INVERSION OF A VERTICAL SEISMIC PROFILE BY MINIMIZATION OF AN ENTROPY LIKE FUNCTION

Abstract: The invention relates to a method of inverting seismic traces Sk by determining the series R = {r i } representative of the seismic reflection coefficients from geological strata. The method consists: in determining an approximate solution R° by minimizing an entropy type function H(R) = Σ f(r i ) under constraints related to the upgoing wavefield Uk and the downgoing wavefield Dk; in determining a correction value ΔR by minimizing a linear function J(ΔR) under linear constraints related to the noise in the traces and to the time-depth curve extracted from the traces; and in calculating R = R°+ ΔR representative of the reflection coefficients. In a variant, it is possible to guide the selection of the approximate solution R° by taking account of information contained in one or more logs, and in determining ΔR so that the series R satisfies the stacking velocities which result from surface seismic processing.

Description: The invention relates to studying the Vertical Seismic Profile generally referred to by the abbreviation VSP, and more particularly to inverting such a profile. In general, a seismic signal received by a detector is the response of variations in acoustic impedance of geological formations to an emitted seismic wave. The acoustic impedance of a formation is defined as being the product of the density of the medium multiplied by the velocity of propagation of the seismic wave through said medium. The acquisition of a VSP consists in recording seismic signals picked up at different levels in a borehole in response to seismic excitations emitted at the solid surface of the earth. the inverse configuration can also be used, i.e. detectors may be situated on the solid surface of the Earth and used in combination with seismic excitations performed at different levels in a borehole. Studying a VSP consists, in particular, in solving the problem of inverting the seismic signals in such a manner as to extract the acoustic impedance of a formation, and thus reconstruct the acoustic impedance profile as a function of a time scale and/or a depth scale. The looked-for acoustic impedance profile corresponds mathematically to a sequence of transmission media for which the rank numbers n , the geometrical positions, and the acoustic impedance values {A i , i = 1,n}, all need defining. An equivalent way of defining the acoustic impedance profile comprises a sequence of reflection coefficients {r i , i = 1,n} placed at the interfaces between the transmission media and having values given by the formula: r i = (A i + 1 - A i )/(A i + 1 + A i ) A major difficulty encountered during inversion lies in the fact that the number of unknowns (for example the positions and the values of the reflection coefficients) is very high (typically about 1000), since each depth-representing sample is a potential candidate for being the position of a reflection coefficient, whereas the number of available equations depends on the width of the frequency spectrum of the seismic traces and, in general, is much less than the number of unknowns. Of the various known inversion techniques, particular mention should be made of the technique described by A. Bamberger et al, in an article entitled "Inversion of normal incidence seismograms" published in Geophysics (vol. 47, May 1982, pages 757-770). This technique is considered as being one of the most satisfactory from the purely mathematical point of view, however its implementation suffers from the drawback of being extremely expensive in computation time. Another known technique consists, in a first step in which a limited number of possible positions for the interfaces are selected, and then in a second step in which the value of the reflection coefficient at each of said determined positions is calculated in such a manner as to minimize the difference between the recorded trace and the synthetic trace obtained from the profile found in this way. The article entitled "Inversion of vertical seismic profiles by iterative modelling" by P. Grivelet published in Geophysics (vol. 50, June 1985, pp. 924-930) provides a good summary of the various steps of such a method. This article also emphasizes that it is desirable to reduce the number of possible solutions by imposing conditions which result from a priori knowledge of the acoustic impedance values of one or more given geological strata. It must initially be emphasized that the above-mentioned method is presently capable of inverting only one trace at a time, and this limitation may be a considerable drawback when the signal being inverted is degraded by noise. It is well known that noise is liable to introduce imaginary interfaces relating to non-existent geological strata. Further, the second step does not allow new interface positions to be re-defined if there is disagreement with the seismic equations, and is therefore prisoner to any errors that may have arisen during the first step. Finally, the only additional a priori information this type of inversion is capable of accepting is information concerning the acoustic impedance of some of the geological strata. A method for deriving reflection coefficients from seismic data is described in an article by Nahi et al entitled "Recursive derivation of reflection coefficients from noisy seismic data". IEEE International Conference on acoustics, Speech of signal processing, Tulsa Oklahoma 10-12th April 1978 p. 524-528. The method uses linear simultaneous equations in terms of reflection coefficients and the deconvolved observed data. The "Levinson" algorithm is used to recursively solve for the reflection coefficients. An inversion method of reflection seismograms is described in an article by D.W. Oldenburg et al entitled "Inversion of band-limited reflection seismograms" Proceedings of the IEEE vol 74 no. 3 March 1986 p. 487-497. The philosophy of this method is to find a model which exhibits the least amount of structural variation and still satisfies data constraints. This method inverts a stacked section of seismic reflection responses recorded at the surface and properly processed. To sum up, prior techniques are incapable of optimizing the inversion procedure by injecting the information contained in all of the recorded seismic signals, together with other information known beforehand, but different in nature from the solution being sought. The present invention starting from EP-A-0053525 (Seeman, Horowicz) seeks to provide an inversion method which makes it possible simultaneously to take account of available information concerning reflection coefficients. A first source of available information is constituted by acoustic information per se, in particular measurements of the propagation times of waves between the various acquisition levels of the VSP, estimates of the noise affecting the recorded traces, and possibly knowledge of stacking