CN109307884B - Method and system for automatically extracting significant reflection interface - Google Patents
Method and system for automatically extracting significant reflection interface Download PDFInfo
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- CN109307884B CN109307884B CN201710630979.4A CN201710630979A CN109307884B CN 109307884 B CN109307884 B CN 109307884B CN 201710630979 A CN201710630979 A CN 201710630979A CN 109307884 B CN109307884 B CN 109307884B
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Abstract
The invention provides a method and a system for automatically extracting a significant reflection interface, wherein the method comprises the following steps: obtaining a sparse reflection coefficient pulse sequence by sparse pulse deconvolution of seismic data reflected waves; extracting singularity index characteristics of the reflection coefficient based on singularity attributes of wavelet transformation; and acquiring the unique position of the seismic significant reflection interface based on the singularity index features. The method of the invention improves the efficiency of extracting the obvious reflection interface, saves the cost for constructing the operation of the interpretation project and improves the market benefit.
Description
Technical Field
The invention belongs to the field of seismic data interpretation, and particularly relates to a method and a system for automatically extracting a significant reflection interface, which can be applied to seismic data processing in petroleum geophysical exploration.
Background
A significantly reflective interface refers to a strong reflective interface that does not interfere with other reflected waves. The position of the obvious reflection interface is determined in seismic data structure interpretation, the accurate tracking of the obvious reflection interface is particularly important, and particularly in fine horizon calibration, the accuracy of identification of the obvious reflection interface directly influences the accuracy of horizon calibration and subsequent interpretation work. The extraction of the obvious reflection interface is mostly explained on the seismic section by manual operation at present, and the method of manual identification is feasible and effective for two-dimensional work areas or small three-dimensional seismic data. However, for the interpretation of mass data, the manual identification method is inefficient, occupies half or even more of the whole construction interpretation period, and causes a time planning obstacle for the project operation in a short period. Picking peaks or valleys directly from seismic data by a computer often presents inaccuracies and multiple solution situations due to the non-smoothness of the seismic waveform. At present, the level of a computer is continuously improved, the advantage of high computing efficiency of the computer is fully utilized, and the development of the computer automatic picking technology of the obvious reflection interface is combined with the effective theoretical basis of the identification of the obvious reflection interface, so that the computer automatic picking technology has important research significance and practical application value.
Disclosure of Invention
Aiming at the characteristic of low working efficiency of the artificial identification of the significant reflection interface, the invention firstly adopts sparse pulse deconvolution to seismic data reflection waves to eliminate the influence of seismic wavelets to obtain a sparse reflection coefficient pulse sequence, researches the singularity characteristic of the reflection coefficient by using a singularity attribute extraction method based on wavelet transformation, combines seismic data constraint to obtain the unique seismic significant reflection interface position, improves the extraction efficiency of the significant reflection interface, saves the cost for constructing the operation of an interpretation project and improves the market benefit.
According to one aspect of the present invention, there is provided a method for automatically extracting a significant reflection interface, the method comprising:
obtaining a sparse reflection coefficient pulse sequence by sparse pulse deconvolution of seismic data reflected waves;
extracting singularity index characteristics of the reflection coefficient based on singularity attributes of wavelet transformation;
and acquiring the unique position of the seismic significant reflection interface based on the singularity index features.
Further, obtaining a sparse reflection coefficient pulse sequence by performing sparse pulse deconvolution on seismic data reflection waves comprises:
establishing a sparse pulse deconvolution target function, and expressing approximate reflection coefficients as:
r＝w-1s，
solving a sparse pulse deconvolution objective function:
rk＝(GTG+μQk-1)-1GTd
wherein Q isk-1＝Q(mk-1)
In the formula, rRepresenting approximated reflection coefficients, w representing a seismic wavelet, sRepresenting approximate seismic signals, w-1Is an inverse operator, G representsThe positive operator, d represents observation data, Q is a constraint term, is a diagonal matrix formed by covariance, determines the sparsity of an inversion solution, mu is a sparse constraint coefficient, the larger mu is, the closer the inversion solution is to zero, the more sparse the inversion result is, m is a reflection coefficient, and the superscript k represents the inversion iteration times.
Further, the reflection coefficient is constrained and modified by the amplitude energy:
|r| is the absolute value of the reflection coefficient, symbol&Representing a logical and calculation of the logical 'and' of,
is the reflection coefficient after energy restriction, (x) is the pulse function when | r is satisfied|≥rTThen, x is 1 and the pulse function value is 1.
