**5. Conclusions**

This report furthers our previous one on the targeted analysis of ceramides (d18:0, d18:1) in almond and pistachio [9]. This technique exploits the performance of the triple quadrupole tandem mass spectrometer well beyond its traditional use by analytical chemists.

Success with this approach will prompt a complete investigation of the structural details of the observed phytochemicals that cannot be resolved with the analytical and spectroscopic techniques employed in this preliminary survey. Semi-preparative chromatographic separation of crude lipid preparations driven by the selective scan will enable to obtain enriched fractions of the unexpected components. The use of high-energy collision induced dissociation will characterize the connectivity of the ceramide and fatty acid subunits [11], and deglycosidation will enable to separately identify the linked hexose.

Availability of a fast and comprehensive method to screen lipid extracts for trace sphingosides with chemically decorated long chain bases will be applied to analyze several more seeds of a nutraceutical interest that are the base of typical foods of the Italian lore.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2304-8158/9/2/110/s1. Figure S1: High-resolution mass spectrometry analysis for the confirmation of the unexpected ceramide species in the extract of a pistachio cultivar. Table S1: High-resolution measurement of fragment ion spectra of the four main ceramide species. Figure S2: High-resolution molecular (a, c, e) and fragment ion spectra (b, d, f) of triglycerides. Figure S3: Chromatographic traces of the Par263 and Par264 scans for a representative extract. Figure S4: Formation of protonated diglyceride and acylium fatty acid fragments from triglycerides. Figure S5: Plot of the relationship of chromatographic retention vs. molecular size for ceramides.

**Author Contributions:** Conceptualization, F.M.R.; methodology, F.M.R., M.D.C.; investigation, F.M.R., M.D.C., M.B.; resources, M.I., R.P.; data curation, F.M.R., M.D.C., M.B.; writing—original draft preparation, F.M.R., M.D.C, R.P.; writing—review and editing, F.M.R., M.D.C, R.P, R.G, M.I.; visualization, F.M.R., M.D.C.; supervision, R.P.; project administration, R.P., M.I.; funding acquisition, R.P., M.I., R.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** MDC receives support from the PhD program in Molecular and Translational Medicine of the Università degli Studi di Milano, Italy. Part of this work was carried out in OMICs, an advanced mass spectrometry platform established by the Università degli Studi di Milano.

**Conflicts of Interest:** The authors declare no conflict of interest.


**5. Opportunities related to the Fourth Industrial Revolution**

Besides the problems that may arise or get bigger during the Fourth Industrial Revolution, there are also significant economic and social opportunities that may contribute to a sustainable socioeconomic growth (see [32, 33] among others). Concerning entrepreneurship, new technologies must not be treated as a threat for human work but as a valuable tool/assistant for employees to increase their productivity and facilitate their decision-making and for entrepreneurs to boost their business competitiveness and productivity. Governments could also support entrepreneurship, focusing on the following:

These policies may benefit both businesses and governments; entrepreneurs will be smoothly adapted to the new technological conditions and the digitalization having the appropriate support, and governments will increase their tax revenue due to the higher labor income and the increased business gains (due to the use of new technologies that improves businesses' effectiveness). This additional tax revenue may finance higher public spending on health and education and support additional jobs in these areas.

The new IT systems may also give to entrepreneurs the chance to participate in new supply chains for small- and medium-sized enterprises and have access to new product and service markets that under other conditions would be difficult and costly. The development of new markets with greater quantity and variety of products and services, and eventual lower prices, in combination with the improvement of the existing jobs' efficiency and the improvement of customer service, will benefit consumers driving to a demand increase and consequently to a labor demand increase. New technologies may further increase the labor demand by creating new, stable, and well-paid jobs in innovative technological sectors that will

**21**

hunger.

**6. Case study: Greece**

and elderly care and less to routine tasks.

*Fourth Industrial Revolution: Opportunities, Challenges, and Proposed Policies*

acquire a healthy social life having a better work-life balance.

reduce the potential job loss due to automation and will substantially contribute to the fight against poverty worldwide. A characteristic example is the information and communication technology (ICT) sector that has been a key driver of economic growth in OECD countries and led to a 22% increase in jobs in 2013. Briefly, new technologies may contribute to the reduction of unemployment, to the fight against poverty and to the improvement of the quality and the prices of products and services offered to people improving in this way the quality of their lives.

In the direction of human life quality improvement, significant steps have also been done in the health sector. The broad technological innovation in the field of medicine, involving nanotechnology and genetic engineering, allow the treatment of devastating diseases and illnesses increasing the life expectancy. Moreover, smart digital assistants and intelligent robots are able to perform complex surgical procedures that under different circumstances would be impossible to be done. Except the physical health, the opportunity for more flexible forms of work due to the technological progress improves the mental health of people as well; workers have the possibility to distribute their time according to their needs, to create family and

Digital technology also facilitates the access of all people (in developing and developed countries) to education giving them the chance to improve their knowledge and their skills by attending educational and training programs by distance. In this way, the barriers in access to quality education for all are reduced, and the fight against inequalities and discrimination among countries and social classes becomes more effective. Moreover, the improvement of their skills enforces the self-confidence and the competiveness of individuals in labor market, helps them to be smoothly and quickly adapted to the new conditions, gives them the incentives to live and work in their country (and not to immigrate), and helps them to efficiently deal with their economic problems by becoming more productive in their work. In this way, labor income increases contributing to the reduction of poverty and

The *fight against poverty and hunger* is also supported by the technological progress in the field of sustainable agricultural production and the "smart farming," using new effective "smart" cultivation systems that may help people not only to have food for a specific period but also to learn how to easily and effectively cultivate the land in order to ensure their food forever. In this direction, the varieties of drought-resistant vegetables and fruits that may ensure food to people who live in countries that are strongly affected by drought like many countries in Africa also contribute. The technological innovations in recycling for industry and households such as the innovative composting methods may also help in the direction of food

An interesting case study is that of Greece. It is about a country that does not belong to heavy industrial economies, such that of Germany, Slovakia, and Italy which have relatively inelastic labor markets and large tertiary service sectors that may be strongly affected by the Fourth Industrial Revolution. Jobs in Greece are more related to tasks that require the involvement of human factor such as teaching

In general, the automation process involves three overlapping waves: (i) an *Algorithm wave* that mainly focuses on automating simple computational tasks such as structured data analysis and mathematical calculations, and it is expected to reach its full maturity by 2020, (ii) an *Augmentation wave* that focuses on the

waste reduction contributing further to the fight against hunger.

*DOI: http://dx.doi.org/10.5772/intechopen.90412*

### *Fourth Industrial Revolution: Opportunities, Challenges, and Proposed Policies DOI: http://dx.doi.org/10.5772/intechopen.90412*

reduce the potential job loss due to automation and will substantially contribute to the fight against poverty worldwide. A characteristic example is the information and communication technology (ICT) sector that has been a key driver of economic growth in OECD countries and led to a 22% increase in jobs in 2013. Briefly, new technologies may contribute to the reduction of unemployment, to the fight against poverty and to the improvement of the quality and the prices of products and services offered to people improving in this way the quality of their lives.

In the direction of human life quality improvement, significant steps have also been done in the health sector. The broad technological innovation in the field of medicine, involving nanotechnology and genetic engineering, allow the treatment of devastating diseases and illnesses increasing the life expectancy. Moreover, smart digital assistants and intelligent robots are able to perform complex surgical procedures that under different circumstances would be impossible to be done. Except the physical health, the opportunity for more flexible forms of work due to the technological progress improves the mental health of people as well; workers have the possibility to distribute their time according to their needs, to create family and acquire a healthy social life having a better work-life balance.

Digital technology also facilitates the access of all people (in developing and developed countries) to education giving them the chance to improve their knowledge and their skills by attending educational and training programs by distance. In this way, the barriers in access to quality education for all are reduced, and the fight against inequalities and discrimination among countries and social classes becomes more effective. Moreover, the improvement of their skills enforces the self-confidence and the competiveness of individuals in labor market, helps them to be smoothly and quickly adapted to the new conditions, gives them the incentives to live and work in their country (and not to immigrate), and helps them to efficiently deal with their economic problems by becoming more productive in their work. In this way, labor income increases contributing to the reduction of poverty and hunger.