velocities provided by surface seismic processing. A second important source of information comes from logging measurements, for example measurements of resistivity, porosity, density, etc..., which have the advantage over seismic data of being sampled at a very much smaller sampling interval. The study of such borehole logs makes it possible to define the probable positions of the major interfaces with very good accuracy in the longitudinal direction of the borehole. The invention also seeks to minimize, as much as possible, the computation time required on the central processor unit of a computer in order to invert a VSP, and in particular to keep the time required beneath a commercially acceptable threshold. According to a first aspect, the present invention provides a method of identifying underground geological strata from a plurality of seismic traces Sk(t) recorded in a borehole at different levels k of depth Zk, with each trace Sk(t) being constituted by n discrete values which are sampled as a function of time t at a constant time interval Δt, the method consisting in determining the series R = {r i , for i = 1,n} representative of the seismic reflection coefficients at the interfaces formed between successive geological strata, with i being an index relating to the sampling depth on a time scale at said constant interval Δt, said method including the following steps: establishing the time-depth curve relating the depth Zk to the transit time Tk recorded on the trace Sk(t) for each level k ; extracting the upgoing wavefield Uk(t) and the downgoing wavefield Dk(t) from the traces Sk(t); and calculating the traces U mean (t), U min (t), and U max (t) respectively representative of a mean value, the minimum value, and the maximum value of the upgoing wavefield Uk(t-Tk), and the trace D mean (t) representative of a mean value of the downgoing wavefield Dk(t + Tk); the method being characterized by the following steps which consist in: a) a first step of determining a series R° = {r° i } representative of an approximation to the series R by minimizing the function H(R) = Σ f(r i ) where f(r i ) is an increasing function of |r i |, which is symmetrical on the X axis, which is concave, and which satisfies the constraint R*D mean (t) = U mean (t) in which the operator * represents a convolution product; b) a second step of determining a series ΔR = {δr i } representative of a value for correcting the series R° and minimizing a function J(ΔR) which is linear in |δr i | under the constraints: U min (t) < [(R° + ΔR)*D mean (t)] < U max (t) and |Zk - Zks(R° + ΔR)| < εk in which Zks(R° + ΔR) is a linearized function in ΔR representative of the depth Zks calculated by synthesis at level k from the initial solution R° and the magnitude εk is representative of the uncertainty in the magnitudes of Zk and Tk; and c) a third step of calculating the series R = R° + ΔR {r i = r° i + δr i } representative of seismic reflection coefficients. Preferably, the values U mean (t) and D mean (t) are transformed into the frequency domain so as to obtain values U mean (ω) and D mean (ω), and the constraints of the first step are expressed in the frequency domain, i.e.: F.R = U mean (ω)/D mean (ω) where F is the matrix associated with the Fourier transform restricted to the useful frequency band of the seismic traces. Preferably, the function J(ΔR) is of the form: J(ΔR) = Σ a i |δr i | where a i is a weighting coefficient dependent on r° i . A variant of the method in accordance with the invention further includes the following steps: using said time-depth curve {Zk, Tk} to transpose at least one logging curve Y(d) previously recorded as a function of depth d in said borehole over a depth interval which covers, in particular, the depth levels Zk, into a discrete log Y(t) = {y i (t)} sampled on a time scale at the same time interval Δt; calculating the discrete differential of Y(t), i.e. Y′(t) = {y′ i (t) = Y i + 1 (t) - y i (t)}, together with its transform into the frequency domain, i.e. Y′(ω); and the first step consists in determining two series R° = {r° i } and Y′ = {y′ i } respectively representative of an initial approximate solution and of the discrete differential of the zoned log Y(t) and for which the zero values of r° i and y′ i have the same index i, by simultaneously minimizing the functions H(R) = Σ f(r i ) H(Y′) = e(y′ i ) in which e(y′ i ) is an increasing function of |y′ i |, which is symmetrical on the X axis and which is concave, under the constraints F.R = U mean (ω)/D mean (ω) E.Y′ = Y′(ω) in which E is the matrix associated with the Fourier transform restricted to an identical number of frequencies as that of said useful band. Another variant of the method according to the invention consists in minimizing the function J(ΔR) under the additional constraint: |Wa - Was(R° + ΔR)| < εv in which Wa are the stacking velocities corresponding to times τa obtained by surface seismic processing, and in which Was(R° + ΔR) is a linearized function in ΔR, representative of the synthetic stacking velocities Was available from the initial solution R°, and the magnitude εv is representative of the uncertainty on the stacking velocities Wa. According to a second aspect of the invention, the method consists in performing a zoning operation on a discrete log Y(t) = {y i (t), i = 1,n} sampled at a constant time interval Δt, thereby obtaining the series Y′ = {y′ i , i = 1,n} indicative of the positions of the interfaces formed between successive geological strata, characterized by the following steps: the discrete differential Y′ = {y′ i } of Y is calculated, and so is its transform into the frequency domain Y′(ω); the series Y′ indicative of the positions of the interfaces is determined by minimizing the function: H(Y′) = Σ e(y′ i ) in which e(y′ i ) is an increasing function of |y′ i |, which is symmetrical on the X axis and which is concave, under the constraint E.Y′ = Y′(ω) in which E is the matrix associated with the Fourier transform restricted to a selected number of frequencies. Preferably, the functions e and f are identical: Other characteristics and advantages of the invention appear from the following detailed description and the accompanying drawings, in which: Figure 1 is a general flowchart of the method in accordance with the invention which consists in identifying the successive geological strata through which a borehole passes only from information contained in the downhole seismic traces Sk(t); Figure 2 is a detailed flowchart showing a particular method of minimizing the first step as described with reference to Figure 1; Figure 3 is a general flowchart of a variant of the method in accordance with the invention which consists in identifying the successive geological strata through which a borehole passes not only from the downhole seismic traces Sk(t), but also from information derived from surface seismic traces and/or one or more borehole logs; Figure 4 is a