Further, the reflection coefficient is subjected to multi-scale decomposition through wavelet transformation, and the coefficient C { r } (sigma, tau) obtained through the wavelet transformation is expressed as follows:
wherein
Is the Morlet wavelet basis function, σ is the scale, the notation x is the convolution,
and is
Further, the singularity index is solved using a least squares fit:
log|C{r}(σ,τ)|≤logb+αlogσ
and | C { r } (sigma, tau) | is the absolute value of the wavelet transform coefficient, log is the natural logarithm, the upper bound of the alpha value is the singular index of the signal, and b is a constant.
Further, the larger alpha represents the stronger singularity, and the position corresponding to the strong singularity index value indicates the unique position of the significant reflection interface.
Further, sparse pulse deconvolution is the computation of the amplitude and time of reflection coefficients with sparsely distributed features from noisy seismic traces.
Furthermore, local maximum points in the wavelet transform coefficient C { r } (σ, τ) form a wavelet transform modulus maximum line WTMML, and the above formula is fitted by using a least square method, and the slope converging to the maximum line is the value of α.
According to another aspect of the present invention, there is provided an automatic extraction system for a significant reflection interface, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
obtaining a sparse reflection coefficient pulse sequence by sparse pulse deconvolution of seismic data reflected waves;
extracting singularity index characteristics of the reflection coefficient based on singularity attributes of wavelet transformation;
and acquiring the unique position of the seismic significant reflection interface based on the singularity index features.
The invention establishes a set of computer automatic picking technology for the significant reflection interface. The method comprises the steps of reducing the influence of seismic wavelets in seismic data reflection waves by sparse pulse deconvolution to obtain a sparse reflection coefficient pulse sequence, researching the singularity characteristics of reflection coefficients by a singularity attribute extraction method based on wavelet transformation, combining seismic data constraint to obtain the unique seismic significant reflection interface position, and improving the efficiency and the precision of significant reflection interface extraction.
The sparse pulse deconvolution-based significant reflection interface extraction method is verified through a one-dimensional theoretical model and actual data, and the technology is applied to significant reflection interface extraction of two-dimensional actual data. The model and the practical data application show that the invention can achieve ideal effect.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
FIG. 1 shows a method process flow diagram in accordance with one embodiment of the present invention.
FIG. 2 illustrates seismic data and its wavelet domain multi-scale features according to one embodiment of the invention.
FIG. 3 illustrates the effect of model data significant reflection interface extraction according to one embodiment of the present invention.
FIG. 4 illustrates actual data significant reflection interface extraction according to one embodiment of the present invention.
FIG. 5 illustrates two-dimensional actual data significant reflection interface extraction according to one embodiment of the present invention.
Fig. 6 shows a flow chart of the method for automatically extracting a significant reflection interface of the present invention.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The significant reflection interface or the structural interface plays an important role in seismic data interpretation. Aiming at the characteristics of low working efficiency of manually identifying the obvious reflection interface and uncertain representation of the reflection interface, the invention establishes a set of computer automatic picking technology for the obvious reflection interface. According to the method, firstly, the seismic wavelet influence is eliminated by adopting sparse pulse deconvolution on seismic data reflected waves to obtain a sparse reflection coefficient pulse sequence, the singularity characteristic of a reflection coefficient is researched by utilizing a singularity attribute extraction method based on wavelet transformation, the unique seismic significant reflection interface position is obtained by combining seismic data constraint, the significant reflection interface extraction efficiency is improved, the cost is saved for constructing the operation of an interpretation project, and the market benefit is improved.
The representation forms of the signals are various, the seismic signal is one of the signals, and the following conditions are satisfied in mathematical expression for one seismic signal:
(1) the function has at least one extreme point;
(2) the number of the extreme points is more than or equal to the number of zero points;
(3) the stability condition is satisfied, and the energy is limited.
Therefore, the seismic wavelets are required to be short pulses with alternating positive and negative, have attenuation characteristics, and return energy to zero in a short time. The seismic wavelets used in practice meet the conditions, the seismic recording is a convolution process of the reflection coefficient and the seismic wavelets, and the conditions are also applicable to seismic reflection. Significant reflection interface extraction is important for its uniqueness. The seismic reflection waves have a certain time width, and reflection wave information needs to be described by using a unique characteristic point. The method of combining the amplitude maximum value and the phase can improve the accuracy of extracting the significant reflection interface of the seismic data, but the method is seriously influenced by noise.