The *fight against poverty and hunger* is also supported by the technological progress in the field of sustainable agricultural production and the "smart farming," using new effective "smart" cultivation systems that may help people not only to have food for a specific period but also to learn how to easily and effectively cultivate the land in order to ensure their food forever. In this direction, the varieties of drought-resistant vegetables and fruits that may ensure food to people who live in countries that are strongly affected by drought like many countries in Africa also contribute. The technological innovations in recycling for industry and households such as the innovative composting methods may also help in the direction of food waste reduction contributing further to the fight against hunger.


*3.2. Reconstruct Linear Equations*

After filtering outliers, the linear equations in Equation (3) are reconstructed by using the remaining *m* measurements as: .

$$
\dot{A}\_{(m)}\theta\_0 = \dot{b}\_{(m)}\tag{15}
$$

However, the linear equations are always close to ill-conditioned, because the elements in the coefficient matrix differ by orders of magnitude. Besides, the unreasonable sensor layout can also result in the linearly dependence of certain equations, in turn to the ill condition of linear equations. The ill condition of linear equations indicates a large difference in the final location results, with a minor change in the coefficient matrix. Moreover, an ill-conditioned linear equation system always has a large condition number of the coefficient matrix . *A*:

$$cond(A) = ||\dot{A}|| \cdot ||\dot{A}^{-1}||\tag{16}$$

Besides, location results deviate largely with a minor disturbance in the coefficient. Therefore, a preconditioning method is applied to linear equations to reduce the condition number and give a more accurate closed-form solution, which is named the preconditioned closed-form solution.

The non-singular and diagonal matrix *P* is found, then the solution of Equation (15) is transformed into that of Equation (17) as:

$$
\dot{P}\dot{A}\theta\_o = P\dot{b} \tag{17}
$$

where *P* = *diag*( 1 *s*1 , 1 *s*2 , ··· , 1 *sn* ) and, *si* = max 1≤*j*≤*m* |(*A*)*ij*|, *i* = 1, 2, ··· , *n*.

Finally, a more accurate location result *θo* can be obtained by:

$$\theta\_o = \left[ \left( \dot{P} \dot{A} \right)^T P \dot{A} \right]^{-1} \left( P \dot{A} \right)^T P \dot{b} \tag{18}$$

due to the improved condition number *cond*(*P* . *A*) *cond*( . *A*).


4.1.4. Currents

Currents speed and direction at 2.5 m depth were validated from 2015 to 2019 using mooring data from CMEMS INS TAC as reported in Table 2 and represented in Figure 5a (red stars). Metrics are reported in Table 7. The main impact due to wave fields on currents statistics is evident when considering the velocity directions, where the coupling reduces the BIAS and RMSE by ~16% and ~10%, respectively, when compared to the control run H0. The improved skill can be mostly attributed to the use of modified stress, showing experiment H2 the lowest BIAS (36◦).

**Table 7.** Validation of 2.5 m depth currents speed and direction: averaged statistics using data as provided by the available moorings in the 2015–2019 period.

The velocity direction RMSE indicated that the H1, H2 and H3 experiments have comparable errors in the range of 107–111◦ and that H4 is the best implementation, with a reduced error of around 100◦. Conversely to the direction, no significant differences were found for the statistics of currents speed in the forced simulation.

Figure 9 shows the five years averaged currents speed and direction at the mooring locations, and it allows us to appreciate how the currents change when hydrodynamics is forced with waves. Each column in the plot represents a specific mooring: EUXRo01, EUXRo02 and EUXRo03, from left to right.

**Figure 9.** Currents speed and direction at 2.5 m deep for moorings (**first row**), H0 (**second row**) and H4 (**third row**). Columns represent the three different mooring locations available.

Additionally, currents speed and direction from observations (first row), from the H0 experiment (second row) and the H4 experiment (third row) is shown. From a qualitative point of view, we can see that in both forced and free-run experiments the model has a stronger western currents component than the observation, probably due to low resolution in space and time of atmospheric forcings, considering also that the observation is taken on a precise location/time.

The main wave direction in this area is aligned with that of the currents (from North-East towards South-West). H0 experiment has a prevalence of one-directional bin, while if waves are considered, the direction of the main current is described by 3–4 directional bins. This explains that the currents simulated by the H4 experiment have a wider dynamic.

Contrarily to H0, in which stronger currents are defined mainly by one direction, the forced experiment H4 has 3–4 directional bins with almost the same speeds and occurrence frequency. Despite this change in magnitude, statistics from Table 5 do not highlight improved skills in the HM simulation. Unfortunately, the very low number of available moorings in the Black Sea region prevents us from performing a more robust validation of the results, but the wider dynamics of H4, which is closer to the observation than H0, seemed to be promising.

#### 4.1.5. RMSE vs. Significant Wave Height

To evaluate a correspondence between the improved representation of the ocean physics in the Black Sea thanks to wave-currents forced model, we propose in Figure 10 the time series of daily averaged RMSE differences for temperature (herein, RMSEdif) between fully-forced H4 and free-run H0 experiments in the layer 5–20 m and the corresponding mean, at observation location, of Hs on 2019.

**Figure 10.** Timeseries of daily H4-H0 RMSE difference (RMSEdif) compared to Hs. The grey line represents RMSEdif and is associated with a green or red scatter point if the forced run is respectively better or worse than free-run. The cyan line represents the mean Hs at observation locations.

This plot helps to evaluate whether the positive or negative impact of the coupling is dependent on specific Hs values or the whole Hs spectrum. The figure shows that in general, the H4 has an error lower than H0 in most of the year. The range of variability for RMSEdif evolves seasonally, during Winter and Spring is confined to [ −0.5: +1 ◦C], with the lowest value between mid of February—beginning of April, while during Summer and Autumn the range extends to [ −2: +1.8 ◦C]. From March to May and from September to October, Hs rarely exceeds 1.5 m and it is always close or below 1.0 m: in this period, the fully-forced run H4 seems to have the best performances (e.g., lower error than H0). This investigation revealed that the forced experiment performs better than the free-run when the sea state has no large fluctuations, as in February–April or in November During Summer and January, in which the variability of the wavefield is high, the forced run still performs better in most of the cases, but there are several days in which the H0 is the best.

As a general indication, the forced model confirms its good performance, demonstrating that in the thermocline region the improvement can be of the order of about 0.5 ◦C on average. However, to better assess this conclusion, the analysis requires further dedicated investigation over a smaller time scale.

#### 4.1.6. Validation for the Wave Component

In this study, a three-year validation (2016–2018) of Hs was conducted using J2 satellite data. The dataset, filtered according to a quality check, consisted of 10,479, 9035 and 10,447 observations for 2016, 2017 and 2018, respectively. Figure 11 compares W0 experiment (a-panel) with W3 experiment (b-panel) considering the whole dataset (29,961 observations).

**Figure 11.** Significant wave height validation using Jason-2 satellite from 2016 to 2018. Scatter-plots (**a**,**b**) refer to W0 and W3 experiments, respectively.

The coupling with currents and ΔT (W3 experiment) induced a performance improvement in all the statistics: BIAS was reduced from −7 cm to −5.7 cm, RMSE from 28.3 cm to 27.8, the Scatter Index from 0.201 to 0.199, while Pearson's correlation increased from 0.92 to 0.93 and Slope from 0.93 to 0.95.

Table 8 summarises the statistics for all of the wave experiments. All three wave experiments when forced with hydrodynamic fields (W1, W2 and W3) improved model performance, albeit to different extents. The lowest and negligible impact was derived from only-currents (W1 experiment). This result could be a side-effect of the validation method here used. Again, the absence of observations, e.g., from buoy wave gauge in this case, strongly affected our capability to validate the experiments and we were obliged to use satellite data, which has two main disadvantages: it is not reliable near the coast, where currents are stronger and may impact the waves; it does not provide information about wave direction, which could be affected by refraction phenomena.

