangle in Figure 3 and is the average geographic location of the vents (white dots). The eruption products released from the vents are represented by the dark grey regions in Figure 3. The lighter grey areas in Figure 3a are the eruption products of a separate volcanic event. Each volcanic event typically has a time gap of more than 10 ka, and/or is differentiated from

Mutsuhiuchi-dake Older Mutsuhiuchi-dake 41.437 141.057 ca.0.73 K-Ar 5.9 Mutsuhiuchi-dake Younger Mutsuhiuchi-dake 41.437 141.057 0.45 0.2 Strat. 3.6 Osorezan Kamabuse-yama 41.277 141.123 ca.0.8 K-Ar 11.4 Hakkoda Hakkoda P.F.1st. 40.667 140.897 0.65 K-Ar 17.8 Hakkoda South-Hakkoda 40.600 140.850 0.65 - 0.4 K-Ar 52.4 Hakkoda Hakkoda P.F.2nd. 40.667 140.897 0.4 K-Ar 17.3 Hakkoda North-Hakkoda 40.650 140.883 0.16 - 0 K-Ar 30.4 Okiura Aoni F. Aonigawa P.F. 40.573 140.763 ca.1.7 K-Ar 17.6 Okiura Aoni F. Other P.F. 40.573 140.763 1.7 - 0.9 K-Ar 3.7 Okiura Okogawasawa lava 40.579 140.759 0.9 - 0.65 Strat. 0.9 Okiura Okiura dacite 40.557 140.755 0.9 - 0.7 K-Ar 2.1 Ikarigaseki Nijikai Tuff 40.500 140.625 ca.2.0 K-Ar 20.2 Ikarigaseki Ajarayama 40.490 140.600 1.91 - 1.89 K-Ar 2.1 Towada Herai-dake 40.450 141.000 5.1 Towada Ohanabe-yama 40.500 140.883 0.4 - 0.05 K-Ar 8.9 Towada Hakka 40.417 140.867 1.4 Towada Towada Okuse 40.468 140.888 0.055 14C 4.8 Towada Towada Ofudo 40.468 140.888 0.025 14C 22.1

**Location Age (Ma) Dating**

**Latitude Longitude ( km3,**

**Method**

**Eruptive volume**

**DRE )**

probability of the next eruption or eruption phase.

124 Updates in Volcanology - New Advances in Understanding Volcanic Systems

other volcanic events according to geochemistry.

**Volcano Complex Volcanic event**

**3.1. Tohoku volcanic event definition**

**Volcano Complex Volcanic event**

**Location Age (Ma) Dating**

Naruko Nizaka tuff 38.730 140.727 ca.0.073 FT 4.8 Funagata Izumigatake 38.408 140.712 1.45 - 1.14 K-Ar 2.3 Funagata Funagatayama 38.453 140.623 0.85 - 0.56 K-Ar 19.0 Yakuraisan Yakuraisan 38.563 140.717 1.65 - 1.04 K-Ar 0.2 Nanatsumori Nanatsumori lava 38.430 140.835 2.3 - 2 K-Ar 0.5 Nanatsumori Miyatoko Tuffs 38.428 140.793 ca.2.5 Strat. 6.1 Nanatsumori Akakuzure-yama lava 38.433 140.768 1.6 - 1.5 Strat. 1.5 Nanatsumori Kamikadajin lava 38.447 140.772 1.6 - 1.5 K-Ar 0.8 Shirataka Shirataka 38.220 140.177 1 - 0.8 K-Ar 3.8 Adachi Adachi 38.218 140.662 ca.0.08 FT 0.9 Gantosan Gantosan 38.195 140.480 0.4 - 0.3 K-Ar 4.6 Kamuro-dake Kamuro-dake 38.253 140.488 ca.1.67 K-Ar 5.7 Daito-dake Daito-dake 38.316 140.527 5.7 Ryuzan Ryuzan 38.181 140.397 1.1 - 0.9 K-Ar 4.6 Zao Central Zao 1st. 38.133 140.453 1.46 - 0.79 K-Ar 0.8 Zao Central Zao 2nd. 38.133 140.453 0.32 - 0.12 K-Ar 15.2 Zao Central Zao 3rd. 38.133 140.453 0.03 - 0 K-Ar 0.0 Zao Sugigamine 38.103 140.462 1 K-Ar 9.9 Zao Fubosan/byobudake 38.093 140.478 0.31 - 0.17 K-Ar 15.2 Aoso-yama Gairinzan 38.082 140.610 0.7 - 0.4 K-Ar 6.1 Aoso-yama Central Cone 38.082 140.610 0.4 - 0.38 K-Ar 3.0 Azuma Azuma Kitei lava 37.733 140.247 1.3 - 1 K-Ar 24.7 Azuma Higashi Azumasan 37.710 140.233 0.7 - 0 K-Ar 22.8 Azuma Nishi Azumasan 37.730 140.150 0.6 - 0.4 K-Ar 7.2 Azuma Naka Azumasan 37.713 140.188 0.4 - 0.3 K-Ar 4.6 Nishikarasugawa andesite Nishikarasugawa andesite 37.650 140.283 ca.1.5 K-Ar 1.9 Adatara Adatara Stage 1 37.625 140.280 0.55 - 0.44 K-Ar 0.3 Adatara Adatara Stage 2 37.625 140.280 ca.0.35 K-Ar 0.4 Adatara Adatara Stage 3a 37.625 140.280 ca.0.20 K-Ar 2.0 Adatara Adatara Stage 3b 37.625 140.280 0.12 - 0.0024 K-Ar 0.3 Sasamori-yama Sasamari-yama andesite 37.655 140.391 2.5 - 2 K-Ar 0.4 Bandai Pre-Bandai 37.598 140.075 ca.0.7 K-Ar 0.1 Bandai Bandai 37.598 140.075 0.3 - 0 Strat. 14.0 Nekoma Old Nekoma 37.608 140.030 1 - 0.7 K-Ar 11.4 Nekoma New Nekoma 37.608 140.030 0.5 - 0.4 K-Ar 0.9

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**Latitude Longitude ( km3,**

**Method**

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> **Eruptive volume**

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**Volcano Complex Volcanic event**

