CN116381793B - Pre-stack inversion method and device for structure TV regularized joint inter-channel difference constraint - Google Patents
Pre-stack inversion method and device for structure TV regularized joint inter-channel difference constraint Download PDFInfo
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Abstract
The invention provides a prestack inversion method and device for structural TV regularized joint inter-channel difference constraint, comprising the following steps: acquiring original seismic data of a target work area, and performing data processing on the original seismic data to obtain actual seismic data; calculating a reflection coefficient sequence based on actual seismic data, and performing convolution calculation based on the reflection coefficient sequence to obtain forward synthetic data; constructing STV regularization terms and data constraint regularization terms based on the actual seismic data and forward synthetic data; constructing an objective function based on the STV regularization term, the data constraint regularization term and a residual L2 norm between the actual seismic data and the forward synthetic data; and solving the objective function to obtain an inversion result of the elastic parameter. The invention improves the reliability and resolution of the elastic parameters obtained by pre-stack inversion.
Description
Technical Field
The invention relates to the technical field of seismic exploration, in particular to a prestack inversion method and device for difference constraint between structural TV regularized joint channels.
Background
In the field of seismic exploration, pre-stack amplitude versus offset or angle of incidence (AVO/AVA) inversion has become an effective means of connecting seismic data with various elastic parameters. Longitudinal wave speed, transverse wave speed and density are the most common inversion targets for pre-stack inversion, and reservoir parameters such as porosity, young modulus, fluid content and the like can be provided according to a petrophysical model, so that the method has very important significance for describing the change of the reservoir. However, the reliability and resolution of the elastic parameters obtained by the existing prestack inversion method are poor.
Disclosure of Invention
In view of the above, the invention aims to provide a pre-stack inversion method and device for regularizing joint inter-channel difference constraint by a structure TV so as to improve the reliability and resolution of elastic parameters obtained by pre-stack inversion.
In order to achieve the above object, the technical scheme adopted by the embodiment of the invention is as follows:
in a first aspect, an embodiment of the present invention provides a pre-stack inversion method for regularizing a structural TV and combining inter-channel difference constraints, including: acquiring original seismic data of a target work area, and performing data processing on the original seismic data to obtain actual seismic data; calculating a reflection coefficient sequence based on actual seismic data, and performing convolution calculation based on the reflection coefficient sequence to obtain forward synthetic data; constructing STV regularization terms and data constraint regularization terms based on the actual seismic data and forward synthetic data; constructing an objective function based on the STV regularization term, the data constraint regularization term and a residual L2 norm between the actual seismic data and the forward synthetic data; and solving the objective function to obtain an inversion result of the elastic parameter.
In one embodiment, calculating a sequence of reflection coefficients based on actual seismic data and performing convolution calculations based on the sequence of reflection coefficients to obtain forward synthetic data includes: performing interpolation operation and extrapolation operation on actual seismic data based on preset frequency to obtain an initial model of elastic parameters, and performing sub-angle superposition on the actual seismic data to obtain seismic wavelets; calculating a reflection coefficient sequence by adopting a Zoeppritz equation; and carrying out convolution calculation based on the reflection coefficient sequence, the initial model of the elastic parameter and the seismic wavelet to obtain forward synthetic data.
In one embodiment, constructing an STV regularization term and a data constraint regularization term based on actual seismic data and forward synthetic data includes: calculating a stratum dip angle by adopting a plane wave destructive filtering algorithm, and rotating the conventional TV regularization into a parallel dip angle direction and a vertical dip angle direction based on the stratum dip angle to obtain an STV regularization term; a plurality of adjacent seismic traces are determined as a set of seismic traces, and a data constraint regularization term is determined based on differences between actual seismic data and forward synthetic data between the adjacent seismic traces.
In one embodiment, constructing an objective function based on the STV regularization term, the data constraint regularization term, and a residual L2 norm between the actual seismic data and the forward synthetic data, comprises: taking a plurality of adjacent seismic channels as a seismic channel set, and constructing an objective function according to the following formula:
STV(m t )＝||D parl m t || 1 +||D perp m t || 1
wherein J (m) represents an objective function, d t Actual seismic data, G (m), representing a t-th seismic trace gather t Forward synthetic data representing the t-th seismic trace set, B representing a second order differential matrix, m t Elastic parameters representing the t-th gather of seismic traces, [ lambda ] STV (m t ) Representing the regularization term of the STV,representing the data constraint regularization term, η, λ, β representing the regularization parameters.