As shown in fig. 6, there is provided an automatic extraction method of a significant reflection interface, the method including:
obtaining a sparse reflection coefficient pulse sequence by sparse pulse deconvolution of seismic data reflected waves;
extracting singularity index characteristics of the reflection coefficient based on singularity attributes of wavelet transformation;
and acquiring the unique position of the seismic significant reflection interface based on the singularity index features.
In order to eliminate the influence of seismic wavelets, firstly, sparse pulse deconvolution is adopted for seismic data reflected waves to obtain a sparse reflection coefficient pulse sequence. Sparse pulse deconvolution is actually the computation of the amplitude and time of a reflection coefficient with sparsely distributed characteristics from noisy seismic traces using the deconvolution principle.
Seismic signal stCan pass through the seismic wavelet wtAnd the reflection coefficient r of the time domaintBy convolution we obtain:
st＝wt*rt (1-1)
expressed in discrete form:
in the formula: st＝{s0,s1,......sn}，wt＝{w0,w1,......wm}，rt＝{r0,r1,......rp}。
It can also be represented by vectors: s-wr
The task of deconvolution is to find r under the conditions of known s and w. According to the mathematical matrix calculation principle, the wavelet matrix w can be inverted:
r＝w-1s (1-3)
w-1is an inverse operator and is a continuous operator. The reflection coefficient r is less sensitive to small errors in the seismic data s. By sTAnd rTRepresenting accurate seismic data and accurate reflection coefficients, in sAnd rRepresenting approximated seismic data and approximated reflection coefficients, according to an inverse operator w-1With a defined, single-valued and continuous property over the whole space, the approximate reflection coefficient can be solved by r＝w-1sAs w-1sTAn approximation of.
Sparse pulse inversion is a nonlinear optimization problem, and iterative solution is performed through the following formula:
rk＝(GTG+μQk-1)-1GTd (1-4)
wherein:
Qk-1＝Q(mk-1)
the goal of sparse pulse deconvolution is to reduce the effect of seismic wavelets, but not eliminate it completely. It is unique to the position of the reflecting interface rather than having a certain width. It is therefore necessary to determine the uniqueness of the interface. The invention utilizes a singularity attribute extraction method based on wavelet transformation to research the singularity characteristics of the reflection coefficient, combines seismic data constraint and obtains the unique position of the seismic significant reflection interface.
If the function has an infinite derivative it is said that the function is smooth or non-singular if the function has at time t0The singularity indicates that the function is at t0The dots are not differentiable. Some signals have no discontinuities, but have discontinuities in their higher derivatives, which are referred to as having higher singularities. For sparse pulse deconvolution signals, the signals belong to higher order singular signals. Firstly, performing multi-scale decomposition on a deconvolution signal through wavelet transformation:
the coefficient C { r } (σ, τ) obtained by the wavelet transform can be expressed as follows:
wherein
Is a wavelet basis function, and
and is
Local maximum points in the wavelet transform coefficients form a wavelet transform modulus maximum line WTMML. For a certain point in the time scale plane, if the following conditions are satisfied:
|C{r}(σ0,τ)|＜|C{r}(σ0,τ0)|,τ∈(τ0-,τ0+) (1-6)
then point (τ) is called0,σ0) Is the local module maximum point. By observing the modulus maximum of wavelet transform and analyzing its attenuationAnd calculating the characteristics of the singularity index. If the modulus maximum of a certain point wavelet transform mostly converges in a small cone above the point, it means that the function itself has no fast oscillation point in the neighborhood of the singular point, i.e. the reflection coefficient is independent and is not interfered by other reflection interfaces. The attenuation of the modes of the wavelet transform coefficients is controlled by the attenuation of the tapered regions.
Performing wavelet transformation on the reflection coefficient in a certain neighborhood to obtain a wavelet coefficient, if the wavelet coefficient meets the following conditions:
log|C{R}(σ,τ)|≤logb+αlogσ (1-7)
and | C { r } (sigma, tau) | is the absolute value of the wavelet transform coefficient, log is the natural logarithm, and the upper bound of the alpha value is the singular index of the signal. Where b is a constant, the above equation is usually fitted using least squares, and the slope of the line converging to the maximum is the value of α. The larger the α, the more singular, indicating that the reflective interface is more "pronounced".
And a sparse reflection coefficient sequence is obtained through sparse pulse deconvolution, so that the influence of wavelets in the seismic data is reduced, and the resolution of waveform indication in the seismic signals is improved. The selection of the strong reflection interface is controlled by the reflection amplitude energy. Based on the multi-scale characteristics of wavelet transformation, proper wavelets are selected to carry out multi-scale decomposition of the reflection coefficient, and the modulus maximum value of the wavelet transformation in the scale space is utilized to calculate the singular value index, so that the position of the obvious reflection interface is indicated.