126 Updates in Volcanology - New Advances in Understanding Volcanic Systems

**Location Age (Ma) Dating**

Shizukuishi/Takakura Tairagakura-yama 39.808 140.878 ca.0.3 0.1 Nyuto/Zarumori Tashirotai 39.812 140.827 0.3 - 0.2 K-Ar 0.6 Nyuto/Zarumori Sasamori-yama 39.770 140.820 0.23 - 0.1 K-Ar 0.4 Nyuto/Zarumori Yunomori-yama 39.772 140.827 ca.0.3 0.5 Nyuto/Zarumori Zarumori-yama 39.788 140.850 0.56 K-Ar 0.9 Nyuto/Zarumori Nyutozan 39.802 140.843 0.58 - 0.5 K-Ar 5.0 Nyuto/Zarumori Nyuto-kita 39.817 140.855 ca.0.4 K-Ar 0.1 Akita-Komagatake Akita-Komagatake 39.754 140.802 0.1 - 0 K-Ar 2.9 Kayo Kayo 39.803 140.735 2.2 - 1.17 K-Ar 5.9 Kayo KoJiromori 39.828 140.787 0.94 K-Ar 0.3 Kayo Akita-Ojiromori 39.839 140.788 1.7 1.7 1.7 Strat. 0.3 Innai/Takahachi Takahachi-yama 39.755 140.655 1.7 1.7 1.7 K-Ar 0.0 Innai/Takahachi Innai 39.692 140.638 2 - 1.6 K-Ar 0.5 Kuzumaru Aonokimori andesites 39.543 140.983 2.06 K-Ar 0.3 Yakeishi Yakeishidake 39.161 140.832 0.7 - 0.6 K-Ar 9.5 Yakeishi Komagatake 39.193 140.924 ca.1.0 K-Ar 7.6 Yakeishi Kyozukayama 39.178 140.892 0.6 - 0.4 K-Ar 5.7 Yakeishi Usagimoriyama 39.239 140.924 0.07 - 0.04 K-Ar 2.3 Kobinai Kobinai 39.018 140.523 1 - 0.57 FT, K-Ar 2.3 Takamatsu/Kabutoyama Kabutoyama Welded Tuff 39.025 140.618 1.16 TL 3.2 Takamatsu/Kabutoyama Kiji-yama Welded Tuffs 39.025 140.618 0.30 K-Ar 5.1 Takamatsu Takamatsu 38.965 140.610 0.3 - 0.27 K-Ar 3.8 Takamatsu Futsutsuki-dake 38.961 140.661 ca.0.3 0.8 Kurikoma Tsurugi-dake 38.963 140.792 0.1 - 0 K-Ar 0.2 Kurikoma Magusa-dake 38.968 140.751 0.32 - 0.1 K-Ar 1.5 Kurikoma Kurikoma 38.963 140.792 0.4 - 0.1 K-Ar 0.9 Kurikoma South volcanoes 38.852 140.875 ca.0.5 K-Ar 0.3 Kurikoma Older Higashi Kurikoma 38.934 140.779 ca.0.5 K-Ar 2.2 Kurikoma Younger Higashi Kurikoma 38.934 140.779 0.4 - 0.1 K-Ar 0.7 Mukaimachi Mukaimachi 38.770 140.520 ca.0.8 K-Ar 12.0 Onikobe Shimoyamasato tuff 38.830 140.695 0.21 0.21 0.21 FT 1.0 Onikobe Onikobe Centeral cones 38.805 140.727 ca.0.2 TL 1.1 Onikobe Ikezuki tuff 38.830 140.695 0.3 - 0.2 FT 17.3 Naruko Naruko Central cones 38.730 140.727 ca.0.045 14C 0.1 Naruko Yanagizawa tuff 38.730 140.727 ca.0.045 FT 4.8

**Latitude Longitude ( km3,**

**Method**

**Eruptive volume**

**DRE )**

**Volcano Complex Volcanic event**

density of the respective volcanic deposits.

(Figure modified from [1]).

Tazawa Tazawa (lake) 39.723 140.667

**Location Age (Ma) Dating**

Hijiori Hijiori Pyroclastic flow 38.610 140.159 ca.0.01 Strat. 1.0 Hijiori Komatsubuchi lava dome 38.613 140.171 ca.0.01 Strat. 0.0

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Daibutsu Daibutsu 39.817 140.517 2.340 - 2.160 K-Ar 3.2 Kampu Kampu 39.928 139.877 0.030 - 0.000 Strat. 0.6 Toga Toga 39.950 139.718 ca.0.42 FT/K-Ar 2.0 Megata Megata 39.952 139.742 0.030 - 0.020 Strat. 0.1 Inaniwa Inaniwa 40.195 141.050 7.000 - 2.700 K-Ar 14.0 Taira-Komagatake Taira-Komagatake 40.410 140.254 0.200 - 0.170 Strat. 3.0 Tashiro Tashiro 40.425 140.413 0.600 - 0.470 K-Ar 9.0 Tashiro Hirataki nueeardente deps. 40.420 140.413 0.020 - 0.020 Strat. 0.7 Iwaki Iwaki 40.653 140.307 0.330 - 0.000 K-Ar 49.0

**Table 1.** Tohoku volcanic arc [30, 28]. Dense-rock equivalent (DRE) of eruptive volumes is the product of volume and

**Figure 3.** Two volcano types in Tohoku classified according to migration distance from eruption centre [44]: (a) an unstable type with vent (white dots) migration exceeding 1.5 km in 10 ka resulting in a summit with multiple peaks; and (b) a stable type commonly with a narrow saw-tooth or pointed appearance. For consistency in volcanic event definition over the Tohoku region, both types are treated as a single volcanic event (white triangle) in the probability analysis. However they are also optionally weighted with the corresponding eruption products (dark grey regions).

**Latitude Longitude ( km3,**

**Method**

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> **Eruptive volume**

129

**DRE )**

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**Volcano Complex Volcanic event**

128 Updates in Volcanology - New Advances in Understanding Volcanic Systems

Gassan Yudonosan lavas/

pyroclastics

Numazawa Sozan lava domes 37.452 139.577

Numazawa Mukuresawa lava 37.452 139.577 -

**Location Age (Ma) Dating**

38.534 139.988 0.800 - 0.700 K-Ar 7.5

Kasshi/Oshiromori Kasshi 37.184 139.973 0.1 Kasshi/Oshiromori Oshiromori 37.199 139.970 0.7 Kasshi/Oshiromori Matami-yama 37.292 139.886 0.3 Kasshi/Oshiromori Naka-yama 37.282 139.899 0.0 Shirakawa Kumado P.F. 37.242 140.032 1.31 K-Ar 19.2 Shirakawa Tokaichi A.F. tuffs 37.242 140.032 1.31 - 1.24 Strat. 12.0 Shirakawa Ashino P.F. 37.242 140.032 1.2 FT 19.2 Shirakawa Nn3 P.F. 37.242 140.032 1.2 - 1.17 Strat. 0.0 Shirakawa Kinshoji A.F. tuffs 37.242 140.032 1.2 - 1.18 Strat. 9.0 Shirakawa Nishigo P.F. 37.252 139.869 1.11 FT 28.8 Shirakawa Tenei P.F. 37.242 140.032 1.06 Strat. 7.7 Nasu Futamata-yama 37.244 139.971 0.14 K-Ar 3.2 Nasu Kasshiasahi-dake 37.177 139.963 0.6 - 0.4 K-Ar 12.3 Nasu Sanbonyari-dake 37.147 139.965 0.4 - 0.25 K-Ar 5.5 Nasu Minamigassan 37.123 139.967 0.2 - 0.05 K-Ar 8.7 Nasu Asahi-dake 37.134 139.971 0.2 - 0.05 K-Ar 4.6 Nasu Chausu-dake 37.122 139.966 0.04 - 0 K-Ar 0.3 Chokai Shinsan Lava flow 39.097 140.053 0.02 - 0 Strat. Chokai Higashi Chokai 39.097 140.053 0.02 - 0.02 K-Ar 4.3 Chokai Nishi Chokai 39.097 140.020 0.09 - 0.02 Stra. 0.8 Chokai Nishi ChokaiII 39.097 140.020 0.13 - 0.01 K-Ar 21.0 Chokai Old Chokai 39.103 140.030 0.16 - 0.55 K-Ar 67.0 Chokai Uguisugawa Basalt 39.103 140.030 0.55 - 0.6 K-Ar 1.0 Chokai Tengumari volcanics 39.103 140.031 0.55 - 0.6 K-Ar 11.0 Gassan Ubagatake 38.533 140.005 0.400 - 0.300 K-Ar 3.5