In one embodiment, solving the objective function to obtain the inversion result of the elastic parameter includes: and solving an objective function by adopting a Split-Bregman algorithm and a Levernberg-Marquardt algorithm based on the initial model of the elastic parameters and actual seismic data to obtain an inversion result of the elastic parameters.
In one embodiment, the objective function is solved using the Split-Bregman algorithm and the levenberg-Marquardt algorithm, including: converting the objective function into a first objective function by adopting a Split-Bregman algorithm; the first objective function is solved using the Levernberg-Marquardt algorithm.
In a second aspect, an embodiment of the present invention provides a pre-stack inversion apparatus for regularizing a structural TV in combination with inter-channel difference constraint, including: the data acquisition module is used for acquiring original seismic data of a target work area and carrying out data processing on the original seismic data to obtain actual seismic data; the data synthesis module is used for calculating a reflection coefficient sequence based on actual seismic data and carrying out convolution calculation based on the reflection coefficient sequence to obtain forward synthetic data; the constraint item determining module is used for constructing an STV regularization item and a data constraint regularization item based on the actual seismic data and the forward synthetic data; the objective function determining module is used for constructing an objective function based on the STV regularization term, the data constraint regularization term and a residual L2 norm between actual seismic data and forward synthetic data; and the inversion module is used for solving the objective function to obtain an inversion result of the elastic parameter.
In one embodiment, the data synthesis module is further configured to: performing interpolation operation and extrapolation operation on actual seismic data based on preset frequency to obtain an initial model of elastic parameters, and performing sub-angle superposition on the actual seismic data to obtain seismic wavelets; calculating a reflection coefficient sequence by adopting a Zoeppritz equation; and carrying out convolution calculation based on the reflection coefficient sequence, the initial model of the elastic parameter and the seismic wavelet to obtain forward synthetic data.
In a third aspect, an embodiment of the present invention provides an electronic device comprising a processor and a memory storing computer executable instructions executable by the processor to perform the steps of the method of any one of the first aspects described above.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor performs the steps of the method of any of the first aspects provided above.
The embodiment of the invention has the following beneficial effects:
the prestack inversion method and device for the difference constraint between the regularized joint channels of the structure TV provided by the embodiment of the invention comprise the steps of firstly, acquiring original seismic data of a target work area, and performing data processing on the original seismic data to obtain actual seismic data; then, calculating a reflection coefficient sequence based on actual seismic data, and performing convolution calculation based on the reflection coefficient sequence to obtain forward synthetic data; then, constructing STV regularization items and data constraint regularization items based on the actual seismic data and forward synthetic data; then, constructing an objective function based on the STV regularization term, the data constraint regularization term and a residual L2 norm between the actual seismic data and the forward synthetic data; and finally, solving the objective function to obtain an inversion result of the elastic parameter. According to the method, the STV regularization is adopted to introduce the objective function, so that the uncertainty of inversion can be reduced, and the sparsity is enhanced; meanwhile, the data constraint regularization term is introduced into the objective function, so that the inversion result can be more matched with actual data, the space of an inversion solution is reduced from the data layer, and the reliability of elastic parameters obtained by pre-stack inversion is improved; finally, the inversion result of the elastic parameter can be directly obtained by the method, so that errors caused by calculation in the traditional method are effectively avoided, and the reliability and resolution of the inversion result are further improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a pre-stack inversion method of structural TV regularization joint inter-channel difference constraint provided by an embodiment of the invention;
FIG. 2 is a flow chart of another method for pre-stack inversion of structural TV regularization joint inter-channel difference constraint provided by an embodiment of the invention;
FIG. 3 is a schematic structural diagram of a pre-stack inversion device for regularized joint inter-channel difference constraint of a structure TV according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, the traditional prestack inversion firstly inverts the reflectivity, and then obtains elastic parameters such as longitudinal wave speed, transverse wave speed, density and the like through channel integration, so that the reliability and the resolution are poor.
Based on the above, the structure TV regularized joint inter-channel difference constraint pre-stack inversion method and device provided by the embodiment of the invention can improve the reliability and resolution of elastic parameters obtained by pre-stack inversion.
