in general a processing method is disclosed for self - consistently treating multi - measurement nmr data to derive relatively unbiased multi - dimensional distributions which can be used for determining fluid nmr response parameters or as a basis for direct petrophysical interpretation . the maximum entropy principle ( mep ) method for processing multi - dimensional data overcomes the limitation of separable kernels and handles regularization in a simple systematic manner without the need for any user input , regardless of the noise levels of the data or the nature of the underlying distributions . it provides a simple graphical representation of the data that can be used to identify fluid responses in all environments . the graphical representations ( i . e . multi - dimensional distributions ) can themselves be used directly for interpretation or alternatively they may be used to guide the selection of parameters for model - based processing such as mrf . it is important to recognize that the mep technique as well as the methods to interpret d - t2 maps are applicable to cpmg ( carr , purcell , meiboom , and gill ) and de ( diffusion editing ) measurements , or any other nmr measurement scheme that responds to spin relaxation rates , molecular diffusion and combinations of these properties . the response , a , from a three - dimensional measurement can be expressed as a ⁡ ( τ 1 , τ 2 , τ 3 ) = ∑ i ⁢ ⁢ ∑ j ⁢ ⁢ ∑ k ⁢ ⁢ f ⁡ ( i , j , k ) ⁢ h ⁡ ( τ 1 , τ 2 , τ 3 ; i , j , k ) + δ ⁡ ( τ 1 , τ 2 , τ 3 ) ( 1 ) where τ 1 , τ 2 , τ 3 , are three acquisition parameters which define a particular measurement , f ( i , j , k ) is the amplitude of the component i , j , k in a 3 - dimensional distribution ( i . e . each dimension corresponds to a particular property ), h ( τ 1 , τ 2 , τ 3 ; j , k ) is the response of that component to the measurement specified by parameters τ 1 , τ 2 , τ 3 , and δ is a random noise term . note that the number of dimensions in the distribution need not be the same as the number of dimensions in the acquisition . to place eq . ( 1 ) in context , τ 1 , τ 2 and τ 3 could , for example , be wait time ( wt ), inter - echo spacing ( te ) and time ( t ) in a cpmg measurement . similarly , index i might refer to a particular t2 value , t2 ( i ), j may refer to a diffusion rate , d ( j ), and k might correspond to a particular t1 value or t1 / t2 ratio , r12 ( k ). the solution of eq . ( 1 ) consists of determining f ( i , j , k ) given a series of measurements , a ( τ 1 , τ 2 , τ 3 ), assuming that the form of h ( τ 1 , τ 2 , τ 3 ; i , j , k ) is known . at first this may appear to be straightforward since we typically acquire several thousand echoes in a multi - measurement nmr sequence , whereas the distribution might be adequately defined by , say , 20 × 20 × 10 = 4000 components . in other words , the number of measurements ( i . e . echo amplitudes ) is comparable to or more than the number distribution components . in reality , the inversion described by eq . ( 1 ) is grossly under - determined because the response kernels h ( τ 1 , τ 2 , τ 3 ; i , j , k ) are substantially linearly dependent . in fact , even comprehensive multi - measurement data with good signal - to - noise can often be described by as few as 10 parameters , implying that the data could be compressed to just 10 independent components without any significant loss of information . the problem of deriving extensive distributions from limited datasets arises in many areas of science . one interesting and relevant example of this is the determination of molecular distribution functions . in this case , a small number ( sometimes just one ) of measured moments , { overscore ( m )} n , of a distribution may be available , from which an entire , distribution is to be calculated . information theory ( e . t . jaynes , phys . rev . 106 , 620 ( 1957 )) states that in such a situation , the “ most probable ” distribution is f ⁡ ( x i ) = z - 1 ⁢ exp ( ∑ n ⁢ ⁢ α n ⁢ m n ⁡ ( x i ) ) ( 2 ) where z is a normalization function and m n ( x i ) are functions whose expectation values define the moments , m _ n = ∑ i ⁢ ⁢ m n ⁡ ( x i ) ⁢ f ⁡ ( x i ) ( 3 ) and α n are parameters which are adjusted such that the moments computed using eqs . ( 2 ) and ( 3 ) agree with the measured values . the number of parameters , α n , used to fit the data should not exceed the number of measured moments . unlike conventional inversion algorithms commonly used for deriving t2 distributions , this scheme does not require any explicit regularization . this approach provides the distribution which is simultaneously consistent with all the available data and has the maximum entropy , s , as given by s = - k ⁢ ∑ i ⁢ ⁢ ln ⁡ ( f ⁡ ( x i ) ) ⁢ f ⁡ ( x i ) ( 4 ) where k is a