Gassan Gassan 38.550 140.020 0.500 - 0.400 K-Ar 18.0

Numazawa Mizunuma pyroclastic dep. 37.452 139.577 ca.0.05 FT 2.0 Numazawa Numazawa pyroclastics 37.452 139.577 0.005 - 0.005 14C 2.5

Ryuzan Ryuzan 38.181 140.397 1.130 - 0.940 K-Ar 6.0 Sankichi-Hayama Sankichi-Hayama 38.137 140.315 2.400 - 2.300 K-Ar 2.9 Daitodake Daitodake 38.316 140.527 ca.1 Strat. 7.5

**Latitude Longitude ( km3,**

**Method**

**Eruptive volume**

**DRE )**

**Table 1.** Tohoku volcanic arc [30, 28]. Dense-rock equivalent (DRE) of eruptive volumes is the product of volume and density of the respective volcanic deposits.

**Figure 3.** Two volcano types in Tohoku classified according to migration distance from eruption centre [44]: (a) an unstable type with vent (white dots) migration exceeding 1.5 km in 10 ka resulting in a summit with multiple peaks; and (b) a stable type commonly with a narrow saw-tooth or pointed appearance. For consistency in volcanic event definition over the Tohoku region, both types are treated as a single volcanic event (white triangle) in the probability analysis. However they are also optionally weighted with the corresponding eruption products (dark grey regions). (Figure modified from [1]).

### **4. Bayesian model**

The following is a slightly shorter description of the Bayesian methodology published in [1]. A two-dimensional surface distribution is set-up showing the continuous probability of one or more volcanic event(s) forming within a region of interest, in an arbitrarily time frame of the order of 0.1 – 1 Ma. The volcanic event definition defined above means that we are estimating with known uncertainty, the probability of a new volcano forming at a given location (x, y). [1] noted that a challenge with estimating the long-term future spatial distri‐ bution of volcanism is the fact that we are trying to model something that we cannot sample directly; namely the locations of future volcanoes. In this chapter we incorporate 3 He/4 He ratios, as these may be indicative of conduits in the earth's crust through which magma may rise through resulting in future volcano formation [19, 20].

where *x* and *y* represent grid point locations within the volcanic field *A*, *θ*is additional dataset, *P*(*x*, *y*)is the *a priori* PDF, *L* (*θ* | *x*, *y*)the likelihood function generated by conditioning additional data on the locations of volcanic events, and *P*(*x*, *y* |*θ*) the resulting *a posteriori* PDF [1]. The *a posteriori* PDF is normalized to unity by integrating over the entire Tohoku volcanic field; hence total cumulative probability will not change but the shape of the 2-D surface

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Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 131

We assume that past and present volcanic events can be used to estimate future locations of volcanoes over the long-term, as well as constraining upper bound recurrence rates in the volcanic field. The spatial distribution of volcanoes in volcanic arcs like Tohoku are random [48] hence by treating volcanism in Tohoku volcanic arc as a low frequency, random event, it is assumed that the underlying process could be approximated to a Poisson process [1]. Moreover, by treating the location of volcanic events as random points within some set, the spatial distribution of volcanism can be modeled as a spatial point process [1] where a spatial point process is a stochastic model that can be described as the process controlling the spatial locations of the events*s*1,…,*s*1 in some arbitrary set *S* [49]. In applying point process models to volcanism, [42] eloquently defined*s*1,…,*sn* as volcanic events and *S* as the volcanic field.

The Poisson process is 'homogeneous' if the spatial distribution of point events are completely random [49]. However, as with many volcanic fields, spatial patterns of volcanism in the Tohoku volcanic arc are clustered [34, 50], hence the distribution of volcanoes are not com‐ pletely random and therefore non-homogeneous (also referred to as in-homogenous). Apply‐ ing the Clark-Evans nearest-neighbour test [51], [1] showed that the distribution of the volcanic events defined above is clustered with greater than 95% confidence. A non-homogeneous Poisson process is the simplest alternative for modeling such clustered events. Moreover, point process models based on non-homogeneous Poisson processes have been extensively used in modeling the spatial and spatio-temporal characteristics of several volcano fields (e.g. the Springerville volcanic fields in Arizona [38] and the Higashi-Izu monogenetic volcano group, Shizuoka Prefecture, Japan [43]. In these models the local spatial density of volcanic events *λx*,*y* is calculated using a kernel function [37, 52]. The kernel function itself is a density function used to obtain the intensity of volcanic events at a sampling point*xp*, *yp*, calculated as a function

As noted by [42] the choice of kernel function with appropriate values of *h* has some conse‐ quence for the parameter estimation because it controls how *λx*,*y* varies with distance from existing volcanoes. The Gaussian kernel has been used a lot in probabilistic assessments car‐ ried out in monogenetic volcanic fields, [e.g. 15, 43] since it was assumed that the next volca‐ no to form would not be far from an existing volcanoes. In order to include extreme volcanic events further afield however, [1] modelled spatial patterns using the Cauchy kernel which has thicker tails than the Gaussian kernel. [1] also showed that the spatial distribution of volcanic events in the Tohoku volcanic arc fit a Cauchy distribution whereas monogenetic fields such as the Higashi-Izu Monogenetic Volcano Group [43] tend to be Gaussian. We

of the distance to nearby volcanoes and a smoothing constant *h* (Figure 4).

distribution will be modified according to the likelihood function.

**4.2. A priori PDF**

Information, no matter how obtained, can be described by a probability density function (PDF) [e.g. 45, 46]. Once the dataset is expressed as a PDF, it is possible to combine with our initial PDF created based on *a priori* assumptions on volcanism. Bayesian inference is a powerful tool that allows us to construct an a *posteriori* PDF given *a priori* assumptions and the PDF generated by our new dataset.

Essentially, two stages are performed yielding the *a posteriori* PDF. The first is to make a longterm future prediction based solely on the distribution and ages of past volcanic events, creating an *a priori* PDF. The *a priori* assumption is that the past and the present provide information about the future; in other words the locations of past and present volcanism are used as an initial guide to estimating future long-term spatial patterns of volcanism. The basic logic behind the *a priori* assumption is that a new volcano doesn't form far from existing volcanoes. The *a priori* assumption can be quite vague in the first step as it is simply the starting point. The next stage is to update or modify the *a priori* assumptions by incorporating infor‐ mation that is likely to be indicative of the locations of future volcanism and/or we have increased our understanding of the process that controls the location of volcanism. This new information and/or knowledge, obtained from chemical and/or geophysical data, is used to modify the *a priori* PDF to form an *a posteriori* PDF that is expected to better reflect the location of future volcanism. The cycle can be repeated any number of times for other datasets by treating the *a posteriori* PDF as the new *a priori* PDF in the first step above.