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
As shown in fig. 1, a flow diagram of a method process is shown, in accordance with one embodiment of the present invention.
The method of the embodiment specifically comprises the following steps:
firstly, sparse pulse deconvolution is carried out on seismic data to obtain a sparse reflection coefficient sequence. Establishing a sparse pulse deconvolution target function:
table of approximate reflection coefficientsShown as follows: r is＝w-1s (1-8)
The error between the seismic data synthesized by adopting the approximately obtained reflection coefficients and the real seismic data is minimum as a target functional:
J＝||wr-s||2 (1-9)
the reflection coefficient r corresponding to the target spread function value when it reaches the minimumIs the optimal solution to be obtained.
The invention adopts a two-norm form, which implies that the data error obeys Gaussian distribution.
Solving the sparse pulse deconvolution objective function to obtain an approximate reflection coefficient:
rk＝(GTG+μQk-1)-1GTd (1-10)
in the formula, rRepresenting approximated reflection coefficients, w representing a seismic wavelet, sRepresenting approximate seismic signals, w-1The inversion method is characterized in that the inversion method is an inverse operator, G represents a forward operator, d represents observation data, Q is a constraint term, the opposite angle matrix is formed by covariance, the sparsity of an inversion solution is determined, mu is a sparse constraint coefficient, the larger mu is, the more the inversion solution approaches to zero, the more sparse the inversion result is, m is a reflection coefficient, and the superscript k represents the inversion iteration times.
Next, the reflection coefficient is constrained and modified by the amplitude energy.
|r| is the absolute value of the reflection coefficient, symbol&Representing a logical and calculation of the logical 'and' of,
is the reflection coefficient after energy restriction, (x) is the pulse function when | r is satisfied|≥rTThen, x is 1 and the pulse function value is 1.
Next, wavelet transform is performed on the reflection coefficient to obtain a wavelet coefficient C { r } (σ, τ):
wherein
Is the Morlet wavelet basis function, σ is the scale, the notation x is the convolution,
and is
Next, the wavelet coefficients C { r } (σ, τ) are scalably modulo extrema. Local maximum points in the wavelet transform coefficients form a wavelet transform modulus maximum line WTMML.
Next, the singularity index is solved using a least squares fit:
log|C{R}(σ,τ)|≤logb+αlogσ (1-13)
and | C { r } (sigma, tau) | is the absolute value of the wavelet transform coefficient, log is the natural logarithm, the upper bound of the alpha value is the singular index of the signal, and b is a constant.
The above equation is fitted by the least square method, and the slope converging to the maximum line is the value of α.
Finally, a singularity index representation of the significant reflecting interface is obtained. The larger the alpha is, the stronger the singularity is, the position corresponding to the strong singularity index value indicates the unique position of the significant reflection interface, and the strong singular value coordinate value is extracted.
FIG. 2 is a composite seismic data containing a one-dimensional theoretical model of random noise (FIG. 2a) and its wavelet transform (FIG. 2 b). The theoretical model only comprises two reflection interfaces which are respectively positioned at the positions of time 335ms and 585ms, and reflected waves of the two interfaces on the seismic data do not interfere with each other and are standard significant reflection interfaces. In addition to the two stronger energy blobs, some energy blobs due to noise exist on the multi-scale section of the wavelet transform.
Fig. 3a is a reflection coefficient obtained by performing sparse pulse deconvolution on the seismic data, fig. 3b is a scale profile obtained by performing wavelet transform on the reflection coefficient, and it is apparent from comparison with fig. 2b that noise energy clusters hardly exist in fig. 3b, and the position of the singular value accurately represents the position of the significant reflection interface.
FIG. 4 shows the effect of one-dimensional data application. FIG. 4a is the seismic data observed in the actual field, FIG. 4b is the reflection coefficient obtained by sparse pulse deconvolution, and the reflection coefficient of part of weak energy and the reflection coefficient of interference are eliminated by energy constraint (FIG. 4 c). FIG. 4d shows the wavelet domain characteristics of reflection coefficients, with two large energy blobs, wherein the energy blob at the 200ms position shows significant wedge characteristics, and the energy blob at the 600ms position is the result of the co-operation of multiple reflection coefficients. The 200ms singularities on the singular values of fig. 4e are strong and independent, and the positions of the significant reflection interfaces in fig. 4f are obtained through the amplitude constraint of the original data.