For the convenience of understanding the present embodiment, first, a pre-stack inversion method for regularizing a structure TV and combining inter-channel difference constraint disclosed in the present embodiment is described in detail.
Embodiment one:
the embodiment of the invention provides a prestack inversion method of difference constraint between structural TV regularized joint tracks, which is shown in a flow chart of the prestack inversion method of difference constraint between structural TV regularized joint tracks in FIG. 1, and mainly comprises the following steps S101 to S105:
step S101: and acquiring the original seismic data of the target work area, and performing data processing on the original seismic data to obtain actual seismic data.
In one embodiment, acquiring raw seismic data for a target work area includes: seismic shot gather data, logging data, and horizon data; performing data processing on the original seismic data, including: removing noise, deconvolution, static correction, dynamic correction, super-trace set and the like, and obtaining the actual seismic data with higher signal-to-noise ratio. The embodiment of the invention can also convert the processed actual seismic data with higher signal-to-noise ratio into the angle gather. Specifically, when angular gather conversion is performed, the root mean square velocity may be determined from the log data, and then the actual seismic data may be converted into angular gathers based on the root mean square velocity.
Step S102: and calculating a reflection coefficient sequence based on the actual seismic data, and performing convolution calculation based on the reflection coefficient sequence to obtain forward synthetic data.
In one embodiment, the reflection coefficient sequence may be calculated using an accurate Zoeppritz equation, and a convolution operation is performed to complete forward modeling, so as to obtain forward modeling synthetic data.
Step S103: STV regularization terms and data constraint regularization terms are constructed based on the actual seismic data and forward synthetic data.
In one embodiment, a conventional TV regularization direction may be rotated to a parallel dip direction and a perpendicular dip direction as STV regularization terms after obtaining formation dip using a plane wave destructive filtering (PWD) algorithm based on actual seismic data; and taking the residual error of the difference between the actual seismic data and the forward synthetic data as a data constraint regularization term.
Step S104: an objective function is constructed based on the STV regularization term, the data constraint regularization term, and the residual L2 norm between the actual seismic data and the forward synthetic data.
In one embodiment, the residual L2 norm between the actual seismic data and the forward synthetic data may be utilized as the body of the objective function, in combination with the STV regularization term and the data constraint regularization term as the new objective function.
Step S105: and solving the objective function to obtain an inversion result of the elastic parameter.
In one embodiment, the Split-Bregman algorithm can be combined with the Levernberg-Marquardt algorithm to solve the objective function, and an inversion result of the elastic parameters is obtained.
According to the prestack inversion method provided by the embodiment of the invention, the uncertainty of inversion can be reduced and the sparsity can be enhanced by introducing the objective function into the system through STV regularization; meanwhile, the data constraint regularization term is introduced into the objective function, so that the inversion result can be more matched with actual data, the space of an inversion solution is reduced from the data layer, and the reliability of elastic parameters obtained by pre-stack inversion is improved; finally, the inversion result of the elastic parameter can be directly obtained by the method, so that errors caused by calculation in the traditional method are effectively avoided, and the reliability and resolution of the inversion result are further improved.
In one embodiment, for the foregoing step S102, that is, when calculating the reflection coefficient sequence based on the actual seismic data and performing convolution calculation based on the reflection coefficient sequence to obtain forward synthesized data, the following manners may be adopted, including but not limited to:
firstly, carrying out interpolation operation and extrapolation operation on actual seismic data based on preset frequency to obtain an initial model of elastic parameters, and carrying out angle-division superposition on the actual seismic data to obtain seismic wavelets.
The elastic parameters in the embodiment of the invention comprise: longitudinal wave velocity, transverse wave velocity, and density. In specific implementation, a proper preset frequency is set, and according to the preset frequency, an initial model m of low-frequency longitudinal wave speed, transverse wave speed and density is obtained by carrying out difference and extrapolation on logging data and horizon data in actual seismic data 0 The method comprises the steps of carrying out a first treatment on the surface of the And then, the actual seismic data are superimposed in different angles to obtain the seismic wave factor W (theta) which is used for convolving forward modeling synthetic data.
The reflection coefficient sequence is then calculated using the Zoeppritz equation.
In particular implementations, the reflection coefficient sequence R is calculated using an accurate Zoeppritz equation PP (θ)。
And finally, carrying out convolution calculation on the basis of the reflection coefficient sequence, the initial model of the elastic parameter and the seismic wavelet to obtain forward synthetic data.