detailed flowchart of a particular method of minimizing the first step described with reference to Figure 3; Figure 5 is an illustrative example showing traces Sk(t), deconvoluted upgoing wavefields Uk(t-Tk) and downgoing wavefields Dk(t + Tk), and mean upgoing and downgoing traces U mean (t) and D mean (t); and Figures 6A, 6B, 6C, and 6D relate to the same illustrative example and show respectively: the series of reflection coefficients r i obtained using the method described with reference to Figure 1; the series of reflection coefficients r i obtained using the variant described with reference to Figure 3; the acoustic impedance profile obtained using the variant described with reference to Figure 3; and the sampled borehole log Y(t) and the log Y(t) obtained after the zoning operation. In order to solve the complex problem posed by inversion simply and rapidly, the following assumptions are usually made: firstly, only acoustical phenomena are actually taken into account; secondly, all of the geological strata are assumed to be in the form of horizontal planes, thereby reducing the problem to one dimension only; and finally, some gain recovery is applied to compensate for the effects of spherical divergence. These assumptions give rise to the following equation: Uk = R * Dk + Bk where: Uk is the upgoing wave recorded at level k ; Dk is the downgoing wave recorded at level k ; R represents the series of reflection coefficients expressed on a time scale; Bk represents noise defined mathematically as being everything which does not correspond to a primary reflected wave, i.e. a wave which does not contain multiple reflections; and the symbol * is the convolution operator. It is thus necessary to solve for the unknown R using the upgoing wave Uk and the downgoing wave Dk. In the frequency domain, equation (I) becomes: Uk(ω) = R(ω) · Dk(ω) + Nk(ω) where: the term Nk(ω) is noise defined in the mathematical sense as being everything which does not correspond to a primary reflection as defined by the one-dimensional wave equation. The set of solutions for equation (II) is infinite. Consequently, it is desirable to limit the domain of possible solutions by means of constraints which result from knowledge of information coming from various different origins and which is not necessarily of the same nature as the solution being sought. From entropy theory, also known as information theory, a particular and preferable solution in the domain of possible solutions is the solution which presents a minimum entropy. However, given that the variables being manipulated are not statistical, a broadened notion of the concept of entropy is considered which retains the essential properties of entropy, and in particular its increase as a function of dispersion in the variables. Work performed by the Applicant has led to functions of the entropy type being defined in a general manner, which functions are of increasing absolute value, are symmetrical on the X axis and are concave in a broad sense. Functions which are concave in a broad sense include, in particular, functions which are linear in absolute value, such as the function proposed in the article entitled "Reconstruction of a sparse spike train from a portion of its spectrum, and application to high resolution deconvolution" by S. Levy and P.K. Fullagar published in Geophysics (vol. 46, pages 1235-1243): Of those entropy type functions which have increasing absolute value, which are symmetrical on the X axis, and which are concave in the broad sense, the Applicant particularly prefers the following strictly concave function: which also has the advantage of being zero at the origin. Inversion method (Figure 1) Figure 1 is a diagrammatic general flowchart of the method in accordance with the invention. The seismic traces Sk(t) shown in Figure 5 are recorded in a borehole at different levels k at depths Zk and are sampled at a constant sampling interval Δt. The traces Sk(t) are used (1), in known manner, to establish (2) the time-depth curve in the form of a series of pairs of values {Zk, Tk} in which Tk represents the transit time or the time of arrival of the first wave recorded on the corresponding trace Sk(t). The traces Sk(t) are then subjected to preliminary processing (3) which consists in extracting the fields of upgoing waves Uk(t) and downgoing waves Dk(t) from the field of traces Sk(t) by means of a known velocity filter, for example the filter described in European patent EP-0053525 in the names of Seeman and Horowicz. It is desirable to apply a conventional deconvolution operator to the fields in order to obtain upgoing wavefields Uk(t) and downgoing wavefields Dk(t) which contain primary waves only. Figure 5 also shows the upgoing wavefields Uk(t - Tk) and the downgoing wavefields Dk(t + Tk) respectively shifted to right and to left by the value Tk so as to align respective up and down events. A window is selected from the upgoing wavefield Uk(t - Tk) and for the downgoing wavefield Dk(T + Tk) obtained in this way corresponding, for example, to an interval of 100 ms, in order to determine the following values (4) in conventional manner: U mean (t) representative of a mean value of the upgoing wave in the time domain, and illustrated in Figure 5; U mean (ω) representative of a mean value of the upgoing wave in the frequency domain; D mean (t) representative of a mean value of the downgoing wave in the time domain and illustrated in Figure 5; D mean (ω) representative of a mean value of the downgoing wave in the frequency domain; U max (t) representative of a maximum value of the upgoing wave in the time domain; and U min (t) representative of a minimum value of the upgoing wave in the time domain. It may be observed that the quantities "mean value", "minimum value", and "maximum value" may equally well be determined arithmetically or be deduced from statistical analysis. For example, quantities deduced from the mean value and the calculated standard deviation may be taken for the extreme values. The numerical values used in the following example are given purely by way of illustration, and consequently they are not to be interpreted as limiting the scope of the invention. In the example described, the sampling rate Δt of the traces Sk(t) is 1 ms over a time of 256 ms. In a first step (5) during which noise N is not taken into account, i.e. during which noise is assumed to be zero, an initial estimate R° = {r° i } of the solution within the solution domain is determined (6) by minimizing a function of the entropy type having increasing absolute value, which is symmetrical on the X axis, and which is concave in the broad sense, and preferably the following function H(r): where n = 256 in the example under consideration, while satisfying the linear constraints that result from equation (II): U mean (t) = R * D mean (t) or in the frequency domain: F.R = U mean (ω)/D mean (ω) where: F is a 256 × 256 matrix associated with the