On the contrary, ΔT (W2 experiment), which acts mainly on Hs has been positively evaluated and confirmed what is demonstrated in the literature [29]. When both currents and ΔT were considered (W3 experiment), the lowest error was obtained, with a reduction of ≈−18% in BIAS, ≈−2% in RMSE. Even the precision of the simulation has been improved, with reduction of scatter index and increasing 1% in *ρ* in correlation.

**Table 8.** Significant wave height (m) validation statistics.


**5. Conclusions**

The chapter presents an analysis of the gap between actual and expected performance in four Green Star institutions in Melbourne, Australia, and how POE generated results assisted in achieving this objective. Outcomes include the development of clear assessment mechanisms for establishing links between performance measurement and performance management with an understanding of how occupants view

#### *Highlighting the Design and Performance Gaps: Case Studies of University Buildings DOI: http://dx.doi.org/10.5772/intechopen.102779*

its value and description of a better performing building as compared to the others. The comparison was carried out on how well the building has been managed in conjunction with the sustainable building targets.

As observed by analysis of POE results of the case study buildings, it has been clearly indicated that the building users (mainly academic and non-academic staff) are not satisfied, and their needs have not been considered in the initial design brief. However, after triangulating the outcomes and studying the broader context, all four buildings have met their key parameters in terms of Green Star certification utility targets. What worked well for all buildings have been exceptional teaching and learning spaces, student study areas and institution image. Building variables that did not work well were lack of consultation with end-users and basic design faults or technical issues. This signifies that it is important to realize that the organizational facilities function cannot exist in isolation if the organization is to effectively exploit its entire asset base to best support the delivery of core services. Making constructive use of performance measurement results is critical if the organization is to improve the performance of its assets. At both individual and organisational levels, a better understanding of the relationship between what is being done and how well the organization succeeds needs to be developed. Based on the analysis, senior management and facility managers are seen to have a direct impact on building operations and performance. They can also adjust or redirect their strategies or identify new strategies for the organization.

Following the lead by the European Union since early 2021, there is a global momentum that is currently shifting towards Nearly Zero Energy Buildings, that is, high performing buildings utilising nearly zero or extremely low amounts of energy being met by renewable sources. While new commercial buildings in the US and Canada are now required to follow these principles, Australia is taking initiatives in the residential sector as well to achieve the same. Significantly, understanding the applications and potential of the building sector to effectively achieve overall sustainable outcomes. The outcomes of this study highlight that focusing on issues of building sustainability are not sufficient for a building's success. Being "green" is only one important feature of building success, but other aspects of building performance (user needs and engagement) must be considered as well. The focus on the green or sustainability aspect has detracted the construction industry from other equally important design issues relative to overall building performance. Understanding and taking the triple bottom line approach of being economically viable, environmentally responsible, and socially inclusive is important rather than simply aiming to be rated as "green" or sustainable. Furthermore, being sustainable and green should be translated to high-performance buildings for optimal benefits. Supporting this argument, the outcomes of this study are noteworthy for individual buildings be they academic or otherwise, and benefits wider government, non-government and market audiences seeking new knowledge about performance development.

Zero-energy programs often build on these frameworks supporting a variety of non-energy benefits relative to standard buildings, such as improved comfort, improved occupant health and productivity, enhanced indoor environment quality, and higher occupancy rates. Therefore, this study concludes that in making building codes, policies and management approaches more stringent, prioritisation and engagement of building stakeholders, and increased interest in decarbonisation, would assist in encouraging and assisting buildings that are truly zero-energy and zero-energy-ready.


**1. Introduction**

The benefits related to the specific properties of engineered nanomaterials (ENMs) have led to an increase in their use and production, raising particular attention to their environmental behavior and toxicological impact. The wide disparity in estimates of industrial production of ENMs demonstrates an important uncertainty in the quantification of ENM discharges into the environment [1–3]. Nevertheless, early studies have predicted that both terrestrial and aquatic environments are likely to be exposed to ENMs [1,4]. In the case of European surface waters, modeled concentrations of nanoparticles (NPs) were found to exceed 0.18 ng.L−1 and 150 ng.L−1 for Ag and ZnO NPs, respectively, in the 10% most exposed rivers, likely corresponding to rivers located near large cities and downstream river systems [5]. ENMs can be transformed before entering the environment,

**Citation:** Stetten, L.; Mackevica, A.; Tepe, N.; Hofmann, T.; von der Kammer, F. Towards Standardization for Determining Dissolution Kinetics of Nanomaterials in Natural Aquatic Environments: Continuous Flow Dissolution of Ag Nanoparticles. *Nanomaterials* **2022**, *12*, 519. https://doi.org/10.3390/ nano12030519

Academic Editor: Vivian Hsiu-Chuan Liao

Received: 19 December 2021 Accepted: 1 February 2022 Published: 2 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

for instance if they are released from wastewaters treatment plants [6]. Moreover, the discharge of pristine ENMs in natural environments should also be considered, since ENMs can be released through accidental events, weathering, or directly from the use of consumer products [7–10].

### *1.1. Dissolution of Nanoparticles in Aquatic Environments*

Dissolution is emphasized as one of the most important mechanisms governing the fate of ENMs in aquatic environments. The oxidative dissolution of Ag NPs has been largely studied in aquatic media [11–18]. For example, Li and Lenhart, (2012) [18] showed that the dissolution of Ag NPs and the release of Ag+ is significantly limited in natural river waters. It has been argued that aggregation of Ag NPs may hamper dissolution due to a substantial decrease in the specific surface area [19]. However, several studies found that dissolution of Ag NPs is dependent on the particle size rather than aggregation [14,20]. For regulatory testing of ENMs dissolution rates, this information is important since up to now it was seen as a requirement to bring ENMs into a stable dispersion for dissolution testing to avoid effects of agglomeration. Extrinsic parameters such as pH, ionic strength, inorganic/organic ligands, and temperature play also an important role. Gondikas et al. [21] demonstrated that dissolution of citrate- and PVP-capped Ag NPs increases in the presence of cysteine. In contrast, several studies showed that Ag NP dissolution is inhibited in the presence of humic and fulvic acids or in the presence of natural organic matter (NOM) with high sulfur and nitrogen content [17,22]. This points to a more complex process of ENMs dissolution than only the complexation of the released cations by NOM, lowering the free ion activity and thereby enhancing the apparent solubility and dissolution rate. Finally, pH is also an important factor controlling the dissolution of Ag NPs, being enhanced at low pH values [23–25].

For soluble ENMs such as Ag NPs, it has been established that the aqueous forms (e.g., dissolved ions or aqueous complexes) are to a large extent causing the toxic effects exerted by these ENMs [26–29]. Aqueous solubility and dissolution kinetics are thus essential for (eco-)toxicological hazard testing and risk assessment strategies and are a requested information for registration of ENMs at regulatory bodies such as the European Chemicals Agency (ECHA) [30]. Determining the dissolution rates of ENMs is particularly important in determining risk/hazard since the rate of release of toxic ions prior to interaction with ligands may be more important than equilibrium concentrations, and the lifetime of the particulate form will determine the exposure to this ENM. However, high degree of uncertainty remains when it comes to understanding dissolution kinetics of ENMs in aquatic environments, mostly due to the lack of studies using aqueous media representing the heterogeneity of the world's aquatic environments, and adapted experimental setups.

### *1.2. Experimental Methods to Study the Dissolution of Nanoparticles in Aquatic Environments*

When it comes to understanding the fate of ENMs in aquatic environments, a limiting factor is the lack of standardized experimental procedures to study the specific endpoints (solubility, dissolution rate, chemical transformation, hetero-agglomeration). Standardized procedures would allow easier comparison between different ENMs and forms of the same ENM, enable grouping and read-across between nanoforms, as well as the production of more reliable data that can be implemented in geochemical models. International regulatory bodies, such as ECHA, the Organization for Economic Cooperation and Development (OECD) and the International Organization for Standardization (ISO), have argued that these points should be addressed [31]. To date, several methods have been applied for dissolution testing of ENMs [16]. However, there is no standardized OECD guideline for dissolution testing of ENMs other than the guidance in OECD GD 318 [32,33]. The OECD TG 105 [34] has been evaluated as not applicable for ENMs [35] and OECD GD 29 has been identified as being in principle applicable with some adjustments for determining solubility [36,37]. Nanomaterial-specific OECD test guidelines for dissolution rate testing

in environmental media would thus be beneficial for generating data that are regulatory relevant and reliable [38].