The method is simultaneously applied to the extraction of the significant reflection interface of two-dimensional actual data, and fig. 5 is the superposition of the offset section and the position of the extracted significant reflection interface, so that the method can be proved to be capable of automatically and accurately extracting the position of the significant reflection interface by using a computer, and has obvious effect, stability and practicability.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (6)
1. An automatic extraction method for a significant reflection interface is characterized by comprising the following steps:
obtaining a sparse reflection coefficient pulse sequence by sparse pulse deconvolution of seismic data reflected waves;
extracting singularity index characteristics of the reflection coefficient based on singularity attributes of wavelet transformation;
acquiring the unique position of the earthquake significant reflection interface based on the singularity index features;
the method for obtaining the sparse reflection coefficient pulse sequence by adopting the sparse pulse deconvolution on the seismic data reflection waves comprises the following steps:
establishing a sparse pulse deconvolution target function, and expressing approximate reflection coefficients as:
r＝w-1s，
solving a sparse pulse deconvolution objective function:
rk＝(GTG+μQk-1)-1GTd
wherein Q isk-1＝Q(mk-1)
In the formula, rRepresenting approximated reflection coefficients, w representing a seismic wavelet, sRepresenting approximate seismic signals, w-1The inversion algorithm is an inverse operator, G represents a forward operator, d represents observation data, Q is a constraint term, the inversion algorithm is a diagonal matrix formed by covariance, the sparsity of an inversion solution is determined, mu is a sparse constraint coefficient, the larger mu is, the more the inversion solution approaches to zero, the more sparse the inversion result is, m is a reflection coefficient, and the superscript k represents the inversion iteration times;
wherein the reflection coefficient is constrained and corrected by the amplitude energy:
|rabsolute value, sign of reflection coefficient with | being approximate&Representing a logical and calculation of the logical 'and' of,
when the reflection coefficient after energy restriction satisfies | r|≥rTThen, x is 1 and the pulse function value is 1.
2. The method of claim 1, wherein the reflection coefficient is subjected to multi-scale decomposition by wavelet transform, and the wavelet transform coefficient C { r } (σ, τ) is expressed as follows:
wherein
Is the Morlet wavelet basis function, σ is the scale, the notation x is the convolution,
and is
3. The method of claim 2, wherein the singular index is solved using a least squares fit:
log|C{r}(σ,τ)|≤logb+αlogσ
the absolute value of the wavelet transform coefficient is | C { r } (sigma, tau) |, log is a natural logarithm, the upper bound of the alpha value is the singular index of the signal, b is a constant, local maximum points in the wavelet transform coefficient C { r } (sigma, tau) form a wavelet transform modulus maximum line WTMML, the above formula is fitted by adopting a least square method, and the slope converging to the maximum line is the value of alpha.
4. The method according to claim 3, wherein the larger α represents stronger singularity, and the position corresponding to the strong singularity index value indicates the unique position of the significant reflection interface.
5. The method of claim 1, wherein sparse pulse deconvolution is an amplitude and time calculation of reflection coefficients with sparsely distributed features from noisy seismic traces.
6. An automatic extraction system for a significant reflection interface, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
obtaining a sparse reflection coefficient pulse sequence by sparse pulse deconvolution of seismic data reflected waves;
extracting singularity index characteristics of the reflection coefficient based on singularity attributes of wavelet transformation;
acquiring the unique position of the earthquake significant reflection interface based on the singularity index features;
the method for obtaining the sparse reflection coefficient pulse sequence by adopting the sparse pulse deconvolution on the seismic data reflection waves comprises the following steps:
establishing a sparse pulse deconvolution target function, and expressing approximate reflection coefficients as:
r＝w-1s，
solving a sparse pulse deconvolution objective function:
rk＝(GTG+μQk-1)-1GTd
wherein Q isk-1＝Q(mk-1)
In the formula, rRepresenting approximated reflection coefficients, w representing a seismic wavelet, sRepresenting approximate seismic signals, w-1The inversion algorithm is an inverse operator, G represents a forward operator, d represents observation data, Q is a constraint term, the inversion algorithm is a diagonal matrix formed by covariance, the sparsity of an inversion solution is determined, mu is a sparse constraint coefficient, the larger mu is, the more the inversion solution approaches to zero, the more sparse the inversion result is, m is a reflection coefficient, and the superscript k represents the inversion iteration times;
wherein the reflection coefficient is constrained and corrected by the amplitude energy:
|rabsolute value, sign of reflection coefficient with | being approximate&Representing a logical and calculation of the logical 'and' of,
when the reflection coefficient after energy restriction satisfies | r|≥rTThen, x is 1 and the pulse function value is 1.
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