In particular implementations, the convolution calculation process is as follows:
wherein d represents forward synthetic data, θ i (i=1, 2, …, N) represents an incident angle, N represents the number of incident angles, m= [ v ] p v s ρ] T Representing the elastic parameters of the inversion, v p Representing longitudinal wave velocity, v s Represents transverse wave velocity, ρ represents density, m j (j=1, 2, …, n) represents the elasticity parameter of the j-th sampling point, j represents the number of sampling points, and k represents the number of seismic traces.
The forward formula in the multi-channel set mode is as follows:
where k represents the number of seismic traces.
In one embodiment, for the foregoing step S103, i.e., when constructing the STV regularization term and the data constraint regularization term based on the actual seismic data, the following approaches may be employed, including but not limited to:
(1) And calculating the stratum dip angle by adopting a plane wave destructive filtering algorithm, and rotating the conventional TV regularization into a parallel dip angle direction and a vertical dip angle direction based on the stratum dip angle to obtain an STV regularization term.
In the specific implementation, a plane wave destructive filtering algorithm can be adopted on the post-stack section to calculate a corresponding dip angle domain section so as to obtain the stratum dip angle; then, the conventional TV regularization is rotated in the lateral and longitudinal directions into parallel and perpendicular tilt directions, constructing an STV regularization term.
Specifically, the plane wave destructive filtering algorithm is as follows:
the plane wave can be represented by a first order differential equation:
where P (x, t) represents the planar wavefield, (, t) represents the local slope and σ (x, t) represents the formation dip. In discrete space, the slope between adjacent points (space interval x) can be represented by time intervals t and (x, t), namely:
p＝σ(x,t)Δx/Δt (4)
within the local range, the wavefield values agree, namely:
P(x,t)＝P(x+Δx,t+pΔt) (5)
the above equation (5) is converted into a spatial domain and a temporal domain using Z transformation:
wherein Z is x 、Z y Conversion operators representing the spatial domain and the temporal domain respectively, representing a plane wave destruction operator:
wherein B (Z) t ) May be obtained by fitting the frequency response of the low frequency filter. Further, the least squares problem of equation (8) can be minimized to determine the target slope, resulting in a dip domain profile.
C(σ,Z x ,Z t )P(Z x ,Z t )≈0 (8)
Sigma is the stratum dip angle obtained, direction of rotation:
wherein r is x And r z Representing the first order differences of the seismic data in the transverse and longitudinal directions, D parl And D perp Representing the first order difference operator parallel and perpendicular to the formation dip σ, respectively.
(2) A plurality of adjacent seismic traces are determined as a set of seismic traces, and a data constraint regularization term is determined based on differences between actual seismic data and forward synthetic data between the adjacent seismic traces.
In particular, in order to avoid that simultaneous inversion of multiple gathers and rearrangement of seismic traces results in a large amount of computation and memory consumption, in the embodiment of the present invention, adjacent p seismic traces are used as a seismic trace set, inversion is sequentially performed, and a data constraint regularization term is determined based on the difference between actual seismic data and forward synthetic data between adjacent seismic traces, i.e. according to d (,) (actual seismic data of the ith seismic trace in the nth seismic trace set) andthe (t+1th seismic data of the ith seismic trace in the seismic trace set) and the corresponding forward synthetic data together form a data constraint regularization term, and the expression is as follows:
wherein G (m) represents the forward synthetic process of the actual seismic data, Δd t 、ΔG(m) t When p seismic traces are respectively represented as one seismic trace set, the difference of actual seismic data and the difference of forward synthesized data of the t-th seismic trace set and the adjacent seismic trace set are respectively represented.
In one embodiment, for the foregoing step S104, i.e., when constructing the objective function based on the STV regularization term, the data constraint regularization term, and the residual L2 norm between the actual seismic data and the forward synthetic data, the following ways may be employed, including but not limited to:
in the implementation, p adjacent seismic channels are used as a seismic channel set, and a residual L2 norm between actual seismic data and forward synthesized data is combined with a parameter second-order differential constraint term, an STV regularization term and a data constraint regularization term to construct an objective function. Specifically, the objective function is constructed according to the following formula:
STV(m t )＝||D part m t || 1 +||D perp m t || 1
wherein J (m) represents an objective function, d t Actual seismic data, G (m), representing a t-th seismic trace gather t Forward synthetic data representing the t-th seismic trace set, B representing a second order differential matrix, m t Elastic parameters representing the t-th gather of seismic traces, [ lambda ] STV (m t ) Representing the regularization term of the STV,representing the data constraint regularization term, η, λ, β representing the regularization parameters.