Generally, dissolution-testing methods can be divided into static batch and continuous flow/flow-through approaches. Batch testing leads to a direct measure of the ENM solubility in a specific aqueous medium. Moreover, when using ultra-trace analytical techniques, it is possible to measure very low ion concentrations. However, it has several limitations, especially when dealing with particles that are rapidly dissolving. For instance, the ultra-centrifugation or centrifugal ultrafiltration of rapidly dissolving particles can lead to an overestimation of the dissolution, because of the dissolution of the particles during the centrifugation process. To the opposite, the dissolved fraction could be underestimated using filtration techniques if the dissolved ions are adsorbed to the ultrafiltration membrane or the walls of the filtration device. Furthermore, batch experiments are performed under static conditions (i.e., limited supply of exposure medium) and may lead to a change of the medium composition, due to ENMs dissolution and/or the re-precipitation of secondary species. The challenges associated with the batch systems can be overcome by using a flow-through/continuous flow setup. The continuous flow dissolution systems are considered to be more appropriate for dissolution rate measurements and present a more environmentally realistic system. It allows for working with high liquid-to-solid ratios (realistic high dilution of the ENM), and when using appropriate analytical instrumentation, it allows for the detection of low levels of dissolved species. Because of a constant and almost unlimited provision of the medium, the back reactions are limited and low electrolyte concentrations in the exposure medium can be used to mimic the chemical composition of natural waters. These results can finally serve as inputs into environmental fate modeling. The flow-through setup has been described in ISO TR 19057 [39]. For instance, Koltermann-Juelly et al. [40] studied the dissolution rates of 24 types of nanomaterials in phagolysosomal simulant fluids, and Bove et al. [31] investigated dissolution of Ag NPs in an in vitro assay simulating conditions which likely occur in human digestion. More recently, Keller et al. [41] applied continuous flow systems to study the dissolution of BaSO4 in phagolysosomal and lung lining fluids. These studies demonstrated the advantage of using continuous flow rather than static incubations to investigate and compare the dissolution rates of ENMs. Nevertheless, when adapted to simulate ENMs dissolution in specific biological media, these studies applied relatively low flow rates and high loadings of ENMs, which resemble elevated solid-to-liquid ratios and would not be translatable to environmentally relevant conditions.

Here, we aim to provide guidance and recommendations for nanomaterials dissolution testing and dissolution rates determination under environmentally relevant conditions. Based on a survey of surface water hydrochemistry, we outline the environmental concentration ranges of key parameters to be considered in studying ENMs dissolution in natural aquatic environments. We present the proof of concept of a continuous flow setup suitable to investigate dissolution kinetics at low ENMs concentrations, relevant for natural environments. The results obtained on Ag NPs exposed to artificial waters demonstrate the suitability of the continuous flow dissolution method to determine dissolution rates of ENMs under non-equilibrium environmentally relevant conditions. This work will aid in the standardization of a continuous flow dissolution approach adapted to study ENMs dissolution kinetics in natural aquatic environments, and supports the ongoing development towards OECD dissolution guidelines.


2.3.2. With TiO2 Semi-Transparent Coating

The deposited-drop experiment was repeated with semi-transparent coating formulated using TiO2 nanoparticles as an antibacterial product: TiO2 powder (KronoClean 7050, KRONOS/Société Industrielle du Titane, Paris, France) and TiO2 dispersion (Kronos type 7454, trial product, KRONOS/Société Industrielle du Titane, Paris, France). The coating formulation included water and acrylic based on the work of Martinez *et al.* [46], as shown in Table 1. Sterilized cover-glasses (26 × 76 mm2 ) were covered with the coatings by instilling 1 mL, so that the total area of each glass was coated. The cover-glasses were then placed under a sterile flow hood for air drying. After drying, the semi-transparent coatings with TiO2 powder (STC-SP (with silicates), STC-P) were gently sanded with fine sandpaper in order to prevent the possible inclusion of nanoparticles in the binder. The semi-transparent coatings with TiO2 in aqueous suspension (STC-A) were pre-aged by irradiating them with UV light (2.5 W/m2 ) for 80 h. The amount of TiO2 was estimated at 2.5 mg/cm2 for samples coated with TiO2 powder (STC-SP, STC-P) and 0.63 mg/cm2 for samples coated with TiO2 aqueous suspension (STC-A). In order to evaluate the possible inclusion of nanoparticles in the binder of STC-A, samples were also prepared with water and TiO2 aqueous suspension, without acrylic resin (STC-A2). For each test sample, corresponding controls were prepared in the same way with water and acrylic resin, but without TiO2.

**Table 1.** Formulation of semi-transparent coatings. STC: semi-transparent coating.

The inoculation suspension (of Section 2.1) was diluted to make the concentration of inoculum 8 × 104 to 2 × 105 CFU/mL. The coated cover-glasses were placed over the internal ring of the Pyrex Petri dishes shown in Figure 1. Relative humidity was maintained with 2 mL of the supersaturated saline solution (KNO3) deposited in the external ring of each dish. Then, 0.4 mL of the inoculum were instilled on each coated cover-glass, and a transparent plastic film was applied, spreading the inoculum over a surface area of 10 cm2 . The Petri dishes were then covered with a Pyrex lid (Figure 1), placed in a sterile flow hood and illuminated with an 8-W black-light bulb.

After different contact times (2 h, 4 h, 6 h), the cover-glasses were recovered with sterile pliers and placed in plastic Petri dishes for wash-out. The wash-out of bacteria cells and the following procedures for CFU counting were repeated as in Section 2.3.1.


**3. Results and Discussion**

### *3.1. Composition of HS*

The average composition of HS, listed in Table 1, was the same reported in a previous work performed with the same HS lot [5]. The major structural component of HS was lignin (40.08 wt% in oven-dry basis). Higher lignin contents, in the range 46–51.3 wt%, were reported for the same substrate [7,40,41]. Hemicelluloses accounted for 32.28% of the dry mass of HS, and were mainly constituted by xylan (22.69%), followed by uronic and acetyl substituents (overall content, 8.93%). Minor amounts of arabinan were also detected. Aydinli and Caglar (2012) [42] reported 28.9% of hemicelluloses, a value markedly higher that the 18.7% reported by Surek and Buyukkileci (2017) [7]. The glucan content of HS accounted 26.49% of the raw material, in the range reported by Aydinli and Caglar (2012) [42], and considerably higher than the 18.7% determined by Surek and Buyukkileci (2017) [7].

**Table 1.** Composition of HS expressed as g of component per 100 g of oven-dried raw material. The values are reported as the average of triplicate measurements ± standard deviation.

### *3.2. Hydrothermal Pretreatment*

The hydrothermal pretreatments were carried out under nonisothermal conditions up to reach temperatures in the range 190–225 ◦C. Once the target temperatures were reached, the reaction media were cooled immediately and filtered. The aqueous phases contained hemicellulose-derived oligomers, monosaccharides, sugar decomposition products, and acetic acid. The generation rate of these compounds depended on the severity of the autohydrolysis conditions [20,43,44], here measured by the maximal temperature.