constant . a simple discussion of the “ maximum entropy principle ” ( mep ) in the context of distribution functions is given in a recent book — dill , k . a . and bromberg , s ., “ molecular driving forces ”, garland science publishing , ( 2003 ). a more specific derivation is also reproduced in most standard texts on statistical thermodynamics ( eg . mcclelland , b . j ., “ statistical thermodynamics ”, chapman and hall , ( 1973 )) as part of the discussion of maxwell - boltzmann statistics . in that case , the “ measured ” quantity is the energy , e , e = ∑ i ⁢ ⁢ e ⁡ ( x i ) ⁢ f ⁡ ( x i ) ( 5 ) f ( x i )= z − 1 exp (− β e ( x i )) ( 6 ) where β = 1 / kt ( t = temperature in kelvin ) and e i is the energy of state i . a good example of the practical application of mep is given by catalano , d ., di bari , l ., veracini , c ., shilstone , g . and zannoni , c ., j . chem . phys ., 94 , 3928 , ( 1991 ), who derive internal rotameric distribution functions for substituted biphenyl molecules from nmr dipolar coupling measurements . maximum entropy principles can also be applied to deriving relaxation time and diffusion rate distributions from nmr well - log data . at least one difference is its application to deriving multi - dimensional rather than one - dimensional distributions . as demonstrated herein , the disclosed mep approach is capable of treating such multi - dimensional distributions , which are otherwise expected to be grossly under - determined by the available data . the first step in applying mep is to identify a suitable set of basis functions ( i . e m n ( x i ) ) defined in the space of the distribution ( eg . in t2 - d space ), whose mean values can be measured . it is recommended ( although not strictly required ) that the basis functions be orthogonal and that they can be ranked according to the degree of “ detail ” they contain . in other words , we expect that simple distributions with a small number of broad peaks should be defined by the first few moments . one set of functions which satisfies these requirements can be obtained by singular value decomposition ( svd ) of the measurement kernel , h ( τ 1 , τ 2 , τ 3 ; i , j , k ). the svd method decomposes the kernel matrix , h , into the product of three separate matrices ( h ) = ( u ) ⁢ ( s 1 0 ⋯ 0 s n ) ⁢ ( v t ) ( 7 ) where s is a diagonal matrix whose elements , s i are the singular values of h . the columns of u and v are known as the left and right singular vectors respectively and are orthogonal , ∑ i = 1 m ⁢ ⁢ u ik ⁢ u ij = δ jk ; ∑ i = 1 n ⁢ ⁢ v ik ⁢ v ij = δ jk ; ( 8 ) in order to express h as a 2 - dimensional matrix , we define a general measurement co - ordinate , τ , ( eg . τ = wt , te ( l ), n ) and a distribution co - ordinate , x , ( eg x = t2 , d , r12 ). from eq . ( 7 ), we can now write ∑ τ ⁢ ⁢ u n t ⁡ ( τ ) ⁢ h ⁡ ( τ , x ) = s n ⁢ v n t ⁡ ( x ) = v n ⁡ ( x ) ( 9 ) where ν n are un - normalized orthogonal vectors expressed in the space ( x ) of the distribution function . combining eqs . ( 1 ) and ( 9 ), leads to the useful expression ∑ τ ⁢ ⁢ u n t ⁡ ( τ ) ⁢ a ⁡ ( τ ) = ∑ x ⁢ ⁢ v n ⁡ ( x ) ⁢ f ⁡ ( x ) + δ n = v _ n + δ n ( 10 ) since the u t n ( x ) are available from svd analysis of h , and the a ( τ ) are just the measured echo amplitudes , it is straightforward to compute the { overscore ( ν )} n . note that the second part of eq . ( 10 ) is formally identical to eq . ( 3 ). thus , to within some statistical noise , δ n , we can measure a set of moments , { overscore ( ν )} n , of the distribution function , f ( x ), which are independent of each other moment within that same set . it is known that , according to svd analysis , these moments correspond to the expectation values of a set orthogonal functions of the distribution co - ordinate , x . from the preceding discussion of the mep method , it follows that the distribution function can be expressed as f ⁡ ( x ) = z - 1 ⁢ exp ( ∑ n ⁢ ⁢ α n ⁢ v n ⁡ ( x ) ) ( 11 ) where the parameters , α n , are adjusted such that the measured moments , { overscore ( ν )} n , are reproduced ( see eq . ( 10 )). the number of components , n , included in the exponent summation on the right of eq . ( 11 ) should not be greater than the number of valid measured moments , { overscore ( ν )} n . the number of valid moments can be estimated by comparing the absolute values of the moments with their estimated standard deviations . since the moments tend to decay with increasing rank , n , it is relatively straightforward to determine at what value of n the moments become insignificant relative to noise levels . this determination of n is in contrast to inversions requiring subjective regularization that are largely indeterminate . alternatively , n may be fixed at some reasonable value determined