Cooley-Tuckey fast Fourier transform "FFT" and restricted to the useful frequency band; R is a 1 × 256 matrix comprising the series {r i }, where i is an index varying from 1 to 256 and relating to the sampling depth in terms of a time scale having a regular interval Δt; and U mean (ω)/D mean (ω) is a 1 × 256 matrix comprising 256 samples u i mean /d i mean . The constraints thus represent a set of 256 linear equations for each corresponding integer frequency f or angular frequency ω . It must be underlined that this constraint is meaningful only over the frequency band [fmin, fmax] which is effectively usable, said band being related to the nature and to the quality of the seismic source itself. Generally, it is considered that seismic sources in current use correspond to a useful frequency band lying between 10 Hz and 80 Hz. In the example used in this description, a useful frequency band lying between 10 Hz and 50 Hz is used for illustrative purposes, with D(ω) ≃ 0, i.e. noise, being assumed for frequencies of less than 10 Hz and more than 50 Hz. Consequently, only m constrained equations need to be considered, which means that only m coefficients can be uniquely calculated, where m is defined by the formula: where: n is the number of samples in each of the seismic traces, i.e. 256 in the present example; fmax and fmin define the useful frequency band, i.e. 10 Hz to 50 Hz in the example described; and fnyq represents the Nyquist frequency derived from the rate at which the seismic signals are sampled, i.e. 500 Hz in the example described; giving m = 21 possible interfaces over an analyzed interval of 256 ms, or else in another form giving a maximum of 21 non-zero reflection coefficients r i . The process (5) of minimizing the function H(R) can be performed by any primal method operating in the feasible domain as is shown in the book entitled "Linear and non-linear programming" by D.G. Luenberger, published 1984 by Addison-Wesley Publishing Company. A primal method is described below in detail with reference to Figure 2 which shows a minimization method using a modified reduced gradient algorithm, although it should be understood that the method could also be performed in similar manner using a modified projected gradient algorithm. The result of the minimization gives rise to an initial approximate solution (6) R° = {r° i } where i = 1 to 256, giving 21 non-zero reflection coefficients with the other 235 coefficients being zero. We now turn to the second step of the method in accordance with the invention in which the initial solution R° is refined using the following equation: R = R° + ΔR or r i = r° i for i = 1, 256 The increment ΔR = {δr i } is calculated by taking account of the noise Bk and by taking account of the information relating to the time-depth curve {Zk, Tk}. To this end, the following weighted linear function J(ΔR) is minimized (7): in which a i is a weighting factor which penalizes samples for which r° i is zero by giving them larger weights than those which are given to samples for which r° i is non-zero. More particularly, it is preferable for a i to be a positive real number corresponding to the largest one of the following two values 1/r° min , 1/r° i . a i = max {1/r° min , 1/r° i } where r° min = minimum value of the non-zero coefficients r° i . This minimization is subjected to constraints which are defined firstly by noise and secondly by the time-depth curve. Firstly, the constraints related to noise come from the maximum and minimum values U max (t) and U min (t) determined above (4) which define a range within which the final solution for R must be found. In other words, these constraints may be put in the form of the following inequality which is linear in ΔR: U min (t) < [(R° + ΔR) * D mean (t)] < U max (t) Further, the constraints related to the time-depth curve stem from the series of values {Tk, Zk} determined above (2). It is possible to assume that the difference between the real depth Zk and the synthetic depth Zks as calculated in independent manner from the propagation velocities, must be less than a magnitude εk which is deduced from the uncertainty on the measurement errors in Zk and Tk. This constraint may be expressed mathematically in the form of the following inequality: |Zk - Zks(R° + ΔR)| < εk When developed, this inequality becomes: in which Δt is the time sampling rate, i.e. 1 ms in the present example; Nk represents the time of the first arrival at depth Zk expressed as a number of samples; and V i is the velocity of the seismic wave in the 1 ms interval [i, i + 1]. If the density of the rock formations is assumed to be constant, it is possible to write: where V₁ is the velocity of the top stratum as determined from the trace recorded at the shallowest level. By differentiating the above equation about the initial solution R° and neglecting second order terms, the constraint relating to the time-depth curve can be expressed by the following linear inequality in δr i : where V° j is the propagation velocity calculated from the coefficients r₁ obtained during the first step using the formula: To sum up, the second step thus consists in minimizing the function: under the following constraints which are linear in ΔR: U min (t) < [(R° + ΔR)*D mean (t)] < U max (t) and |Zk - Zks(R° + ΔR)| < εk However, since the norm J(ΔR) is expressed as a function of absolute values and therefore does not constitute a differentiatable function of r i , the increment δr i must be decomposed into two positive values δr i ⁺ and δr i ⁻, and this must be done regardless of the value of the index i, such that: δr i = δr i ⁺ - δr i ⁻ where δr i ⁺ > 0 and δr i ⁻ > 0 for i = 1,n It is easy to show that the minimum of J(ΔR) is reached when at least one of the values, i.e. δr i ⁺ or δr i ⁻ is equal to zero for each value of the index i. Consequently, the following may be written: |δr i | = |δr i ⁺ - δr i ⁻| = δr i ⁺ + δr i ⁻ The problem is finally reduced to finding two series ΔR⁺ = {δr i ⁺} and ΔR⁻ = {δr i ⁻} which minimize the function while satisfying the constraint equations U min (t) < [(R° + ΔR⁺ = ΔR⁻)* D mean (t)] < U max (t) and |Zk - Zks(R° + ΔR⁺ - ΔR⁻)| < εk The process of minimizing the function J(ΔR) may be performed using a commercially available computer program which codifies one of the numerous appropriate linear programming algorithms. By way of example, the Applicant uses IMSL (International Mathematical and Scientific Libraries) software which codifies the well-known simplex algorithm. The final solution R is obtained (9) by adding together the initial solution R° = {r° i } obtained during the first step (6), and the increment ΔR = {δr i } obtained (8) during the second step, i.e. R = {r i } = {r° i + δri} = {r° i + δr i ⁺ - δr i ⁻} Figure 6A shows the series of coefficients r i plotted along a time scale and as obtained from the traces Sk(t) shown in Figure 5. Minimizing the function H(R) (Figure 2) A detailed description