Table 2 shows data regarding the effects of the hydrothermal pretreatments performed at diverse temperatures on both the solid yield and the composition of liquid phases. The solid yield decreased steadily with temperature, a trend that was more marked up to 210 ◦C. This behavior can be explained because increasing temperatures promote the progressive solubilization of xylan and acetyl and uronic groups in hemicelluloses. Although arabinan also makes part of hemicelluloses, its practical importance is limited owing to the low contents. The progressive removal of hemicelluloses from the raw material led to the production of AS with increased glucan and lignin content. At the highest temperature assayed (225 ◦C), the content of both lignin and glucan accounted for 93.13% of AS. The glucan and lignin contents of AS varied in the ranges of 25.47%–38.70% and 41.08%–54.43%, respectively, corresponding to recovery yields of glucan and Klason lignin in solid phase higher than 90% and 85%, respectively. These results are favorable for the subsequent processing of AS by other methods, thus enabling the integral valorization of the raw material [2,13,20].

**Table 2.** Solid yields (measured as g of oven-dry AS/100 g of oven-dry HS) and composition of AS (measured as g component/100 g of oven-dry AS) obtained in experiments performed up to the desired temperatures. The results are reported as the average of triplicate measurements ± standard deviation.

Table 3 lists the results determined for the composition of the reaction media in the same set of experiments. When the hydrothermal pretreatments were carried out at temperatures below 210 ◦C, the concentration of nonvolatile compounds (NVC) increased up to 26.46 g/L. This concentration remained fairly constant in the range 210–220 ◦C, and dropped at the highest temperature assayed. The data are expressed in terms of the identified NVC (INVC), calculated as the joint contributions of OS and monosaccharides. XOS, the most abundant components in AL, reached their highest concentration (16.24 g/L, accounting for 73.67% of the xylan present in the feedstock) at 210 ◦C. Harsher conditions resulted in decreased XOS concentrations, owing to the generation of xylose (and furfural under the most severe conditions assayed). Arabinooligosaccharides (ArOS) were only found (in little concentrations) in assays performed under mild conditions. The corresponding monomer (arabinose) reached its highest concentration (0.45 g/L) at 210 ◦C. Glucooligosaccharides (GOS) and glucose also reached limited concentrations, revealing the solubilization of a small fraction of glucan under the most severe conditions. Concerning the substituents, the concentration of acetyl groups (AG) bound to OS reached concentrations up to 3.55 g/L at 220 ◦C (corresponding to 77.05% of the amount present in the feedstock), whereas the maximal concentrations of uronic groups linked to OS (U) were found at a milder temperature (200 ◦C). From the results shown in Table 3, it can be calculated that the maximum concentration of substituted OS (including GOS, XOS, ArOS, AG, and U) was achieved at 210 ◦C, and reached 20.49 g/L. The concentrations of monosaccharides (maximum value, 2.68 g/L achieved at 220 ◦C) were comparatively low.

**Table 3.** Concentrations of products present in the liquid phase of hydrothermal treatments performed at temperatures ranging from 190 to 225 ◦C. Data are expressed in g/L.

GOS: glucooligosaccharides; XOS: xylooligosaccharides; ArOS: arabinooligosaccharides; AG, acetyl groups linked to oligosaccharides; U: uronic acid linked to oligosaccharides; HMF: hydroxymethylfurfural. NVC: total nonvolatile compounds; INVC: identified nonvolatile components; VC: volatile compounds. \* Others: measured as the difference of the total NVC and the INVC.

### *Agronomy* **2020**, *10*, 760

The concentrations of volatile compounds (VC) increased smoothly up to 210 ◦C, and then increased markedly as a result of reactions taking place under severe conditions (for example, cleavage of AG into acetic acid and furfural generation from pentoses).

### *3.3. Refining of Oligosaccharides by Membrane Treatment*

Based on the results discussed above, 210 ◦C was selected as the optimal temperature because this experiment (denoted AL210) led to the highest concentration of substituted OS. However, unwanted compounds (including monosaccharides or nonsaccharide compounds) were also present in the liquid phase, and could limit the application of the NVC fraction in the food, cosmetic, or pharmaceutical industries. OS refining of can be achieved using a number of separation techniques, including solvent extraction [21,45], adsorption [23,33], chromatography [45], and membrane filtration [21,23,25–27]. Membrane processing is currently seen as the most promising technology for the industrial manufacture of high purity, owing to the low energy requirements, easy manipulation of operational variables, and relatively easy scale up [25,45].

Figure 2 shows the scheme of the purification method used in this work, including data regarding the chemical characterization of streams and material balances. The solution AL210 (content of total OS, 20.49 g/L, accounting for 77.41% of the NVC) was employed as a feed. This stream also contained some unwanted components that should be removed, including monosaccharides (2.01 g/L), ONVC (3.97 g/L), and VC (1.11 g/L). The feed solution was refined by DD, which led to a retentate (containing 17.44 g NVC/L) and permeate (NVC concentration, 4.02 g/L). As expected, the retentate showed an increased proportion of nonvolatile solutes corresponding to OS (90.87 g/100 g of NVC, in comparison to 77.41 g OS/100 g of NVC determined for the feed solution AL210). This finding confirmed the suitability of membrane processing for OS purification, keeping a good balance between the concentrations of the target products in retentate (15.85 g/L) and in permeate (1.76 g/L). It can be noted that most unwanted compounds were present in permeate (i.e., 1.63 g ONVC/L and 0.63 g monosaccharides/L).

The available data allow the comparison of the molar ratios of oligomer components (XOS:AG:U) between the feed and the retentate (1:0.47:0.05 and 1:0.43:0.04, respectively). The molar ratio XOS:AG was slightly higher than the one reported in literature for autohydrolysis liquors from hazelnut shells [7]. A comparative molar ratio XOS:AG of 1:0.56 was also reported for membrane processing of autohydrolysis liquors from peanut shells [24].

AL210 also contained VC (1.11 g/L), a fraction mainly made up of acetic acid (0.90 g/L) and minor amounts of HMF and furfural. The VC concentration decreased considerably in the retentate (0.29 g/L) relative to the feed content.

From the above data, it can be concluded that DD of AL210 allowed a selective recovery of substituted OS in retentate, in which the target products accounted for 90.87% of the NVC fraction, while most ONVC, monosaccharides, and VC were removed in the permeate. This finding is in agreement with the results reported in literature [24], with an increasing purity of oligomers from autohydrolysis liquors of peanut shells from 55.70% up to 72.4% using DD. In a related study, Singh et al. (2019) [26] obtained XOS of low degree of polymerization from almond shells using enzymatic treatments and membrane assisted refining. To our knowledge, no previous studies reported on the membrane refining of HS-derived OS.

**Figure 2.** Diagram of membrane processing and composition of the involved streams: AL210, retentate, and permeate. *Agronomy* **2020**, *10*, 760


1. **The total temporal extent of the stack must be short**. The first consequence of reducing the number of images in the stack is to reduce the effect of terrain deformations. In the model of the interferometric phase (Eq. 2), a term due to the presence of ground deformation is present. With an average subsidence rate of 10–20 mm/year, if the stack is kept short, the impact of deformation on the interferometric phase is minimal. Considering 8 images with a total temporal extent of about 50 days (in the case of Sentinel-1), the error will be less than 2 mm. This bias in the estimate is tolerable for our purposes. Notice that these considerations are valid just in the presence of a small-medium subsidence rate, not for extreme deformations or earthquakes where the subsidence can be in the order of several tens of cm.

The stack temporal extent also needs to consider the average "life" of a distributed scatterer. As already pointed out, we exploit all possible interferograms with *N* images with Phase Linking. If the coherence of the interferograms with a very long temporal baseline is very low, they will bring noise into the final estimate.

2. **The number of independent samples used to estimate the covariance matrix must be carefully selected.** The interferometric phase's quality is tightly related to the number of independent samples used to estimate the covariance matrix. We found that with roughly 600 independent samples, the standard deviation of the phase always remains below 1 mm. Sentinel-1 shows a nominal resolution of 5 × 20 m2 in IW acquisition mode in range and azimuth, respectively. The images are over-sampled at about 2*.*5 × 15 m2. To obtain a number of independent looks equal to 600, we need roughly 3000 pixels. We choose a 149 × 25 window spanning 375 × 375 m2. This last figure is the resolution of our APS maps which is well below the OSCAR requirements.

Once the interferometric phases have been derived, they are unwrapped: phase unwrapping is a well-known technique and will not be discussed in this contribution.