empirically from previous data or from modeling . it should be noted that z − 1 is included to comport with accepted notation of a normalization parameter . however , according to one embodiment , the normalization parameter is omitted altogether . according to another embodiment , the normalization parameter is used by normalizing the equation to a value of one . it should further be noted that although the inversion is described in terms of a set of orthogonal functions , orthogonality is merely a result of the svd approach and is not required . an example of a non - svd analysis is the use of legendre polynomials applied to single cpmg data . this concludes the general theoretical discussion of mep with regard to deriving relaxation / diffusion distributions from nmr data . at this point it is useful to consider specific forms for kernel , h , and derive the corresponding svd vectors . for a general multi - measurement nmr acquisition , performed in a magnetic field gradient , g , the elements of h might correspond to the amplitude of a particular echo , n , measured with wait time ( wt ), and inter - echo spacing ( te ). however , for multi - measurement acquisition sequences , the total number of echoes is typically several thousand , while the distribution may be defined by several hundred components . evidently , h can become very large and cumbersome for the purposes of evaluating svd . it is therefore expedient in practice ( this is a limitation of the available processing capabilities and not of the algorithm itself ) to compress the data prior to numerical evaluation . one simple compression method is to compute “ window sums ”. each echo train is divided into sections in which the echo amplitudes are summed ( freedman u . s . pat . no . 5 , 291 , 137 ). in order to accurately represent the multi - exponential decays , window sums in the early part of an echo train generally contain small numbers of echoes and windows for latter parts of the train contain large numbers of echoes . for standard cpmg sequences acquired on the cmr tool , which has a distribution of magnetic field gradients , f ( g ), the window - sum kernel can be written as h ⁡ ( wt , te , m ⁢ : ⁢ t2 , d , r12 ) = ( 1 σ e ⁢ 1 + n2 ⁡ ( m ) - n1 ⁡ ( m ) ) ⁢ ∑ k ⁢ ⁢ f ⁡ ( g k ) ( 1 - ⅇ - wt / ( r12 × t2 ) ) × ⁢ [ ⅇ - ( n1 ⁡ ( m ) - 0 . 5 ) ⁢ te / t2 d , g , te - ⅇ - ( n2 ⁡ ( m ) + 0 . 5 ) ⁢ te / t2 d , g , te ] ⁢ t2 d , g , te te ( 12 ) ( t2 d , g , te ) - 1 = t2 - 1 + ( γ ⁢ ⁢ g ⁢ ⁢ te ) 2 ⁢ d 12 ( 13 ) where σ e is the noise per echo for the particular measurement , γ is the proton gyromagnetic ratio and n1 ( m ) and n2 ( m ) are the first and last echoes of the mth window sum . note that other types of compression ( eg . svd ) could also be applied to the data . the resulting kernel would need to be modified accordingly . one focus of the model - independent analysis is to provide an unbiased representation of the data . information contained in the multi - dimensional maps is essentially identical to that contained within the original echo amplitudes . although the maps provide a way to understand the data from an nmr perspective , they do not give a petrophysical interpretation of the results . in some cases a petrophysical interpretation may be quite straightforward from visual inspection of the maps . however , in some situations , poor resolution along t1 / t2 or diffusion axes might not be sufficient to identify separate fluids which appear with identical t2 values . in order to proceed further and derive saturations and hydrocarbon viscosities , it is necessary to apply a model to the results present multi - dimensional models ( for oil and water ) states , in part , that for each value of t2 , the diffusion constant can assume just two possible values , which correspond to diffusion of water and oil . the water diffusion constant is a known function of temperature , t , and is independent of t2 , whereas for oil , the diffusion constant is linearly proportional to t2 , in other words , present model solutions corresponds to a horizontal ( water ) and diagonal ( oil ) lines on a d vs t2 map . in standard analysis , the raw data is fit directly using the constraints of eqs . ( 14 )-( 15 ). an alternative approach is to use the maps themselves as input to derive the solution . since the information contained in the maps is essentially identical to that of the original data , the two methods of solution should be equivalent . in practice , the data is often lacking in diffusion information ( i . e . resolution ) and the problem then consists of re - assigning the amplitude spread in the diffusion axis of the d - t2 map to the different formation fluids . a simple approximate way to do this is to use the geometric mean diffusion rate for each t2 − d lm ( t2 )— computed from the maps , and redistribute the amplitude at this t2 according to the model water and oil d values . it is convenient to define an apparent water saturation at each t2 value , sx 0 ( t2 ), d lm ( t 2 )= d w ( t 2 ) sx0 ( t2 ) d o ( t 2 ) 1 − sx0 ( t2 ) ( 16 ) sx0 ⁡ ( t2 ) = ln ⁡ ( d lm ⁡ ( t2 ) / d o ⁡ ( t2 ) ) ln ⁡ ( d w ⁡ ( t2 ) / d o ⁡ ( t2 ) ) ( 17 ) separate water and oil t2 distributions , f h20 , and f oil , can now be derived , f h2o ( t 2 i )= sx 0 ( t 2 i )× f ( t 2 i ) ( 18 ) f oil ( t 2 i )=( 1 − sx 0 ( t 2 i ))× f ( t 2 i ) ( 19 ) f ⁡ ( t2 i ) = ∑ j ⁢ ⁢ ∑ k ⁢ f ⁡ ( t2 i , d j ⁢ r12 k ) ( 20 ) note that other schemes may be implemented to derive individual fluid volumes . for example , specified areas of the map associated with a particular fluid type may be integrated to derive the corresponding volumes , from which saturations can then be computed . referring to fig1 , there is shown an apparatus for investigating subsurface formations 31 traversed by a borehole 32 , which can be used in practicing embodiments of the method of the invention . an investigating apparatus or logging device 30 is suspended in the borehole 32 on an armored cable 33 , the length of which substantially determines the relative depth of the device 30 . the cable length is controlled by suitable means at the surface such as a drum and winch mechanism ( not shown ). surface equipment , represented at 7 , can be of conventional type , and can include a processor subsystem and communicates with the downhole equipment . the logging device which makes the measurements can be any suitable nuclear magnetic resonance logging device , for use in wireline logging applications as shown , or of a type that can be employed in measurement while drilling applications . the device 30 includes , for example , a means for producing a static magnetic field in the formations , and radio frequency ( rf ) antenna means for producing pulses of rf magnetic field in the formations and for receiving the spin echoes from the formations . the means for producing a static magnetic field can comprise for example a permanent magnet or magnet array , and the rf antenna means for producing pulses of rf magnetic field and receiving spin echoes from the formations can comprise for example one or more rf antennas . an embodiment of the invention utilizes a suite of measurements from an nmr logging device of a type that can be operated to obtain separate measurements from a plurality of closely spaced thin shell regions in the surrounding formations . a simplified representation of some of the components of a suitable type of logging device 30 is illustrated in fig2 . the figure shows a first centralized magnet or magnet array 36 and an rf antenna , represented at 37 , which can be a suitably oriented coil or coils . fig2 also illustrates a general representation of the type of closely spaced cylindrical thin shells , 38 - 1 , 38 - 2 . . . 38 - n , that can be frequency selected using the referenced type of multifrequency logging device . as is known in the art , for example as disclosed in u . s . pat . no . 4 , 710 , 713 , the logging device can select the shell region to be investigated by appropriately selecting the frequency of the rf energy in the transmitted pulses . in fig2 , a further magnet or magnet array is represented at 39 , and can be utilized for applying a pre - polarizing static magnetic field to formations being approached by the investigating region of the logging device as it is raised in the borehole in the direction of arrow z . reference can be made , for example , to u . s . pat . no . 5 , 055 , 788 . also see u . s . pat . no . 3 , 597 , 681 . turning now to fig3 , shown is an exemplary flow diagram of the steps according to a disclosed embodiment . beginning at step 302 , the axis values and limits are assigned . these axes will be used to ultimately generate a multi - dimensional map of the inversion results . for example , according to one embodiment , a diffusion and an t2 relaxation axis is defined over which to display the nmr data . as mentioned , other axis values may be employed , such as replacing the t2 axis with a t1 / t2 , or r12 , axis or t2 v . t1 or t2 v . r12 maps . discretization is also applied to generate an object distribution . discretization generally consists of specifying the resolution along each dimension axis . for example , in order to simplify the analysis each axis is resolved across a specified number of values along either a logarithmic or linear scale . at step 304 , a compression scheme is defined . this step is primarily made necessary due to limited computing power . the mep approach itself , however , may be applied either to compressed data or to the entire dataset . thus , step 