In one embodiment, for the foregoing step S105, that is, when solving the objective function, the inversion result of the elastic parameter may be adopted, including but not limited to the following ways: and solving an objective function by adopting a Split-Bregman algorithm and a Levernberg-Marquardt algorithm based on the initial model of the elastic parameters and actual seismic data to obtain an inversion result of the elastic parameters.
In specific implementation, when the Split-Bregman algorithm is combined with the Levernberg-Marquardt algorithm to solve the objective function with L1 norm, firstly, the Split-Bregman algorithm is adopted to convert the objective function into a first objective function; the first objective function is then solved using the Levernberg-Marquardt algorithm.
Specifically, in the inversion process, a new parameter f needs to be introduced parl ＝ perp ＝ parl ＝g perp = inversion specific algorithm is as follows:
input: an initial model; seismic data
And (3) outputting: high resolution elastic parameter results: v p ，v s ，ρ
1, obtaining a pre-stack seismic section.
2, initializing: m= 0 ，f parl ＝ perp ＝ parl ＝ perp ＝。
3, circulation: whislev<v max (Split-Bregman algorithm)
End
4, outputting the final inversion result v+1 ) t 。
Further, a Levernberg-Marquardt algorithm is adopted to solve a first objective function, namely a formula (12), and the model update quantity delta m is calculated t The expression is as follows:
Δm t ＝-A -1 C (2)
wherein q=w×x, W represents a wavelet matrix, x represents a jacobian matrix, B represents a second-order differential matrix, which is a partial derivative matrix between a reflection coefficient and an elastic parameter, and the accuracy of calculation directly affects the final result of the LM algorithm, so that to improve the calculation accuracy and expand the angle range in which inversion can be applied, a jacobian matrix based on an accurate Zoeppritz equation needs to be established. The expressions of jacobian matrix x and hessian matrix H at the time of single-pass inversion are as follows:
where l=3, n denotes the number of incidence angles,
when multi-channel sets are simultaneously inverted, the jacobian matrix and the hessian matrix become respectively:
through the algorithm, the embodiment of the invention has no approximate condition of traditional prestack inversion, does not need to carry out channel integration, and can directly obtain v p ，v s And rho, finally comparing the inversion result with a logging curve, adjusting proper regularization parameters, setting iteration times, and inverting all p seismic channel sets to obtain a high-resolution elastic parameter profile.
According to the method provided by the embodiment of the invention, in order to enable the inversion result to be more stable, the STV regularization term, the parameter second-order difference L2 norm and the data constraint regularization term are introduced into the objective function, the parameter second-order difference L2 norm can bring excellent anti-noise capability, the STV regularization can protect stratum boundaries, the resolution is improved, the data constraint regularization term can further compress the inversion solution space in a data layer, and the forward synthesized data change characteristics are more in line with actual data. The multi-channel simultaneous inversion mode is adopted, compared with the traditional single-channel inversion mode, the relation between adjacent channels can be better considered, the traditional TV regularization can be expanded to the structure STV regularization, and the method has important significance for structural inversion of inclined stratum faults and the like.
Embodiment two:
the embodiment of the invention also provides a prestack inversion method of the structural TV regularized joint inter-channel difference constraint, which is shown in FIG. 2 and mainly comprises the following steps S201 to S207:
step S201: seismic data is acquired.
Specifically, shot gather data, logging data and horizon data of a target work area are obtained, and denoising and other treatments are carried out on the shot gather data, so that angle gather data with higher signal-to-noise ratio are obtained.
Step S202: forward modeling is performed based on the seismic data.
Specifically, the reflection coefficient sequence is calculated by using an accurate Zoeppritz equation, and convolution operation is performed to complete forward modeling so as to obtain forward modeling synthetic data.
Step S203: STV regularization terms are constructed.
Specifically, after the stratum dip angle is obtained by using a plane wave destructive filtering algorithm, the traditional TV regularization direction is rotated into a parallel dip angle and a vertical dip angle direction to construct an STV regularization term.