#### *4.3 GNSS Calibration*

In the interferometric phase model described in Sect. 2, the term ψorbit represents the so-called Orbital Phase Screen (OPS). This component arises due to the inaccurate knowledge of the satellite's orbit during the acquisition of each image. The OPS manifests as a phase plane (i.e., a trend) in the interferogram. The most common solution in literature is simply fitting and removing a plane in the interferometric phase. However, this process could corrupt the estimated APS's low-frequency components.

In this contribution, we developed a novel technique based on a network of GNSS stations. The workflow extract from SAR data the value of the APS over each station. The ZTD provided by each station is differentiated in time to form the differential ZTD (dZTD) that is then projected in the slant range using the SAR looks angle, obtaining a GNSS-derived equivalent APS.We recall now that the SAR interferogram contains both the APS and the OPS. If we remove from the SAR interferogram the GNSS-based APS, what is left is just the orbital plane.

Only then it is possible to fit a plane and estimate the OPS without the risk of filtering the signal of interest. Moreover, if the plane is calculated in radar coordinates, the parameters of the plane equation are related to physical quantities of the SAR acquisition, namely the normal baseline error and the derivative of the parallel baseline error. Once the parameters have been estimated, the forward problem can be computed for each pixel in the interferogram grid leading to a set of calibrated APS.

#### *4.4 Transforming the Maps from Differential to Absolute*

The last step before the ingestion into NWPM in the so-called *absolutization* process [6]. All the maps are differential with respect to a common but unknown reference atmosphere. The simplest and most effective solution is to provide an a-priori map to be summed to each interferogram. One of the sources of the prior could be a NWPM itself. It is sufficient to sum a single absolute ZTD map to all the phase-linked interferograms to obtain a stack of absolute ZTDs (see Eq. 1). We remark again that a NWPM or a GNSS network can provide this prior. In this latter case, the absolute ZTD must be interpolated over the whole SAR grid.

#### **5 Results with Sentinel-1 Data**

In this section we provide some results using real data acquired with the constellation Sentinel-1 (S1) over South Africa. The data, composed by five S1 frames covers an area of 250 × 850 km2. The footprint is depicted in Fig. 3. The area shows also a

**Fig. 3** The five Sentinel-1 frames used lead to a 250 × 850 km2 interferogram

very poor mean coherence meaning that the scene is very unstable due to temporal decorrelation making it hard to obtain a reliable estimate with traditional InSAR phase estimators.

In Fig. 3 we depicted also a single APS maps. Five images form the stack, but for brevity, we decided to depict just one of them. The visible holes are areas where the phase estimation is unreliable such as water bodies or dense forests with severe temporal decorrelation. Spatial variograms and spatial spectra have been computed to characterize the maps derived statistically.

In Fig. 4a the spatial variogram is depicted. In black dashed lines, all the variograms for the five images are represented, in blue the average variogram, while in red and green the 2*/*3 power law and 5*/*3 power laws. From the figure, it is evident that the data follow on average the theoretical model proving its ability to capture the APS dynamics. The same fit can be found for radially average spectra. In Fig. 4b the results are shown: the derived spectra fit well the theoretical model provided for the turbulent part of the APS. The model is a 1*/ f* α power law where α = 5*/*3 for radially averaged power spectra [4].

We also validated the maps with an external source. In our case, we used freely available data from the Generic Atmospheric Correction Online Service (GACOS). It can provide absolute ZTD and differential APS derived by NWPM to correct atmospheric artifacts in InSAR products. In our study we did not use these datasets to *correct* the APS, but to *validate* it. In Fig. 5 one comparison is depicted where we

**Fig. 4 a** Variogram of the APS in the South African case study. The data follows the theoretical model. **b** Average power spectra of the data. Also in this case the theoretical model is respected

**Fig. 5** Comparison between GACOS and one of the estimated APS maps. The bias between the two is roughly 1 cm, with a standard deviation around 2 cm

can see from the histogram that the error is slightly more than 1 cm in the mean and with a standard deviation of roughly 2 cm.

#### **6 Conclusion**

In this contribution, we presented a novel way to estimate Atmospheric Phase Screens (APS) from a stack of SAR data. The method exploits both Permanent and Distributed Scatterers (PS,DS): this feature allows the generation of wide and dense maps that can be ingested into NWPM to improve weather forecast. The algorithm has been extensively described in this chapter, its working principle is detailed, and results using real data acquired by the European constellation Sentinel-1 are presented. The validation has been carried out using a statistical and theoretical analysis of the variograms and spectra of the maps and by comparing them with a NWPM (GACOS). In both cases, the accordance is very high, showing the capability of the proposed method to capture most of the dynamics of the atmosphere.

#### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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## **How Deep Learning Can Help Solving Geophysical Inverse Problems**

**Francesco Picetti**

**Abstract** This brief summarizes some of the main results I obtained during my Ph.D. studies at Politecnico di Milano, under the supervision of Professor Stefano Tubaro. The thesis provides contributions to understanding the advantages, and limitations, of data-driven deep learning approaches to geophysical inverse problems, with a special focus on Convolutional Neural Networks (CNNs). Exploration Geophysics aims at estimating accurate physical properties of the Earth subsurface from seismic data acquired close to the surface. Seismic data show a great variety of statistically relevant and independent patterns. I devise Deep Learning methods to solve several geophysical tasks by learning such patterns. First, I devise generative networks as a post-processing operator for refining reflectivity images. When trained on pure image datasets, these networks suffer from the lack of physical knowledge. Then, I show a different approach named Deep Priors, which are CNNs that precondition the inverse problem. In particular, I develop a scheme to interpolate seismic data. Finally, I leverage the features extraction ability of CNNs for buried landmine detection on Ground Penetrating Radar (GPR) acquisitions. While the presented methods are effective compared to the state of the art, improvements can be achieved by integrating pure data-driven algorithms within general inverse problems theory through a-priori information derived from domain knowledge.

**Keywords** Inverse problems · Deep learning · Deep priors · Geophysics

#### **1 Introduction**

During my doctoral studies, I mainly focused on reflection seismology problems. As the name suggests, reflection seismology processes seismic waves reflected by the subsurface features like faults, sediment layers, and rock bodies. Intrinsically, reflection seismology is a collection of inverse problems [4, 18]. Additionally, given

F. Picetti (B)

Dipartimento di Elettronica, Politecnico di Milano, Informazione e Bioingegneria via Ponzio 34/5, 20133 Milano, Italy e-mail<EMAIL_ADDRESS>
© The Author(s) 2023

C. G. Riva (ed.), *Special Topics in Information Technology*,

PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-031-15374-7\_12

the dimensionality of the datasets, the costs related to acquisitions and processing, and the scarcity of ground truth, its tasks are often hard to solve and thus very challenging.

Advanced problems are ill-posed, which means that the solution is not unique, and are ill-conditioned, which means that small errors in the data result in dramatic changes in the solution [19]. These aspects and computational issues make the inverse problem theory long to be considered fulfilled.

For excellent physical reasons, seismic spectra imply that single-experiment data are insufficient for imaging purposes. This observation explains why seismic surveys are multi experiment [18]. The redundant data and the nonlinear nature of the modeling functions motivated the study of supervised deep learning imaging techniques. Although it is strongly based on statistical methods, machine learning has the particular focus on *making decisions*in an automated manner. In other words, it intrinsically aims at merging data processing with human interpretation. In exploration geophysics, solutions to inverse problems must fulfill some physical constraints, as they must be interpreted. In this sense, geophysicists are studying how to embed ML-generated features into traditional inversion schemes. Vice versa, physical constraints have been applied to machine learning scheme for lithofacies segmentation [11], data processing [14] and inversion problems [5]. Additionally, in [6] human interpretation methods are integrated into a CNN scheme.

Generative models were also proposed to precondition the inversion, under the assumption that CNNs can extract high-level features from data. This is the case of the so-called *Deep Prior* paradigm, initially developed for super-resolution, denoising, and inpainting of natural images [21].