of minimizing the function h(r) is now given with reference to figure 2, including a practical example for showing how it is implemented. the matrix constraint equation f.r = u mean (ω)/d mean (ω) may be represented in r²⁵⁶ as a polyhedron with each of its vertices being defined by 256 coordinates, of which m = 21 are non-zero and are referred to as base variables, while the remaining 235 coordinates are zero and are referred to as out-of-base variables. A conventional linear programming theorem which is applicable to a concave function such as H(R) shows that the function H(R) has an absolute minimum which is situated on one of the vertices of the polyhedron, i.e. the coordinates of the series R° being looked for correspond to one of the vertices of the solution polyhedron for which m = 21 co-ordinates are non-zero and (n - m) = 235 coordinates are zero. Thus, minimizing the function H(R) consists in successively considering the series R constituting the vertices of the polyhedron of solutions in order to obtain the solution R° for which the function H(R°) reaches its absolute minimum. Thus, by using the reduced gradient method as described in pages 345-358 of the book entitled "Linear and non-linear programming" by D.G. Luenberger published 1984 by Addison- Wesley Publishing Company it is possible to segment the set R into two sub-sets R0 and R1 respectively containing 235 zero coefficients r i and 21 non-zero coefficients r i . The problem of minimizing the function H(R) can thus be re-written in the following form: minimize H(R) = H(R0, R1) = H(R0) + H(R1) under the constraint F.R = (F1.R1, F0.R0) = U mean (ω)/D mean (ω) in which the sub-matrices F0 and F1 correspond respectively to the sub-sets R0 and R1 at the frequencies under consideration. The index 0 is segmented (10) by determining the 21 interface positions using the upgoing waves U mean (ω) and the downgoing waves D mean (ω) (4) and then by calculating the corresponding series R1₀ by solving the equation R1₀ = [F1₀]⁻¹.U mean (ω)/D mean (ω) in which F1₀ is the sub-matrix of F0 relating to the segmentation. This segmentation of the index 0 is determined so that F1₀ is a numerically invertable matrix, for example by using the method described in the above-mentioned article "Inversion of vertical seismic profiles by iterative modelling" P. Grivelet (page 926). Then, using an iterative method, a new estimate R1 p + 1 , R0 p + 1 is found at iteration p on the basis of the segmentation F1 p , F0 p (11): for which the value of the function H(R p + 1 ) is less than the value of H(R p ); which has the same proportion of non-zero and zero coefficients (21 and 235 respectively), i.e. which corresponds to a new vertex of the polyhedron; and which differs from the preceeding solution in that one of the coefficients of R1 p becomes zero, while one of the coefficients of R0 p becomes non-zero. To this end, the value of H(R1 p ) and its gradient ▿H(R1 p ) are calculated (12) for each of the base values of R1 p . Simultaneously, the matrix G is calculated (13) as the product of the inverted matrix [F1 p ]⁻¹ multiplied by the matrix F0 p . At (14) the product G.▿H(R1 p ) is performed thus obtaining a vector g having 235 coordinates, indicative of the variation of H(R p ) with respect to these 235 coordinates. The vector g is then subjected at (15) to a test to establish whether it is zero or non-zero. If all of the 235 coordinates in g are zero, then the absolute minimum of the function H(R) has been reached, i.e. the looked-for solution R° has been found (16). However, if some of the 235 values of g are not zero, the largest coordinate g j of the 235 coordinates in g is selected (17) since this very probably represents the direction of the largest slope in the function H(R) and, consequently, is very likely to be the direction to be followed in order to minimize the function H(R) as quickly as possible. The direction d j of the slope g j is a vector of dimension 21, which is calculated at (18) as follows: d j = G.g j = {d j(i) , i = 1 to 21} By following this direction, the distance λ to be followed along this slope in order to reach another vertex of the polyhedron is then determined (19). In other words, one of the base variables is to be zeroed in order to turn it into an out-of-base variable, and conversely one of the out-of-base variables is to be transformed into a non-zero base variable. To do this, there are 21 possible values of λ {λ i , i = 1 to 21} (24) suitable for zeroing the corresponding non-zero reflection coefficient r1 1(i) , with λ i being given by the equation: λ i = r1 1(i) /d j(i) For each of the 21 values of λ i obtained in this way, a new series R1 p + 1 i is obtained (20) using the formula: R1 p + 1 i = R1 p - λ i .d j For each of the 21 series R1 p + 1 i thus obtained, the corresponding value of H(R1 p + 1 i ) is calculated (21). Of the 21 values of H(R1 p + 1 i ), only the smallest is retained (22), i.e. the value which reduces the value of the function H(R) as quickly as possible, and it is re-named H(R1 p + 1 ) where: H(R1 p + 1 ) = the minimum of {H(R1 p + 1 i ), i = 1,21} A test at (23) then determines whether H(R1 p + 1 ) is smaller than H(R1 p ) obtained on the preceding cycle. If the test is positive, the new segmentation R1 p + 1 is adopted for a new cycle putting p = p + 1 (26), and then the process returns to block (11). Otherwise, if H(R p + 1 ) is greater than H(R1 p ), the coordinate g j of g is zeroed (25), and the method returns to the test at (15) in order to verify whether or not all of the coordinates in the vector g are zero. It will easily be seen in the present example that the successive solutions R p including the initial solution R°, always includes 21 non-zero coefficients and 235 zero coefficients. Variants of the inversion method (Figure 3A) We now turn to variants of the inversion method which additionally make it possible during the first step to inject information coming from one or more borehole logs and/or coming from surface seismic testing during the second step. In a first variant, the choice of the initial solution R° = {r° i } can be guided by using information contained in one or more logs, for example a resistivity log, a density log, etc... The probability is high that a large variation of amplitude observed in one or more logs corresponds to an interface, i.e. to a variation in acoustic impedance. It is thus highly desirable for the acoustic impedance profile derived from inverting the VSP to be in agreement with information contained in logs relating to the locations of the interfaces. In a second variant, it is desirable for the final solution R = {r i } to agree with the information available from surface seismic studies, in particular the stacking velocities Wa obtained during surface seismic line processing, or indeed in agreement with accurate information concerning one or more geological strata, in particular the propagation velocity through