### **4.1. Bayesian inference and Bayes' theorem**

Bayes' theorem [e.g. 47] is used to setup a model providing a joint probability distribution for the location known volcanic events (*a priori* PDF) and current R/RA contoured datasets recast as a PDF (likelihood function). The joint probability density function or *a posteriori* PDF can be written as the product of two PDFs; the *a priori* PDF and the sampling or likelihood PDF

$$\mathbf{P}(\mathbf{x}, y \mid \boldsymbol{\theta}) = \frac{\mathbf{P}(\mathbf{x}, y)\mathbf{L}(\boldsymbol{\theta} \mid \mathbf{x}, y)}{\int\_{A} \mathbf{P}(\mathbf{x}, y)\mathbf{L}(\boldsymbol{\theta} \mid \mathbf{x}, y) dA} \tag{1}$$

where *x* and *y* represent grid point locations within the volcanic field *A*, *θ*is additional dataset, *P*(*x*, *y*)is the *a priori* PDF, *L* (*θ* | *x*, *y*)the likelihood function generated by conditioning additional data on the locations of volcanic events, and *P*(*x*, *y* |*θ*) the resulting *a posteriori* PDF [1]. The *a posteriori* PDF is normalized to unity by integrating over the entire Tohoku volcanic field; hence total cumulative probability will not change but the shape of the 2-D surface distribution will be modified according to the likelihood function.

### **4.2. A priori PDF**

He/4 He

**4. Bayesian model**

by our new dataset.

The following is a slightly shorter description of the Bayesian methodology published in [1]. A two-dimensional surface distribution is set-up showing the continuous probability of one or more volcanic event(s) forming within a region of interest, in an arbitrarily time frame of the order of 0.1 – 1 Ma. The volcanic event definition defined above means that we are estimating with known uncertainty, the probability of a new volcano forming at a given location (x, y). [1] noted that a challenge with estimating the long-term future spatial distri‐ bution of volcanism is the fact that we are trying to model something that we cannot sample

directly; namely the locations of future volcanoes. In this chapter we incorporate 3

rise through resulting in future volcano formation [19, 20].

130 Updates in Volcanology - New Advances in Understanding Volcanic Systems

ratios, as these may be indicative of conduits in the earth's crust through which magma may

Information, no matter how obtained, can be described by a probability density function (PDF) [e.g. 45, 46]. Once the dataset is expressed as a PDF, it is possible to combine with our initial PDF created based on *a priori* assumptions on volcanism. Bayesian inference is a powerful tool that allows us to construct an a *posteriori* PDF given *a priori* assumptions and the PDF generated

Essentially, two stages are performed yielding the *a posteriori* PDF. The first is to make a longterm future prediction based solely on the distribution and ages of past volcanic events, creating an *a priori* PDF. The *a priori* assumption is that the past and the present provide information about the future; in other words the locations of past and present volcanism are used as an initial guide to estimating future long-term spatial patterns of volcanism. The basic logic behind the *a priori* assumption is that a new volcano doesn't form far from existing volcanoes. The *a priori* assumption can be quite vague in the first step as it is simply the starting point. The next stage is to update or modify the *a priori* assumptions by incorporating infor‐ mation that is likely to be indicative of the locations of future volcanism and/or we have increased our understanding of the process that controls the location of volcanism. This new information and/or knowledge, obtained from chemical and/or geophysical data, is used to modify the *a priori* PDF to form an *a posteriori* PDF that is expected to better reflect the location of future volcanism. The cycle can be repeated any number of times for other datasets by

Bayes' theorem [e.g. 47] is used to setup a model providing a joint probability distribution for the location known volcanic events (*a priori* PDF) and current R/RA contoured datasets recast as a PDF (likelihood function). The joint probability density function or *a posteriori* PDF can be written as the product of two PDFs; the *a priori* PDF and the sampling or likelihood PDF

> q

q

**P L** (1)

(,)(|,) (,|) (,)(|,) *A xy xy x y x y x y dA*

 = ò **P L P**

q

treating the *a posteriori* PDF as the new *a priori* PDF in the first step above.

**4.1. Bayesian inference and Bayes' theorem**

We assume that past and present volcanic events can be used to estimate future locations of volcanoes over the long-term, as well as constraining upper bound recurrence rates in the volcanic field. The spatial distribution of volcanoes in volcanic arcs like Tohoku are random [48] hence by treating volcanism in Tohoku volcanic arc as a low frequency, random event, it is assumed that the underlying process could be approximated to a Poisson process [1]. Moreover, by treating the location of volcanic events as random points within some set, the spatial distribution of volcanism can be modeled as a spatial point process [1] where a spatial point process is a stochastic model that can be described as the process controlling the spatial locations of the events*s*1,…,*s*1 in some arbitrary set *S* [49]. In applying point process models to volcanism, [42] eloquently defined*s*1,…,*sn* as volcanic events and *S* as the volcanic field.

The Poisson process is 'homogeneous' if the spatial distribution of point events are completely random [49]. However, as with many volcanic fields, spatial patterns of volcanism in the Tohoku volcanic arc are clustered [34, 50], hence the distribution of volcanoes are not com‐ pletely random and therefore non-homogeneous (also referred to as in-homogenous). Apply‐ ing the Clark-Evans nearest-neighbour test [51], [1] showed that the distribution of the volcanic events defined above is clustered with greater than 95% confidence. A non-homogeneous Poisson process is the simplest alternative for modeling such clustered events. Moreover, point process models based on non-homogeneous Poisson processes have been extensively used in modeling the spatial and spatio-temporal characteristics of several volcano fields (e.g. the Springerville volcanic fields in Arizona [38] and the Higashi-Izu monogenetic volcano group, Shizuoka Prefecture, Japan [43]. In these models the local spatial density of volcanic events *λx*,*y* is calculated using a kernel function [37, 52]. The kernel function itself is a density function used to obtain the intensity of volcanic events at a sampling point*xp*, *yp*, calculated as a function of the distance to nearby volcanoes and a smoothing constant *h* (Figure 4).