In the light of these considerations, my research focused on understanding the advantages, and drawbacks, of machine learning methods for addressing geophysical inverse problems. To this end, the present brief is organized as follows. In Sect. 2, I first introduce a seismic imaging technique, the Reverse Time Migration (RTM), whose least-squares solution I address by means of generative networks [15]. Such architectures are a powerful tool for enhancing seismic images, but they lack physical information. Thus, I propose to change paradigm. In Sect. 3 I develop a Deep Prior interpolation scheme for seismic data [12]. In Sect. 4 I deploy convolutional autoencoders to detect buried landmine on radar data [3]; a CNN learns the secure soil features, thus failing to reconstruct signatures from possible threats. Finally, I draw my conclusions and devise a pathway for future research.

#### **2 RTM Image Enhancement Through GANs**

Reverse Time Migration (RTM) is a wave-equation-based imaging technique that represents the state-of-the-art industrial technology for depth imaging [23]. The goal of RTM is to image the reflectivity of the Earth subsurface from reflection data [2]:

$$\mathbf{d} = F\left(\mathbf{m}\right) + \mathbf{n},\tag{1}$$

where **d** are the observed scattered data, **m** is the reflectivity model, *F* is a forward modeling operator and **n** is an additive noise.

Usually, the reflectivity image is computed as:

$$\mathbf{m} = \mathbf{B}^{\prime}\mathbf{d},\tag{2}$$

where**B**is the Born linear approximation of *F* that leverages finite difference Green's functions, and represents the adjoint operator (i.e., the conjugate transpose).

To overcome limitations such as spatial aliasing, limited aperture, and nonuniform illumination, Least-Squares Reverse Time Migration (LS-RTM) was proposed to invert the forward modeling operator through iterative algorithms [17] in a least-squares sense:

$$
\hat{\mathbf{m}} = \operatorname\*{arg\,min}\_{\mathbf{m}} \|\mathbf{d} - \mathbf{Bm}\|\_2^2 + \lambda R\left(\mathbf{m}\right),
\tag{3}
$$

where *R* is a proper regularizing operator that imposes a-priori information or desired features on the resulting solution (e.g., it can enforce solution sparsity, smoothness, etc., depending on the result on the desired goal).

#### *2.1 Generative Adversarial Networks*

We can split the least-squares problem into two different problems: a standard imaging operator followed by a post-processing operator. In particular, we can invert the model-data relationship (1) as

$$F^{-1} = \mathcal{G} \circ \mathcal{B}',\tag{4}$$

where *G* is a mapping operator (i.e., a post-processing machine) that transforms the migrated **m** = **B**- **d** into the desired image **m**¯ .

I propose to use a convolutional neural network (CNN) as a general-purpose postprocessing tool after RTM to produce images as if more sophisticated algorithms generated them. Specifically, I devise a Generative Adversarial Network (GAN), which is a class of CNNs that have been proposed in [8] to learn deep representations using a small training dataset. To achieve the goal of a realistic generation, they are trained singularly.

In supervised training, the generator*G* minimizes a distance between the generated and reference images. In GANs, the generator is flanked by a fellow *discriminator D*, which is a binary classifier trained to discriminate whether its input is a pristine image or generated by its mate. At the same time, *G* is trained to obtain the desired output from a given input and fool *D*. Therefore, *D* can be seen as a regularizer driving *G* to output realistic images. More formally, we can write the adversarial loss as the sum of two components. **m** and **m**¯ being the input and the ground truth vectors, respectively, the first term, referred to as the *generator loss*, is often defined as the -2-norm of the error introduced by the generator:

$$\mathcal{L}\_{\mathcal{G}} = \|\mathcal{G}(\mathbf{m}) - \mathbf{\bar{m}}\|\_{2}^{2},\tag{5}$$

it forces the generated model to be similar to the target. As second term, we define the *discriminator loss* starting from a query image **m**ˆ as

$$\mathcal{L}\_{\mathcal{D}} = \log \mathcal{D}(\hat{\mathbf{m}}, \bar{\mathbf{m}}) + \log \left( 1 - \mathcal{D}(\hat{\mathbf{m}}, \mathcal{G}(\mathbf{m})) \right), \tag{6}$$

which measures how likely the generator is able to fool the discriminator in terms of binary cross-entropy. Finally, these two terms are added together to form the GAN loss:

$$
\mathcal{L} = \mathcal{L}\_{\mathcal{G}} + \lambda \mathcal{L}\_{\mathcal{D}},
\tag{7}
$$

where the parameter λ balances the two contributions.

#### *2.2 Experimental Results*

Let us consider a fast track project: we desire high quality migrated images, but we have no resources to perform RTM over the entire data. We could perform a fine migration over a subset of the available dataset, and a faster migration (e.g., with subsampled data) over the whole dataset. Then, we train a GAN to refine the coarsely migrated images, and finally, we can deploy it over the entire coarse dataset.

Input images **m** have been generated by migrating 10 equispaced sources and 80 equispaced receivers covering the whole acquisition surface. The target images **m**¯ have been migrated from 200 sources and 800 receivers.

The training was performed for 300 epochs; nonetheless, the net converged after 50 epochs approximately. The parameter λ was set to 0.1. Once the training is completed, the time required to create an output image of the network was about 2 min, almost entirely dedicated to migration with the coarse geometry, while a fine geometry would require 40 min.

It is interesting that results are pretty promising on the test image, showing that the proposed solution does not simply overfit the training set but can generalize. Nevertheless, a few details are lost on the test set. It is worth noticing a channel around the center of the target patch shown in Fig. 1, which is missing in the output and barely present in the coarse input. To increase the quality of the generated images, we would like to add physical information to the CNN training.

**Fig. 1** Input **m** (**a**), output **m**ˆ (**b**) and desired **m**¯ (**c**) patches from the test set

#### **3 Seismic Data Interpolation Through Deep Priors**

Let us consider an ideal densely and regularly sampled three-dimensional cube of seismic data. This data cube represents the true model we aim at estimating through interpolation. Without loss of generality, we can represent ideal data as a vector **m**¯ . We can think of the observed seismic data **d** as generated by a linear sampling operator **S** applied to **m**¯ . Therefore, we can define the interpolation problem as the inversion of

$$\mathbf{d} = \mathbf{S}\bar{\mathbf{n}}.\tag{8}$$

Within the Deep Prior paradigm [21], the standard inverse problem cost function

$$J(\mathbf{m}) = \|\mathbf{S}\mathbf{m} - \mathbf{d}\|\_2^2 \tag{9}$$

is recast as finding the set of parameters *θ* which minimizes

$$J(\theta) = \|\mathbf{S}f\_{\theta}(\mathbf{z}) - \mathbf{d}\|\_{2}^{2},\tag{10}$$

where **z** is a noise realization and *θ* the parameters of the CNN *fθ*.

Once the optimum set of parameters *θ*∗ has been computed, the inverted model is simply the output of such optimized network:

$$
\hat{\mathbf{m}} = f\_{\theta^\*} \left( \mathbf{z} \right) . \tag{11}
$$

Figure 2 helps visualizing the deep prior scheme adopted for interpolation. The convolutional autoencoder *fθ* is optimized by computing the loss function over the actual known traces only; no ground truth is required. As a byproduct, it also generates missing traces.

In the inversion process, the CNN implicitly assumes the role of prior information that exploits correlations in the data to learn their inner structure. Therefore, choosing a specific CNN architecture is critical for a suitable and well-performing solution.

**Fig. 2** Seismic data interpolation in the Deep Prior paradigm

#### *3.1 Experimental Results*

In this example, the desired shot gather is obtained via FD acoustic propagation over a 3D extension of the Marmousi model, depicted in the top-right pane of Fig. 3. It comprises 175 crosslines and 100 inlines; the sampling interval is 10m. The wavelet is a Ricker centered in 15 Hz; the recording time is 3.5 s. The top-left pane shows the input decimated data, obtained by randomly deleting 90% of the traces. Interpolation is performed with a modified version of the Multi-Resolution U-Net architecture [10], employing 2D kernels (thus processing slices of the volume) and 3D kernels. The interpolation results are shown in the bottom left and right panes, respectively. The 3D convolutions are the right choice to extract relevant information from the decimated volume; however, the computational cost is much higher than the 2D case.