one or more given strata. A third variant consists in simultaneously combining the first and second variants mentioned above. Reference is now made to Figure 3 which is a flowchart of the third variant, given that the first and second variants are easily deduced therefrom by omitting information coming from surface seismic processing or from logging, as the case may be. For reasons of simplicity and conciseness of description, only one log Y(d) is used. However the inversion method can equally well accept a plurality of logs using an entirely identical processing sequence in parallel with the sequence described for the log Y(d). The available data includes: the stacking velocities Wa corresponding to the surface seismic times ta (301); the seismic traces Sk(t) of the VSP (101); and a log Y(d) expressed (201) as a function of borehole depth. The following values are determined in conventional manner identical to that described with reference to Figure 1 from the traces Sk(t) of the VSP (101): the time-depth curve {Zk, Tk} (102); the upgoing wavefield Uk(t) and the downgoing wavefield Dk(t) which contain only primary waves (103); and the values U mean (t), U mean (ω), D mean (t), D mean (ω), U max (t) and U min (t) (104). Using the time-depth curve {Zk, Tk} the depth log Y(d) is transformed into a time log Y(t) = {y i (t)} (202) expressed as a function of a time scale and sampled at the same interval as the sampling interval used for Sk(t), i.e. as an interval of 1 ms in the present example, by a conventional change of scale method. Figure 6D shows the time log Y(t) obtained in this way. The discrete differential Y′(t) = {y′ i (t)} of the log Y(t) is then calculated (203) as follows: Y′(t) = {y′ i (t) = y i + 1 (t) - y i (t)} together with its transform Y′(ω) in the frequency domain. There is an infinity of possibilities for the number of interfaces and their locations. As mentioned above, a particular and preferable solution is the solution having an entropy minimum. In addition, it is desirable for the locations of the interfaces to correspond with the transitions that can be observed on a zoned log through relatively uniform geological strata. The zoned log is written Y = {y i } and its discrete differential is written Y′ = {y′ i }. Taking up the entropy theory approach used by the Applicant, the zoning of a log may be expressed in the mathematical form which consists in minimizing an entropy type function which is increasing in absolute value, which is symmetrical on the X axis, and which is concave in the broad sense: H(Y′) = Σ f(y i ) and more particularly the following function: under the linear constraints F.Y′ = Y′(ω) It should be observed that this new approach to the problem of zoning is applicable, in a general manner, to any curve zoning operation, independently of VSP inversion. In VSP inversion, this approach gives rise to an additional intermediate result constituted by the zoned log. In VSP inversion, and by virtue of the limitation imposed by the usable frequency band [fmin, fmax], the zoning should reduce to a problem of m = 21 possible interfaces for the example described. It will readily be understood that the series Y′ should consequently comprise 21 non-zero values corresponding to 21 interfaces and 235 values corresponding to continuous zones. The problem thus consists in finding two series R° = {r° i } and Y′ = {y′ i } which have the characteristic whereby the same indices correspond respectively to zero values and to non-zero values; and which simultaneously minimize the following two entropy type functions: H(R) = Σ f(r i ) and H(Y′) = Σ e(y′ i ) in which f and e are both functions with increase in absolute value, which are symmetrical on the X axis, and which are concave in the broad sense under the constraints F.R = U mean (ω)/D mean (ω) and E.Y′ = Y′(ω) in which F and E are the matrices associated with the Cooley-Tuckey fast Fourier transform "FFT" and are respectively restrained to a number which is identical to the selected frequencies. It is essential that each of these two matrices should be restrained to an identical number of selected frequencies so that the two constraint equations define two respective polyhedra of the same family, i.e. whose vertices are defined by n coordinates of which not more than m are non-zero, while the remaining n-m coordinates are zero. In order to simplify the description, it will be assumed below that the functions f and e are identical and equal to the function f, and that the matrices F and E cover the same useful frequency band, i.e. the useful frequency band of the matrix F. In this case, the following two functions therefore need to be minimized simultaneously (105): H(R) = Σ f(r i ) and H(Y′) = Σ f(y′ i ) whence, and preferably, the following two functions: under the following linear constraints F.R = U mean (ω)/D mean (ω) and F.Y′ = Y′(ω) The process (105) of simultaneously minimizing H(R) and H(Y′) is described in detail with reference to figures 4A and 4B using a modified reduced gradient algorithm, but it should be understood that this process could be performed, in similar manner, using a modified projected gradient algorithm. The results of this minimization (105) gives rise to an initial solution R° and to the discrete differential Y′ (106). Integration of the discrete differential Y′ obtained in this way, followed by shifting by an appropriate constant makes it possible to reconstitute the zoned log Y which is shown in graphical form in Figure 6D. This clearly shows up the different geological strata. In the second step (107) and as already described with reference to Figure 1, it is necessary to minimize the same linear function J(ΔR), as follows: subjected to the same constraints as explained above, namely: U min (t) < [(R° + ΔR)*D mean (t)] < U max (t) and |Zk - Zks(R° + ΔR)| < εk In addition, the minimizing of J(ΔR) can also be subjected to one or more additional constraints which are linear and which are taken, either from the stacking velocities obtained by surface seismic processing, or else from the constraints related to prior knowledge of the propagation velocity through one or more given geological strata, or else from both simultaneously. The stacking velocities corresponding to times τa are designated by Wa. It is known that the stacking velocities are very close to the square roots of the second order moment of the series of internal velocities V i . Consequently, in a manner similar to the constraint due to the time-depth curve, it is possible to assume that the difference between the real stacking velocities Wa and the synthetic stacking velocities Was calculated independently from the propagation speeds V i must be less than a magnitude εv which is deduced from the uncertainty on Wa. This constraint can thus take the form of the following inequality: in which Na represents time τa expressed as a number of samples at the sampling interval duration Δt, i.e. 1 ms in