Step S204: constructing a data constraint regularization term.
Specifically, taking the residual error of the difference between the actual seismic data and the difference between the inversion forward synthetic data as a data constraint regularization term, namely an inter-channel difference constraint term.
Step S205: an objective function is established.
Specifically, the residual L2 norm between the actual seismic data and the forward synthetic data is used as an objective function main body, and the STV regularization term and the data constraint regularization term are combined as a new objective function.
Step S206: and solving an objective function.
Specifically, a Split-Bregman algorithm is combined with a Levernberg-Marquardt algorithm, and an L1 norm constraint term in an objective function is solved by the Split-Bregman algorithm; then solving by using a Levernberg-Marquardt algorithm.
Step S207: and (5) finishing inversion.
Specifically, regularization parameters are selected, iteration times are set, and stable three-parameter inversion results are obtained.
According to the method provided by the embodiment of the invention, the accurate Zoeppritz equation is applied to forward modeling, so that the assumption error caused by an approximation formula is avoided; the simultaneous inversion mode of the multi-channel sets is adopted, so that adjacent channel sets can be fully considered, and the channel-to-channel connection is enhanced; the seismic data gather difference constraint is added, so that the transverse continuity and resolution are enhanced, and meanwhile, the inversion method has a good inversion result for the adjacent gathers with large difference; the second-order difference L2 norm and STV regularization are added, so that the inversion has stronger anti-noise capability, stratum resolution capability and prominent construction capability; the Split-Bregman algorithm is combined with the Levernberg-Marquardt algorithm to solve the objective function, so that the L1 norm optimization problem can be stably and rapidly solved; by combining the regularization term and the optimization algorithm, the inversion method provided by the embodiment of the invention not only considers that the inter-channel connection has strong noise resistance, but also can protect stratum boundaries, highlight geological structures, has good inversion results for areas with large inter-channel differences, and improves the reliability and resolution of the inversion results.
Embodiment III:
the embodiment of the invention provides a prestack inversion device for structural TV regularized joint inter-channel difference constraint, which is shown in a structural schematic diagram of the prestack inversion device for structural TV regularized joint inter-channel difference constraint in FIG. 3, and can comprise the following parts:
the data acquisition module 301 is configured to acquire original seismic data of a target work area, and perform data processing on the original seismic data to obtain actual seismic data;
the data synthesis module 302 is configured to calculate a reflection coefficient sequence based on actual seismic data, and perform convolution calculation based on the reflection coefficient sequence to obtain forward synthesized data;
a constraint term determination module 303, configured to construct an STV regularization term and a data constraint regularization term based on the actual seismic data and the forward synthetic data;
the objective function determining module 304 is configured to construct an objective function based on the STV regularization term, the data constraint regularization term, and a residual L2 norm between the actual seismic data and the forward synthetic data;
and the inversion module 305 is used for solving the objective function to obtain an inversion result of the elastic parameter.
According to the pre-stack inversion device provided by the embodiment of the invention, the uncertainty of inversion can be reduced and the sparsity can be enhanced by introducing the objective function into the device through STV regularization; meanwhile, the data constraint regularization term is introduced into the objective function, so that the inversion result can be more matched with actual data, the space of an inversion solution is reduced from the data layer, and the reliability of elastic parameters obtained by pre-stack inversion is improved; finally, the device can directly obtain the inversion result of the elastic parameter, thereby effectively avoiding errors caused by calculation in the traditional method and further improving the reliability and resolution of the inversion result.
In one embodiment, the data synthesis module 302 is further configured to: performing interpolation operation and extrapolation operation on actual seismic data based on preset frequency to obtain an initial model of elastic parameters, and performing sub-angle superposition on the actual seismic data to obtain seismic wavelets; calculating a reflection coefficient sequence by adopting a Zoeppritz equation; and carrying out convolution calculation based on the reflection coefficient sequence, the initial model of the elastic parameter and the seismic wavelet to obtain forward synthetic data.
In one embodiment, the constraint term determination module 303 is further configured to: calculating a stratum dip angle by adopting a plane wave destructive filtering algorithm, and rotating the conventional TV regularization into a parallel dip angle direction and a vertical dip angle direction based on the stratum dip angle to obtain an STV regularization term; a plurality of adjacent seismic traces are determined as a set of seismic traces, and a data constraint regularization term is determined based on differences between actual seismic data and forward synthetic data between the adjacent seismic traces.