#### *3.2 Tackle Aliasing with Event Dips*

Deep Priors have shown effective reconstruction performance when resolving irregular sampling. However, such techniques do not optimally perform when facing aliased data. A viable way to improve the performance is to drive the solution according to the events slopes.

Specifically, I first estimate a low-pass version of the data considering as

$$J\left(\theta\right) = \left\|\mathbf{S}f\_{\theta}\left(\mathbf{z}\right) - \mathbf{L}\mathbf{d}\right\|\_{2}^{2},\tag{12}$$

**L**being a second order Butterworth low-pass filter applied trace-by-trace, thus removing alias effects that could badly impact on standard Deep Prior inversion. Then, I estimate the slope angles *φ* through the structure tensor algorithm [22], to build

**Fig. 3** Deep Priors interpolation of pre-stack seismic data. Top: input coarse data and desired dense data; bottom: interpolated data through 2D and 3D kernels, respectively

the directional Laplacian operator −→∇ *φ* [9]. The latter is employed to regularize the inversion of the full-band seismic data:

$$J(\theta) = \|\mathbf{S}f\_{\theta}(\mathbf{z}) - \mathbf{d}\|\_{2}^{2} + \varepsilon \mathbf{w}^{2} \left\| \overrightarrow{\nabla}\_{\phi} f\_{\theta}(\mathbf{z}) \right\|\_{2}^{2},\tag{13}$$

where **w** is the vector that collects the anisotropy of the estimated gradient square tensor for each data sample [22], and it can be interpreted as a confidence measure of the estimated slopes. Notice that, as the optimization goes on, the interpolated data can be used to produce a better estimate of the data slopes, updating −→∇ *φ* at runtime.

I compare the interpolation results with and without the proposed regularizer on a synthetic dataset of 4 linear events with different dips. Three events are mildly steep, while the fourth event has been designed to produce aliasing effects. This example is simple yet particularly challenging due to the slope of the events. The

**Fig. 4** Interpolation results on synthetic linear events: input decimated data, target complete data, output of Deep Prior optimization with and without the proposed regularization. Above, the data; below, the corresponding FK spectra

desired data are depicted in the second column of Fig. 4. I build a regular binary mask for simulating the decimation by keeping one trace over 3, as depicted in the first column. The network is optimized for 3000 iterations to fit the low-passed data (12). Then, the slopes are estimated, and the regularized optimization is performed for 7000 iterations with ε = 5.

The results of Deep Prior optimization when minimizing the regularized (13) and the standard data fitting (10) are reported in the third and fourth columns, respectively. Standard Deep Prior optimization could not resolve the aliasing of the steeper event, while the regularized results are more homogeneous. This effect is evident also in the spectra depicted in the fourth column, where we can notice a residual aliasing pattern; it has been mitigated by regularization.

#### **4 CNN Landmine Detection**

Landmines have been massively deployed in a vast amount of countries all over the world. According to the United Nations, the 99.6% of mines and unexploded ordnance must be safely removed from an area to consider it landmine-free. The majority of modern landmines are mainly composed of plastic materials, significantly reducing the efficacy of metal detectors. A suitable alternative is the Ground Penetrating Radar (GPR), as it is capable of detecting even small dielectric changes in the subsurface.

The 2D image **V**(*t*, *x*) whose coordinates are the reflection time *t* and the *inline* axis *x* is called B-scan. By concatenating *Y* consecutive B-scans, we sample the soil also in the *crossline* axis *y*, thus obtaining the volume **V**(*t*, *x*, *y*). If the *y*-th B-scan has been acquired over a buried target, we associate the binary label *l*(*y*) = 1; if it has been acquired over a target-free area, we label it with *l*(*y*) = 0. The buried

$$\underbrace{\cdots\cdots\cdots\cdots}\_{\bullet\text{ \text{\textquotedblleft}\dots\text{\textquotedblright}\dots}}\underbrace{\varepsilon\cdots\cdots\varepsilon}\_{\bullet\text{ \textquotedblleft}\dots\cdots\cdots}\underbrace{\varepsilon\cdots\varepsilon}\_{\bullet\text{ \textquotedblleft}\dots\cdots\cdots}$$

**Fig. 5** The proposed anomaly detection scheme

object detection problem we aim at solving consists in taking the volume **V**(*t*, *x*, *y*) as input, and producing a label ˆ*l*(*y*) for each B-scan.

Instead of a naive supervised binary classification scheme, let us train a convolutional autoencoder on patches of mine-free B-scans by minimizing the MSE between the input volume **v***i* and the autoencoder output **v**ˆ*i* . Once the training has converged, we can state that the CNN has learned how to reconstruct background soil information correctly. When a volume of soil **V** is to be analyzed, it is split into sub-blocks **v***i* and the anomaly metrics depicted in Fig. 5 is computed as:

$$e\_i = \left\| \mathcal{E}(\mathbf{v}\_i) - \mathcal{E}(\mathcal{D}(\mathcal{E}(\mathbf{v}\_i))) \right\|\_2^2. \tag{14}$$

Then, all *ei* values are merged into a volumetric anomaly mask **E** of the same size of **V**. Finally, the label ˆ*l*(*y*) is computed by hard-thresholding the maximum value of **E**(*t*, *x*, *y*) with a global value Γ .

#### *4.1 Experimental Results*

To compare against other methods proposed in the literature, I consider a simple detector that uses the maximum value of the B-scan energy, a standard constant false alarm rate (CFAR) technique [1], a model-based method [7], a computer vision approach [20] and two CNN-based methods [13, 16]. I evaluated my technique employing Receiver Operating Characteristic (ROC) curve, which represents the probability of correct detection (i.e., correctly finding a threat) and probability of false detection (i.e., detecting objects in clear areas) by spanning all possible values of the threshold . Each point of a ROC curve is obtained by comparing the ground truth *l*(*y*) with ˆ*l*(*y*) binarized with respect to threshold . In a real scenario, traces generated by a single landmine can be observed in multiple B-scans. We consider a misdetection every time we miss a single B-scan. Therefore all presented results can then be considered a lower bound on the performance of the proposed method.

As depicted in Fig. 6, the proposed anomaly detector achieves the best Area Under the Curve (AUC) score. It is worth noticing that, along with the method proposed by my colleagues in [13], the proposed autoencoder is able to achieve a TPRFPR=0 near 0.9, which is highly desirable in a demining system.

#### **5 Conclusions**

Convolutional Neural Networks (CNNs) are an uprising tool to leverage data patterns, especially when acquisition campaigns produce redundant datasets as in Exploration Geophysics. In many physical applications, however, the data features extracted by convolutional layers are not sufficient to describe the complex phenomena that we aim to invert. This consideration suggests that a-priori domain information should also be inserted in machine learning schemes.

In this brief, I presented three different machine learning paradigms applied to imaging, interpolation, and interpretation tasks. First, I study the Generative Adversarial Networks (GANs) as a tool to refine Reverse Time Migration (RTM) images. This supervised network produces very good results but it lacks of physical information on the geological features it processes. Then, I described how Deep Priors could be relevant to seismic data interpolation. While producing state-of-the-art results, this method cannot solve the intrinsic spectral incompleteness of seismic traces, especially in the presence of aliased data, for which I proposed a slope-informed regularization term. Finally, I show how learning patterns can be employed in an anomaly detection scheme for spotting landmines in Ground Penetrating Radar (GPR) data. The learning ability of a CNN is leveraged to discriminate safe background soil images and buried threats signatures. Such a scheme is based on a single class training, thus relaxing the requirements, and costs, of collecting datasets.

In the last years, a considerable number of architectures have been proposed for improving human-level tasks. A general trend is to go for deeper, heavier, and more complex ensemble models. It is worth noticing that this race does not guarantee better results. Therefore, extra care should be spent studying new problem-setting approaches rather than problem-solving ones.

#### **References**

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