As noted by [42] the choice of kernel function with appropriate values of *h* has some conse‐ quence for the parameter estimation because it controls how *λx*,*y* varies with distance from existing volcanoes. The Gaussian kernel has been used a lot in probabilistic assessments car‐ ried out in monogenetic volcanic fields, [e.g. 15, 43] since it was assumed that the next volca‐ no to form would not be far from an existing volcanoes. In order to include extreme volcanic events further afield however, [1] modelled spatial patterns using the Cauchy kernel which has thicker tails than the Gaussian kernel. [1] also showed that the spatial distribution of volcanic events in the Tohoku volcanic arc fit a Cauchy distribution whereas monogenetic fields such as the Higashi-Izu Monogenetic Volcano Group [43] tend to be Gaussian. We therefore also use a two-dimensional Cauchy kernel here to calculate the spatial recurrence rate *λx*,*y* at grid point *xp*, *yp*where:

functions with varying values of smoothing are compared with the fraction of volcanic vents

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 133

**Figure 5.** Suitable values of smoothing coefficient *h* are estimated by plotting cumulative distances to nearest neigh‐ bour volcanic events and cumulative probability distribution with differing values of smoothing coefficient. From this plot, suitable values of smoothing coefficient for known volcanic events in Tohoku volcanic are estimated to be 1-1.5 km for the Cauchy kernel. (Figure modified from [1]). As a comparison, the monogenetic volcanoes in the Yucca

The cumulative plots in Figure 5 suggest that the spatial distribution of volcanic events in the Tohoku volcanic arc fit a Cauchy distribution with smoothing coefficients of *h* = 1–1.5 km.

Probability estimates for each grid point *xp*, *yp* are computed by using a Poisson distribution

l l

*t xy* ) (3)

Mountain Region (YMR) and the Higashi-Izu-Oki monogenetic volcano group are also plotted.

where *λx*,*y* represents the intensity parameter computed using equation (2) :

*P Nt x y*, , { } ( ) 1 1 exp ³ = - - DD ( *t xy*

**4.4. A priori probabilities**

and nearest-neighbour volcanic event distances in Tohoku (Figure 5).

**Figure 4.** Local volcano intensity λ*x*,*y* at each grid point (*x*, *y*) is computed using a Cauchy kernel function. λ*x*,*y*is a func‐ tion of volcano distance from grid point (*x*, *y*) for *N* = 6 volcanoes (modified from [1]).

$$\mathcal{A}\_{x,y} = \frac{1}{\pi h^2 N} \sum\_{i=1}^{N} \left\{ \frac{l\_{vi}}{1 + \left[ \left( \frac{\mathbf{x}\_p - \mathbf{x}\_{vi}}{h} \right)^2 + \left( \frac{\mathbf{y}\_p - \mathbf{y}\_{vi}}{h} \right)^2 \right]} \right\} \tag{2}$$

*xvi* , *yvi* are Cartesian coordinates of the *i*th volcanic event, *N* the number of volcanic events used in the calculation and *l vi* is a factor for weighting eruption volume of the correspond‐ ing *i*th volcanic event. *l vi* is set to unity when eruption volume is excluded. The calculation is repeated on a 10 km mesh in the study area 139 to 143 longitude and 37 to 41.6 latitude and the resulting PDF is normalized to unity. The 10 km grid spacing was selected taking into account the resolution of available geophysical or geochemical datasets across the entire To‐ hoku volcanic arc.

#### **4.3. Estimating an optimum smoothing coefficient h for the volcanoes in Tohoku**

The choice of the smoothing coefficient depends on a combination of the size of the volcanic field, size and degree of clustering and the amount of robustness and conservatism required at specific points within or nearby the volcanic fields in question. In order to estimate the most likely optimum value of smoothing coefficient, [1] plotted cumulative probability density functions with varying values of smoothing are compared with the fraction of volcanic vents and nearest-neighbour volcanic event distances in Tohoku (Figure 5).

**Figure 5.** Suitable values of smoothing coefficient *h* are estimated by plotting cumulative distances to nearest neigh‐ bour volcanic events and cumulative probability distribution with differing values of smoothing coefficient. From this plot, suitable values of smoothing coefficient for known volcanic events in Tohoku volcanic are estimated to be 1-1.5 km for the Cauchy kernel. (Figure modified from [1]). As a comparison, the monogenetic volcanoes in the Yucca Mountain Region (YMR) and the Higashi-Izu-Oki monogenetic volcano group are also plotted.

The cumulative plots in Figure 5 suggest that the spatial distribution of volcanic events in the Tohoku volcanic arc fit a Cauchy distribution with smoothing coefficients of *h* = 1–1.5 km.

### **4.4. A priori probabilities**

therefore also use a two-dimensional Cauchy kernel here to calculate the spatial recurrence

**Figure 4.** Local volcano intensity λ*x*,*y* at each grid point (*x*, *y*) is computed using a Cauchy kernel function. λ*x*,*y*is a func‐

*vi*

å (2)

is a factor for weighting eruption volume of the correspond‐

is set to unity when eruption volume is excluded. The calculation is

ì ü ï ï

*h h*

è øè ø î þ ë û

are Cartesian coordinates of the *i*th volcanic event, *N* the number of volcanic events

*l*

, 2 2 2

*h N xx yy*

= í ý é ù ï ï æ öæ ö - - ê ú + + ç ÷ç ÷

*i p vi p vi*

repeated on a 10 km mesh in the study area 139 to 143 longitude and 37 to 41.6 latitude and the resulting PDF is normalized to unity. The 10 km grid spacing was selected taking into account the resolution of available geophysical or geochemical datasets across the entire To‐

The choice of the smoothing coefficient depends on a combination of the size of the volcanic field, size and degree of clustering and the amount of robustness and conservatism required at specific points within or nearby the volcanic fields in question. In order to estimate the most likely optimum value of smoothing coefficient, [1] plotted cumulative probability density

**4.3. Estimating an optimum smoothing coefficient h for the volcanoes in Tohoku**

tion of volcano distance from grid point (*x*, *y*) for *N* = 6 volcanoes (modified from [1]).

1

*N*

1

1

*vi*

*vi*

p=

*x y*

l

used in the calculation and *l*

ing *i*th volcanic event. *l*

hoku volcanic arc.

*xvi* , *yvi*

rate *λx*,*y* at grid point *xp*, *yp*where:

132 Updates in Volcanology - New Advances in Understanding Volcanic Systems

Probability estimates for each grid point *xp*, *yp* are computed by using a Poisson distribution where *λx*,*y* represents the intensity parameter computed using equation (2) :

$$P\_{x,y}\left(\mathcal{N}(t)\geq 1\right) = 1 - \exp\left(-t\mathcal{N}\_{\mathbf{r}}\mathcal{N}\_{x,y}\Delta\mathbf{x}\Delta y\right) \tag{3}$$

where, *N(t)* represents the number of future volcanic vents that occur within time *t* and area *ΔxΔy* (10 km x 10 km). The parameter *λx*,*y* is normalized to unity across the Tohoku, so, equa‐ tion (3) represents the probability of one or more volcanic event(s) forming in an area *ΔxΔy*cen‐ tred on point *xp*, *yp* given the formation of a new volcanic event in Tohoku. This calculation is repeated on a grid throughout Tohoku. The resolution is such that the spatial recurrence rate *λx*,*y*does not vary within each cell. For the regional recurrence rate*λt* an average of 120 volcan‐ ic events per million years is used, effectively taking average Quaternary activity [1].