304 would not be necessary if sufficient computing power is available . one example of a compression scheme is the above mentioned window summing . however , many other compression schemes may be used without altering the mep approach . continuing at step 306 , a response kernel is computed for each data point in the object distribution . equation 12 is one example of a response kernel equation . specifically , h ( wt , te , m : t2 , d , r12 ) is determined at each t2 , d , r12 value for every echo ( or in the case of window compression , every echo window sum ). at step 308 , corresponding functions are defined in both the measurement domain , ( wt , te , t ) and in the object distribution domain , ( t2 , d , r12 ) of equation 1 . acquisition of the nmr data is performed at step 310 . however , the timing of the acquisition can widely vary . for example , acquisition may have occurred during some prior logging run or may be currently acquired as the mep algorithm is applied during a logging run . further , acquisition can be performed by any number of nmr acquisition tools , such as a wireline tool , a logging while drilling tool , a fluid sampling tool , a portable or laboratory apparatus . in addition , the mep method is not dependent upon the acquisition sequence that is used and may be applied to , for example , data obtained in response to cpmg , cpmg modified or diffusion editing sequences . continuing at step 312 , the dataset is compressed according the compression scheme of step 304 . at step 314 , the measurement domain moments ( spin echo domain ) m j are evaluated over the nmr data , or compressed data . this step or , alternatively , a separate step , includes a determination of the number of significant moments , n . alternatively , a pre - defined set of n valid moments is set aside . higher moments may be discarded at this point . at step 316 , the n measurement domain moments m j are fit simultaneously using an optimization algorithm in which n parameters are adjusted independently . the n adjustable parameters are the weighting factors associated with each of the n functions in the object distribution domain which were defined in step 308 . the values of these n parameters , together with their corresponding functions define a complete object distribution function , according to an exponential sum expression . the final estimate for the ( multi ) dimensional distribution is that for which the n computed moments most closely match the n measured moments . according to one embodiment , a suitable optimization algorithm is shown in fig3 a . at step 320 , an initial set of n coefficients , α n , is defined . at step 322 , a distribution is computed for the set of α n coefficients according to equation 11 . a set of moments are then computed at step 324 , according to to evaluate h , it is first necessary to specify an acquisition sequence . in this example , we consider a typical mrf acquisition comprising 6 cpmg measurements . a summary of the acquisition sequence is given in table 1 . note that this sequence includes measurements with different inter - echo spacings and different wait times . the nmr response is modulated by t2 , r12 ( or t1 ) and d , so the appropriate inversion is 3 - dimensional . the limits of the distribution axes and the number of components along each axis must now be selected . sufficient numbers of components must be chosen to adequately describe each of the different responses given the number of measurements which discriminate these responses , whilst maintaining a small enough total number of components for acceptable numerical efficiency . table 2 summarizes the inversion parameters used for this example . no attempt has been made to optimize the discretization scheme . however , within reasonable bounds , the details of the discretization appear to have relatively little bearing on the final inversion results . table 2 parameters used for 3 - dimensional inversion of multi - measurement nmr data . number of dimension components min value max value spacing t2 16 0 . 5 ms 5000 ms logarithmic d 16 3 × 10 − 11 m 2 s − 1 3 × 10 − 7 m 2 s − 1 logarithmic r12 3 1 3 linear to demonstrate the processing , four examples of synthetic data are presented . the data was generated for the acquisition sequence of table 1 using 1 component ( tests 1 and 2 ) or 2 components ( tests 3 and 4 ) each with unique t2 , d and r12 values . the total amplitude of the signal was fixed at 0 . 20 v / v and a random noise of 0 . 