the present example, and V i is the wave propagation velocity in the interval [i, i + 1]. Repeating the mathematical reasoning used above for linearizing the constraints related to the time-depth curve {Zk, Tk}, the constraint related to the stacking velocities can be summarized by the following inequality which is linear in δr i : in which V° j is the propagation velocity as calculated from the coefficients r°₁ as obtained during the first step from the equation: To sum up, the variant of the second step thus consists in minimizing the function: under the following constraints which are linear in ΔR: U min (t) < [(R° + ΔR)*D mean (t)] < U max (t) and |Zk - Zks(R° + ΔR)| < εk Wa - Was (R° + ΔR) < εv In this variant, the process of minimizing the function J(ΔR) is also performed by means of a linear programming algorithm, such as the simplex algorithm as codified in the above-mentioned IMSL software. Figure 6B shows a series of reflection coefficients r i obtained using the above-described variant. It may be observed, in particular, that the positions of the reflection coefficients in Figure 6B closely match the positions of the interfaces of the zones log Y(t) shown in Figure 6D. Simultaneously minimizing H(R) and H(Y′) (Figures 4A and 4B) There follows a detailed description with reference to Figures 4A and 4B of the process for simultaneously minimizing the functions H(R) and H(Y′) which corresponds to a variant of the first step. By analogy with the description made with reference to Figure 3, the equations of the constraints may be represented as two polyhedra, with the vertices of each of these polyhedra being defined by 256 coordinates. Thus, the process of simultaneous minimization consists in running successively through the vertices of the two polyhedra R²⁵⁶ and Y′²⁵⁶ in order to obtain the low entropy series R° and Y′. The prior processing described with reference to Figure 3 gives rise to mean upgoing and downgoing waves U mean (ω) and D mean (ω) (104), together with the discrete differential Y′(ω) (203). The maxima of the differential Y′(ω) are then determined in order to guide locating the interfaces and in order to obtain an initial segmentation of the index p = 0 of the matrix F into two sub-matrices F0₀, F1₀ (112). From this initial segmentation, the series: R1₀ = [F1₀]⁻¹ · U mean (ω)/D mean (ω) and Y′1₀ = [F1₀]⁻¹.Y′(ω) are calculated simultaneously (115 and 116) with each of them corresponding to a vertex of the corresponding polyhedron and each serving as a starting point for an iterative process similar to that described with reference to Figure 2. In the iterative process, two new series {R1 p , R0 p } and {Y′1 p , Y′0 p } are found at iteration p corresponding to the segmentation F1 p , F0 p , on the basis of the preceding segmentation F1 p-1 , F0 p-1 , such that the two new series: simultaneously reduce the values of the functions H(R) and H(Y′); each comprise a number of zero coefficients and non-zero coefficients identical to the corresponding numbers of the series of index p-1; there is a strict correspondence between the indices of the zero coefficients and the non-zero coefficients; and each zero one coefficient in each of the two series and with the same index, while enabling another zero coefficient in each of the two series and likewise having the same index in both cases to become non-zero. To this end, the value of the function H(R1 p ) and its gradient ▿H(R1 p ) are calculated (115), as are the value of the function H(Y′1 p ) and its gradient ▿H(Y′1 p ) (215). Simultaneously, the matrix G which is the product of the inverted matrix [F1 p ]⁻¹ multiplied by the matrix F0 p is also calculated (116). By obtaining the products G.▿(R1 p ) (117) and G.▿H(Y′1 p ), two vectors g r and g y are respectively obtained each comprising 235 values respectively indicative of the variation for each of their 235 coordinates of the corresponding function H. A vector g is calculated (118) whose 235 coordinates are equal to the product of respective coordinates of g r and g y . Then the vector g is subjected to a test (119) in order to determine whether it is zero or non-zero. If all of the 235 coordinates of g are zero, then the looked-for solutions R° and Y′ are found (120). Otherwise, if some of the coordinates of g are non-zero, then the coordinate g j in the vector g having the maximum absolute value is selected (121). Then, by analogy with the process described with reference to Figure 2, the following are evaluated in succession: the directions d r,j = G.g r,j (122) and d y,j = G.g y,j (222) each of which directions is represented by a vector of 21 co- ordinates; for each of the values of index i (130) the following distances are calculated: λ r,i = r1 p(i) /d r,j(i) (123) and λ y,i = y′1 p(i) /d y,j(i) (223) which are to be followed along the respective directions d r,j(i) and d y,j(i) in order to reach the following vertex of the corresponding polyhedron; for each of the values of index i (130), the following series are calculated: R1 p + 1 i = R1 p - λ r,1 .d r,j(i) (124) and Y′1 p + 1 i = Y′1 p - λ y,i · d y,j(i) (224) for each of the values of index i (130) the values of the following functions are calculated H(R1 p + 1 i ) (125) and H(Y′ p + 1 i ) (225) for each of the values of index i (130) the value P p + 1 i (126) produced by multiplying together the functions H(R1 p + 1 i ) and H(Y′ p + 1 i ), i.e. P p + 1 i = H(R1 p + 1 i ) · H(Y′ p + 1 i ) Of the 21 values of P p + 1 i , only the smallest value is retained (126) and is renamed P p + 1 , i.e. P p + 1 = minimum of {P p + 1 i } for i = 1 to 21 A test (128) then serves to verify whether the inequality P p + 1 < P p is true or not. If the test (128) is positive, the new segmentation R1 p + 1 is adopted for a new cycle with p = p + 1 (129) and the process returns to block (114). If the test (128) is negative, the coordinate g j is reset to zero (130), and the process returns to the test at (119) in order to verify whether all of the coordinates in the vector j are zero or not. Rather than verifying that the value of the product of the two functions H(R) and H(Y′) is less than its value at the preceding iteration, an alternative to the above test (128) consists in verifying, more restrictively, that the value of each of these two functions for common index i is simultaneously less than its value at the preceding iteration. As mentioned above, the description of the variants relates solely to the case for which the functions f and e are identical and equal to function f, and for which the matrices F and E cover the same useful frequency band, namely that of the matrix F. However, it is perfectly possible to minimize the entropies defined by two distinct function f and e both of which are increasing, are symmetrical on the X axis, and are concave. It is also possible to keep the matrices F and E as distinct matrices providing that the same number of frequencies is selected for both of them.