**4.5. The likelihood function**

hot springs [20, 54, 55]

and values of 10 km2

Here the *a priori* PDF is conditioned 3

He/4

in the crust, and hence potential volcanism based on distinct 3

since it is not possible to differentiate heat from *P* wave velocity alone.

data into a likelihood function according to how such information is judged by the expert and/or indicated by experimental result to relate to the distribution of volcanism [1]. Helium isotopes have been shown to provide evidence for the presence of mantle derived materials

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

18]. [1] looked at seismic tomographs and geothermal gradients. This is because *P* velocity perturbations (*ΔV* / *V* ) in particular at 40 km depth [29] is a good estimate of the minimum depth of partial melting in the mantle for most of the volcanoes in Tohoku. Geothermal gra‐ dients on the other hand were used by [1] as an additional aid to *P* velocity perturbations

**Figure 7.** Distribution of R/RA data (Ra denotes the atmospheric 3He/4He ratio of 1.4×10-6) taken from boreholes and

In order to compare the R/RA ratios, cumulative plots of values around all volcanic events

all volcanic events (8a) and volcanic events less than 100 ka (8b). In both cases approximate‐ ly 90% of all volcanic events are distributed in regions with R/RA ratios greater than 3. In

The R/RA ratios are interpolated to represent a continuous, differentiable surface and then the spatial data are mapped into a likelihood function based on the percentage of recent volcanic events that lie within the binned R/RA ratios in Figure 8. For low P velocity perturbation, [1] as‐ sumed an inverse linear relationship; based on the interpretation that low *P* velocity perturba‐ tion corresponds to partial melting (and hence increased probability of volcanism). In this case, 10% of volcanoes less than 100 ka located in regions where *ΔV* / *V* ranged from -6% to -5% etc. For geothermal gradients [1] used a linear relationship for recasting the data values as a PDF.

other words 90% volcanoes are located in regions where 3

bins over all of Tohoku are plotted. Figure 8 shows R/RA ratios below

He/4

He is elevated.

He ratios. This is done by normalizing additional

He/4

He ratios (Figure 7) [17,

Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 135

Using smoothing coefficients of 1 - 1.5 km for the Cauchy kernel, as well as weighting eruption volumes, probability plots were constructed using equation (3). A probability contour plot for one case is shown in Figure 6.

**Figure 6.** Probabilities of one or more volcanic events occurring in the next 100 ka based on *a priori* PDF (Cauchy (*h* = 1.5 km). White triangles denote the volcanic events used in the calculation and black lines are active faults [35]

The highest probabilities are located in the Sengan region (10-6 - 10-5 / a) which has the highest density of volcanic events in the Tohoku volcanic arc. By testing the two volcanic event subdefinitions (weighted with and without eruption volume), [1] found that the probabilities in the vicinity of monogenetic volcanoes on the back-arc region were higher when volcanic events were not weighted with eruption volumes (1 - 4 x 10-7/a, weighted; 1 - 4 x 10-6/a, unweighted), whereas the probabilities around established centers such as Iwaki, Towada, Sen‐ gan and Chokai were reduced slightly. This is expected as volcanoes with large eruption volumes are the sites of highest magma production. However if the focus of the assessment is on new volcano event formation, irrelevant of whether the new volcano evolves into are large complex stratovolcano and/or caldera or not, then selecting the volcanic event definition that is not weighted with eruption volume would seem more appropriate.

### **4.5. The likelihood function**

where, *N(t)* represents the number of future volcanic vents that occur within time *t* and area *ΔxΔy* (10 km x 10 km). The parameter *λx*,*y* is normalized to unity across the Tohoku, so, equa‐ tion (3) represents the probability of one or more volcanic event(s) forming in an area *ΔxΔy*cen‐ tred on point *xp*, *yp* given the formation of a new volcanic event in Tohoku. This calculation is repeated on a grid throughout Tohoku. The resolution is such that the spatial recurrence rate *λx*,*y*does not vary within each cell. For the regional recurrence rate*λt* an average of 120 volcan‐

Using smoothing coefficients of 1 - 1.5 km for the Cauchy kernel, as well as weighting eruption volumes, probability plots were constructed using equation (3). A probability contour plot for

**Figure 6.** Probabilities of one or more volcanic events occurring in the next 100 ka based on *a priori* PDF (Cauchy (*h* = 1.5 km). White triangles denote the volcanic events used in the calculation and black lines are active faults [35]

The highest probabilities are located in the Sengan region (10-6 - 10-5 / a) which has the highest density of volcanic events in the Tohoku volcanic arc. By testing the two volcanic event subdefinitions (weighted with and without eruption volume), [1] found that the probabilities in the vicinity of monogenetic volcanoes on the back-arc region were higher when volcanic events were not weighted with eruption volumes (1 - 4 x 10-7/a, weighted; 1 - 4 x 10-6/a, unweighted), whereas the probabilities around established centers such as Iwaki, Towada, Sen‐ gan and Chokai were reduced slightly. This is expected as volcanoes with large eruption volumes are the sites of highest magma production. However if the focus of the assessment is on new volcano event formation, irrelevant of whether the new volcano evolves into are large complex stratovolcano and/or caldera or not, then selecting the volcanic event definition that is

**Towada**

ic events per million years is used, effectively taking average Quaternary activity [1].

one case is shown in Figure 6.

**Sengan R.**

**Iwaki**

134 Updates in Volcanology - New Advances in Understanding Volcanic Systems

**Chokai**

not weighted with eruption volume would seem more appropriate.

**Monogenetic volcanoes**

Here the *a priori* PDF is conditioned 3 He/4 He ratios. This is done by normalizing additional data into a likelihood function according to how such information is judged by the expert and/or indicated by experimental result to relate to the distribution of volcanism [1]. Helium isotopes have been shown to provide evidence for the presence of mantle derived materials in the crust, and hence potential volcanism based on distinct 3 He/4 He ratios (Figure 7) [17, 18]. [1] looked at seismic tomographs and geothermal gradients. This is because *P* velocity perturbations (*ΔV* / *V* ) in particular at 40 km depth [29] is a good estimate of the minimum depth of partial melting in the mantle for most of the volcanoes in Tohoku. Geothermal gra‐ dients on the other hand were used by [1] as an additional aid to *P* velocity perturbations since it is not possible to differentiate heat from *P* wave velocity alone.

**Figure 7.** Distribution of R/RA data (Ra denotes the atmospheric 3He/4He ratio of 1.4×10-6) taken from boreholes and hot springs [20, 54, 55]

In order to compare the R/RA ratios, cumulative plots of values around all volcanic events and values of 10 km2 bins over all of Tohoku are plotted. Figure 8 shows R/RA ratios below all volcanic events (8a) and volcanic events less than 100 ka (8b). In both cases approximate‐ ly 90% of all volcanic events are distributed in regions with R/RA ratios greater than 3. In other words 90% volcanoes are located in regions where 3 He/4 He is elevated.