01 v / v was added to the echo trains . simulation parameters are summarized in table 3 . results of the inversion tests are presented in fig4 - 7 . fig4 illustrates application of the mep approach as defined in test 1 . graph 402 shows the synthetic nmr spin echo data and the fit to the window sums derived from this data . as indicated by the y - axis , the nmr data is compressed using window summing . the graph 404 is generated based on the spin echo data of graph 402 after processing according to the mep process . the y - axis is defined by diffusion values and the x - axis is defined by t2 values , hence a d - t2 map . the d - t2 map is derived by summing over the third dimension ( r12 = t1 / t2 ). the peak 405 in graph 404 represents the probable existence of a fluid ( oil , water or gas ). the lower left graph 406 is the integrated t2 distribution 410 compared with the input distribution 408 . the upper right graph 412 is the integrated d distribution 416 compared with the input distribution 414 . the single peak 405 is represented accurately in both t2 and d axes . just 12 svd functions were required to achieve a good fit ( to within statistical noise levels ) to the data . fig5 illustrates the results of test 2 . this simulation is identical to test 1 except for the t1 / t2 ratio , r12 , which is increased to 2 in test 2 . the top left graph 503 is the t2 - d map derived by summing over the third dimension ( r12 = t1 / t2 ). the lower left graph 507 is the integrated t2 distribution 502 compared with the input distribution 504 . the upper right graph 509 is the integrated d distribution 506 compared with the input distribution 508 . once again , the single peak 505 is represented accurately in both t2 and d axes . a slight degradation in resolution in the d axis is observed relative to test 1 results . this may be a statistical variation due to different noise realizations in the two simulations . again , 12 svd functions were used to fit the data . fig6 compares the t1 and t2 distributions computed for tests 1 and 2 . for test 1 , which used a put value of r12 = t1 / t2 = 1 , the computed t1 and t2 distributions overlay exactly , as shown in graph 602 . in contrast for test 2 , shown in graph 604 , which used a value of r12 = t1 / t2 = 2 , the computed t1 distribution 608 is centered at ˜ 2 seconds , about a factor of 2 higher than the t2 distribution 606 . this demonstrates that the inversion is able to accurately determine t1 values . note that the range over which accurate t1 values may be determined is governed by the choice of wait times in the acquisition program . results of 2 - dimensional mep inversion of diffusion editing data 702 acquired in oil - bearing sandstone formation is shown in fig7 . shown at top left is the t2 - d map . overlaying are lines for water ( horizontal line 704 ) and dead oil ( faint diagonal line 706 ) responses . the lower left graph is the integrated t2 distribution . the upper right graph is the integrated d distribution . the diffusion editing sequence comprised 10 measurements with long echo spacings varied between 2 ms and 12 ms . a single wait time was used for all measurements , so in this case the inversion was 2 - dimensional ( ie . t2 , d ). as shown , the mep inversion accurately resolves the two fluid instances 708 and 710 in both the t2 and d domains for this diffusion editing acquisition . it is appropriate to again note , that according to an embodiment , the nmr processing can be performed independent of downhole operations once the data has been retrieved . for example , according to one embodiment , raw data may be processed downhole or transmitted to a surface processor concurrent with the borehole operations to obtain real time interpretation of the raw data . as is known , borehole operations may include while drilling operations as well as wireline operations occurring after the drill string has been removed from the borehole . according to another embodiment , processing may occur subsequent to obtaining the data . a general model - independent method , based on the maximum entropy principle ( mep ), has been developed that analyses multi - measurement nmr data governed by ( one - or multi - dimensional ) distributions of properties . the objective of the method is to present complex data in an understandable format without imposing any bias or interpretation whilst minimizing noise - related artifacts . although the technique is quite general , it is likely to be most useful in situations where model - based analysis breaks down due to deviations of nmr properties from the “ ideal ” behavior assumed in the models . the forgoing disclosure and description of the various embodiments are illustrative and explanatory thereof , and various changes to the nmr acquisition sequence , the logging process , the materials utilized in the antenna design , the inversion process and the order and timing of the steps taken , as well as in the details of the illustrated system may be made without departing from the disclosed subject matter .