Claims: 1. A method of identifying underground geological strata from a plurality of seismic traces Sk(t) recorded in a borehole at different levels k of depth Zk, with each trace Sk(t) being constituted by n discrete values which are sampled as a function of time t at a constant time interval Δt, the method consisting in determining the series R = {r i , for i = 1,n} representative of the seismic reflection coefficients at the interfaces formed between successive geological strata, with i being an index relating to the sampling depth on a time scale at said constant interval Δt, said method including the following steps: establishing the time-depth curve relating the depth Zk to the transit time Tk recorded on the trace Sk(t) for each level k; extracting the upgoing wavefield Uk(t) and the downgoing wavefield Dk(t) from the traces Sk(t); and calculating the traces U mean (t), U min (t), and U max (t) respectively representative of a mean value, the minimum value, and the maximum value of the upgoing wavefield Uk(t - Tk), and the trace D mean (t) representative of a mean value of the downgoing wavefield Dk(t + Tk); the method being characterized by the following steps which consist in: a) a first step of determining a series R° = {r° i } representative of an approximation to the series R by minimizing the function H(R) = Σ f(r i ) where f(r i ) is an increasing function of |r i |, which is symmetrical on the X axis, which is concave, and which satisfies the constraint R * D mean (t) = U mean (t) in which the operator * represents a convolution product; b) a second step of determining a series ΔR = {δr i } representative of a value for correcting the series R° and minimizing a function J(ΔR) which is linear in |δr i | under the constraints: U min (t) < [(R° + ΔR)*D mean (t)] < U max (t) and |Zk = Zks(R° + ΔR)| < εk in which Zks(R° + ΔR) is a linearized function in ΔR representative of the depth Zks calculated synthetically at level k from the initial solution R° and the magnitude εk is representative of the uncertainty in the magnitudes of Zk and Tk; and c) a third step of calculating the series R = R° + ΔR {r i = r° i + δr i } representative of seismic reflection coefficients. 2. A method according to claim 1, characterized by the fact that it further includes a step consisting in transposing the values of U mean (t) and D mean (t) into the frequency domain so as to obtain the values U mean (ω) and D mean (ω), and that the constraints of the first step are expressed in the frequency domain, i.e.:F.R = U mean (ω)/D mean (ω) in which F is the matrix associated with the Fourier transform restricted to the useful frequency band of the seismic traces. 3. A method according to claim 1 or 2, characterized by the fact that the function J(ΔR) is of the form J(Δ R ) = Σ a i |δr i | in which a i is a weighting coefficient depending on r° i . 4. A method according to claim 2 or 3, characterized by the fact that it further includes the following steps: using said time-depth curve {Zk, Tk} to transpose at least one logging curve Y(d) previously recorded as a function of depth d in said borehole over a depth interval which covers, in particular, the depth levels Zk, into a discrete log Y(t) = {y i (t)} sampled on a time scale at the same time interval Δt; calculating the discrete differential of Y(t), i.e. Y′(t) = {y′ i (t)}, together with its transform into the frequency domain, i.e. Y′(ω); and by the fact that the first step consists in determining two series R° = {r° i } and Y′ = {y′ i } respectively representative of an initial approximate solution and the discrete differential of the zoned log Y(t) and for which the zero values of r° i and y′ i have the same index i, by simultaneously minimizing the functions H(R) = Σ f(r i ) and H(Y′) = Σ e(y′ i ) in which e(y′ i ) is an increasing function of |y′ i |, which is symmetrical on the X axis and which is concave, under the constraints F.R = U mean (ω)/D mean (ω) and E.Y′ = Y′(ω) in which E is the matrix associated with the Fourier transform restricted to an identical number of frequencies as that of said useful band. 5. A method according to claim 3 or 4, characterized by the fact that the second step consists in minimizing the function J(ΔR) under the additional constraint |Wa - Was(R° + ΔR)| < εv in which Wa are the stacking velocities corresponding to times τa obtained by surface seismic processing, and in which Was(R° + Δr) is a linearized function in ΔR, representative of the synthetic stacking velocity Was computed from the initial solution R°, and the magnitude εv is representative of the uncertainty on the stacking velocities Wa. 6. A method according to any preceding claim, characterized by the fact that the function f is the following function; 7. A method according to any one of claims 3 to 6, characterized by the fact that the matrices E and F are restricted to the same frequency band, and are consequently identical. 8. A method according to any one of claims 3 to 7, characterized by the fact that the weighting coefficient a i is equal to the larger of the two values 1/r° min and 1/r° i , with the value r° min being the minimum value of the non-zero coefficients r° i . 9. A method of identifying underground geological strata by performing a zoning operation on a discrete log Y(t) = {y i (t), i = 1,n} sampled at a constant time interval Δt, thereby obtaining the series Y′ = {y′ i , i = 1,n} indicative of the positions of the interfaces formed between successive geological strata, characterized by the following steps: the discrete differential Y′ = {y′ i } of Y is calculated, and so is its transform into the frequency domain Y′(ω); the series Y′ indicative of the positions of the interfaces is determined by minimizing the function: H(Y) = Σ e(y′ i ) in which e(y′ i ) is an increasing function of |y′ i |, which is symmetrical on the X axis and which is concave, under the constraint E.Y′ = Y′(ω) in which E is the matrix associated with the Fourier transform restricted to a selected number of frequencies. 10. A method according to claim 9, characterized by the fact that the function e is the following function:

Inventors: CARRON, DIDIER
Applicants: SCHLUMBERGER LIMITED, SOCIETE DE PROSPECTION ELECTRIQUE  SCHLUMBERGER
Application year: 1988
Main classification: G01V1
IPC codes: G01V1