The R/RA ratios are interpolated to represent a continuous, differentiable surface and then the spatial data are mapped into a likelihood function based on the percentage of recent volcanic events that lie within the binned R/RA ratios in Figure 8. For low P velocity perturbation, [1] as‐ sumed an inverse linear relationship; based on the interpretation that low *P* velocity perturba‐ tion corresponds to partial melting (and hence increased probability of volcanism). In this case, 10% of volcanoes less than 100 ka located in regions where *ΔV* / *V* ranged from -6% to -5% etc. For geothermal gradients [1] used a linear relationship for recasting the data values as a PDF.

long-term, given that a volcanic event will occur in the Tohoku volcanic arc during 100 ka. Figure 9 shows a comparison of the *a priori* probability (9a) and a posteriori probability (9b)

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 137

The probability of new volcanic event formation in the forearc region to the east of the volcanic front is reduced slightly in the *a posterior* probability calculation. This is more evident when we repeat the calculation for new volcanic events forming in the next 1Ma (Figure 10).

**Figure 10.** Probability of the formation of a new volcanic event over the next 1Ma; *a priori* (a) and *a posteriori* (b)

The R/RA analyses are compared with the probability calculations conditioned on *P* velocity per‐ turbations (10 or 40 km) (Figure 11) and geothermal gradients (Figure 12) [1]. The *a posteriori* prob‐ ability below Iwaki volcanic event is particularly low when conditioned on 40 km depth P velocity perturbation datasets but that there was no significant change beneath Chokai, anoth‐ er andesitic volcano on the back-arc side of Tohoku. With the *a posteriori* calculation condi‐ tioned on the R/RA analyses, no decrease is seen in the probabilities below Iwaki volcano when compared to *a priori* plots. Similar results can be seen when probabilities are conditioned on 10km

[1] found that *a posteriori* probabilities are not reduced when compared to *a priori* probabilities in the northern regions when conditioning on shallower (10 km) *P* velocity perturbations or on geothermal gradients. This seems reasonable as seismic velocity structure [57] and the depth of Curie isotherms [58] in this part of Tohoku reveal high-temperature-like geophysical anomalies at depths of up to 10 km below Iwaki volcano which may be indicative of the

depth P velocity perturbations (Figure 11a) or geothermal gradients (Figure 12) [1].

probability plots calculated with a Cauchy kernel (*h* = 1.5 km) conditioned on R/RA ratios.

shallower depths (ca. 10km) of magma chambers.

conditioned on R/RA ratios of one or more volcanic events forming in 100 ka.

a b

**Figure 8.** umulative plots of R/RA values below volcanic events and the whole of Tohoku (10km2 grid spacing).

### **4.6. A posteriori probabilities**

Finally the *a posteriori* PDF is calculated from the likelihood function and the *a priori* PDF using equation (1). The integral across the entire field of both the *a priori* and the *a posteriori* PDFs is set to unity; however the shape of the distribution is modified by the likelihood function. The probability of a new volcanic event is calculated for each grid point using equation (3).

**Figure 9.** Comparison of *a priori* (a) and *a posteriori* probability plots (b) calculated with a Cauchy kernel (*h* = 1.5 km) conditioned on R/RA ratios. Both PDFs a weighted by eruption volume. Black lines are active faults [53].

Using equations (1) to (3) above, two dimensional probability plots are subsequently con‐ structed showing the probability of one or more future volcanic event(s) forming during the long-term, given that a volcanic event will occur in the Tohoku volcanic arc during 100 ka. Figure 9 shows a comparison of the *a priori* probability (9a) and a posteriori probability (9b) conditioned on R/RA ratios of one or more volcanic events forming in 100 ka.

The probability of new volcanic event formation in the forearc region to the east of the volcanic front is reduced slightly in the *a posterior* probability calculation. This is more evident when we repeat the calculation for new volcanic events forming in the next 1Ma (Figure 10).

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cummulative

0 2 4 6 8 10 R/ RA

136 Updates in Volcanology - New Advances in Understanding Volcanic Systems

a b

Forearc region

**4.6. A posteriori probabilities**

R/ RA Cor Volcanoes

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Present volcanic front in Tohoku

All QuaternaryVolcanic events Volcanic events< 100ka

Cummulative

**Figure 8.** umulative plots of R/RA values below volcanic events and the whole of Tohoku (10km2 grid spacing).

probability of a new volcanic event is calculated for each grid point using equation (3).

Finally the *a posteriori* PDF is calculated from the likelihood function and the *a priori* PDF using equation (1). The integral across the entire field of both the *a priori* and the *a posteriori* PDFs is set to unity; however the shape of the distribution is modified by the likelihood function. The

**Figure 9.** Comparison of *a priori* (a) and *a posteriori* probability plots (b) calculated with a Cauchy kernel (*h* = 1.5 km)

Using equations (1) to (3) above, two dimensional probability plots are subsequently con‐ structed showing the probability of one or more future volcanic event(s) forming during the

conditioned on R/RA ratios. Both PDFs a weighted by eruption volume. Black lines are active faults [53].

0 2 4 6 8 10 R/ RA

R/ RA Cor Volcanoes

**Figure 10.** Probability of the formation of a new volcanic event over the next 1Ma; *a priori* (a) and *a posteriori* (b) probability plots calculated with a Cauchy kernel (*h* = 1.5 km) conditioned on R/RA ratios.

The R/RA analyses are compared with the probability calculations conditioned on *P* velocity per‐ turbations (10 or 40 km) (Figure 11) and geothermal gradients (Figure 12) [1]. The *a posteriori* prob‐ ability below Iwaki volcanic event is particularly low when conditioned on 40 km depth P velocity perturbation datasets but that there was no significant change beneath Chokai, anoth‐ er andesitic volcano on the back-arc side of Tohoku. With the *a posteriori* calculation condi‐ tioned on the R/RA analyses, no decrease is seen in the probabilities below Iwaki volcano when compared to *a priori* plots. Similar results can be seen when probabilities are conditioned on 10km depth P velocity perturbations (Figure 11a) or geothermal gradients (Figure 12) [1].

[1] found that *a posteriori* probabilities are not reduced when compared to *a priori* probabilities in the northern regions when conditioning on shallower (10 km) *P* velocity perturbations or on geothermal gradients. This seems reasonable as seismic velocity structure [57] and the depth of Curie isotherms [58] in this part of Tohoku reveal high-temperature-like geophysical anomalies at depths of up to 10 km below Iwaki volcano which may be indicative of the shallower depths (ca. 10km) of magma chambers.

**5. Discussion**

The main advantage of probabilistic based models over deterministic models is that the probability of new volcano event formation is never zero. [1] showed that Bayesian inference is well-suited for formally combining observations relevant to the imaging of the magma source region (e.g. seismic tomography) with quantitative methods for estimation of volcano intensity. Moreover, the strength of Bayesian inference is that probabilistic assessments can be improved with increased understanding of the physical processes governing magmatism and/ or data that may be indicative of future volcanism such as the helium isotope ratios presented here. Nevertheless it is worth examining the logic behind what we perceive to be 'data' and

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 139

[1] used the volcano geographical datasets themselves as a starting point in their analy‐ sis. The same approach was applied in this chapter. In the first step a Cauchy kernel was used to calculate*λx*,*y*. This means that the probability new volcanic event formation de‐ creases with increasing distance from existing volcanic events. In the case of selecting a location for a geological repository, there may be a need to have a conservative estimate and accept that extreme events may occur. In this case, selection of the Cauchy as the *a priori* PDF would be most appropriate due to the thickness of the tails. This is especially the case if we have to make probability calculations for periods for 1Ma where the tec‐ tonic setting can change, and we may have a shift in the location of the volcanic front. The probabilities in distal regions would only be reduced in the *a posterior* probability calculation if newly obtained evidence in such regions shows that volcano formation is zero or close to zero. Since R/RA ratios vary due to the heterogeneous release of mantle helium and elevated ratios and are likely to indicate the presence of partial melting [e.g. 20] datasets may give some indication on the future location of volcanism even in nonvolcanic regions. Seismic tomography on the other hand offers a direct view of the man‐

tle that can be interpreted in terms of degree of partial melting [e.g. 58, 59].

It could be equally argued, however that the logic of [1] should be reversed in that the models based on seismic tomography or elevated helium isotope ratios are in fact *a priori* information or knowledge, and the location of volcanic events the 'data'. The philosophy here is that we assume new volcanic events will form in regions where partial melting is likely to be occurring now and that the distribution of known volcanic events are the datasets updating our model and/or knowledge. However, this may be true for the very recent volcanism up to about 1,000 years say, but how relevant are volcanic events that formed over 100,000 years ago or more to the present day geophysical snap-shot of the Earth's crust or upper mantle? This question is difficult to answer as there is very little information on the temporal behaviour of partial melting in the mantle. This is also evident when we try to evaluate our forecasts below. A problem here is that we are always trying to predict the formation of future volcanic events which may or may not be related to historic volcanic events. Our closest 'data' to such future events are thus present day geophysical snap shots of the current conditions in the crust or

what we mean by *a priori* information and knowledge.

**5.1. Which datasets are a priori information?**

**Figure 11.** *A posteriori* probability plots calculated with: (a) Cauchy kernel (*h* = 1.5 km) conditioned on Δ*V* / *V* at 10 km depth; (b) Cauchy kernel (*h* = 1.5 km) conditioned on Δ*V* / *V* at 40 km depth (modified from [1]).

**Figure 12.** *A posteriori* probability plots calculated with Cauchy kernel (*h* = 1.5 km) conditioned on geothermal gradi‐ ents (modified from [1])

### **5. Discussion**

**Iwaki Iwaki**

**(a) (b)**

138 Updates in Volcanology - New Advances in Understanding Volcanic Systems

**Chokai Chokai**

**Iwaki**

**Chokai**

ents (modified from [1])

**Figure 11.** *A posteriori* probability plots calculated with: (a) Cauchy kernel (*h* = 1.5 km) conditioned on Δ*V* / *V* at 10

**Figure 12.** *A posteriori* probability plots calculated with Cauchy kernel (*h* = 1.5 km) conditioned on geothermal gradi‐

km depth; (b) Cauchy kernel (*h* = 1.5 km) conditioned on Δ*V* / *V* at 40 km depth (modified from [1]).

The main advantage of probabilistic based models over deterministic models is that the probability of new volcano event formation is never zero. [1] showed that Bayesian inference is well-suited for formally combining observations relevant to the imaging of the magma source region (e.g. seismic tomography) with quantitative methods for estimation of volcano intensity. Moreover, the strength of Bayesian inference is that probabilistic assessments can be improved with increased understanding of the physical processes governing magmatism and/ or data that may be indicative of future volcanism such as the helium isotope ratios presented here. Nevertheless it is worth examining the logic behind what we perceive to be 'data' and what we mean by *a priori* information and knowledge.

### **5.1.

[1] used the volcano geographical datasets themselves as a starting point in their analy‐ sis. The same approach was applied in this chapter. In the first step a Cauchy kernel was used to calculate*λx*,*y*. This means that the probability new volcanic event formation de‐ creases with increasing distance from existing volcanic events. In the case of selecting a location for a geological repository, there may be a need to have a conservative estimate and accept that extreme events may occur. In this case, selection of the Cauchy as the *a priori* PDF would be most appropriate due to the thickness of the tails. This is especially the case if we have to make probability calculations for periods for 1Ma where the tec‐ tonic setting can change, and we may have a shift in the location of the volcanic front. The probabilities in distal regions would only be reduced in the *a posterior* probability calculation if newly obtained evidence in such regions shows that volcano formation is zero or close to zero. Since R/RA ratios vary due to the heterogeneous release of mantle helium and elevated ratios and are likely to indicate the presence of partial melting [e.g. 20] datasets may give some indication on the future location of volcanism even in nonvolcanic regions. Seismic tomography on the other hand offers a direct view of the man‐ tle that can be interpreted in terms of degree of partial melting [e.g. 58, 59].

It could be equally argued, however that the logic of [1] should be reversed in that the models based on seismic tomography or elevated helium isotope ratios are in fact *a priori* information or knowledge, and the location of volcanic events the 'data'. The philosophy here is that we assume new volcanic events will form in regions where partial melting is likely to be occurring now and that the distribution of known volcanic events are the datasets updating our model and/or knowledge. However, this may be true for the very recent volcanism up to about 1,000 years say, but how relevant are volcanic events that formed over 100,000 years ago or more to the present day geophysical snap-shot of the Earth's crust or upper mantle? This question is difficult to answer as there is very little information on the temporal behaviour of partial melting in the mantle. This is also evident when we try to evaluate our forecasts below. A problem here is that we are always trying to predict the formation of future volcanic events which may or may not be related to historic volcanic events. Our closest 'data' to such future events are thus present day geophysical snap shots of the current conditions in the crust or upper mantle and/or newly formed or forming volcanic events. This has been the motivation for [1] to use such geophysical data or models as the basis of the likelihood function.

a b

tioned on R/RA ratios.

**Figure 13.** Verification probability plots calculated using all volcanic events before 100 ka (white triangles) in order to predict the subsequent distribution of volcanic events that formed from 100 ka to present (red triangles) for (a) the *a priori* probability (Cauchy, h=1.5km, eruption volume weighting included) and the *a posteriori* probability (b) condi‐

Application of the Bayesian Approach to Incorporate Helium Isotope Ratios in Long-Term Probabilistic Volcanic

Hazard Assessments in Tohoku, Japan http://dx.doi.org/10.5772/51859 141

**Figure 14.** Verification probability plots calculated using all volcanic events before 100 ka (white triangles) in order to predict the subsequent distribution of volcanic events that formed from 100 ka to present (red triangles): (a) Cauchy kernel (*h* = 1.5 km) conditioned on Δ*V* / *V* at 40 km depth, (b) Cauchy kernel (*h* = 1.5 km) conditioned on geothermal

**Iwaki Iwaki (a) (b)**

gradient and Δ*V* / *V* at 10 and 40 km depths. (Figure modified from [1]).

On the other hand there are also practical aspects to be considered particularly when starting a hazard analysis in a region where there have not been many studies. In such a case, the only data available to begin with might be just the geographical location of volcanoes. Information from more complicated and expensive surface based investigations might not come until later.

### **5.2. Model evaluation**