In one embodiment, the objective function determining module 304 is further configured to: taking a plurality of adjacent seismic channels as a seismic channel set, and constructing an objective function according to the following formula:
STV(m t )＝||D parl m t || 1 +||D perp m t || 1
wherein J (m) represents an objective function, d t Actual seismic data, G (m), representing a t-th seismic trace gather t Forward synthetic data representing the t-th seismic trace set, B representing a second order differential matrix, m t Elastic parameters representing the t-th gather of seismic traces, [ lambda ] STV (m t ) Representing the regularization term of the STV,representing the data constraint regularization term, η, λ, β representing the regularization parameters.
In one embodiment, the inversion module 305 further functions to: and solving an objective function by adopting a Split-Bregman algorithm and a Levernberg-Marquardt algorithm based on the initial model of the elastic parameters and actual seismic data to obtain an inversion result of the elastic parameters.
In one embodiment, the inversion module 305 is further configured to: converting the objective function into a first objective function by adopting a Split-Bregman algorithm; the first objective function is solved using the Levernberg-Marquardt algorithm.
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage device; the storage means has stored thereon a computer program which, when run by a processor, performs the method according to any of the above embodiments.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, the processor 40, the communication interface 43 and the memory 41 being connected by the bus 42; the processor 40 is arranged to execute executable modules, such as computer programs, stored in the memory 41.
The memory 41 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and the at least one other network element is achieved via at least one communication interface 43 (which may be wired or wireless), which may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 42 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 41 is configured to store a program, and the processor 40 executes the program after receiving an execution instruction, and the method executed by the apparatus for flow defining disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 40 or implemented by the processor 40.
The processor 40 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 40. The processor 40 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 41 and the processor 40 reads the information in the memory 41 and in combination with its hardware performs the steps of the method described above.
The computer program product of the readable storage medium provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where the program code includes instructions for executing the method described in the foregoing method embodiment, and the specific implementation may refer to the foregoing method embodiment and will not be described herein.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method of pre-stack inversion of structural TV regularization joint inter-channel difference constraints, comprising:
acquiring original seismic data of a target work area, and performing data processing on the original seismic data to obtain actual seismic data;
calculating a reflection coefficient sequence based on the actual seismic data, and performing convolution calculation based on the reflection coefficient sequence to obtain forward synthetic data;
constructing an STV regularization term and a data constraint regularization term based on the actual seismic data and the forward synthetic data;
constructing an objective function based on the STV regularization term, the data constraint regularization term, and a residual L2 norm between the actual seismic data and the forward synthetic data;
and solving the objective function to obtain an inversion result of the elastic parameter.
2. The method of claim 1, wherein calculating a sequence of reflection coefficients based on the actual seismic data and performing a convolution calculation based on the sequence of reflection coefficients to obtain forward synthetic data comprises:
performing interpolation operation and extrapolation operation on the actual seismic data based on preset frequency to obtain an initial model of elastic parameters, and performing sub-angle superposition on the actual seismic data to obtain seismic wavelets;
calculating a reflection coefficient sequence by adopting a Zoeppritz equation;
and carrying out convolution calculation based on the reflection coefficient sequence, the initial model of the elastic parameter and the seismic wavelet to obtain forward synthetic data.
3. The method of claim 1, wherein constructing an STV regularization term and a data constraint regularization term based on the actual seismic data and the forward synthetic data comprises:
calculating a stratum dip angle by adopting a plane wave destructive filtering algorithm, and rotating the conventional TV regularization into a parallel dip angle direction and a vertical dip angle direction based on the stratum dip angle to obtain an STV regularization term;
a plurality of adjacent seismic traces are determined as a set of seismic traces, and a data constraint regularization term is determined based on differences between the actual seismic data and the forward synthetic data between the adjacent seismic traces.
4. The method of claim 1, wherein constructing an objective function based on the STV regularization term, the data constraint regularization term, and a residual L2 norm between the actual seismic data and the forward synthetic data comprises:
taking a plurality of adjacent seismic channels as a seismic channel set, and constructing an objective function according to the following formula:
STV(m t )＝||D parl m t || 1 +D perp m t || 1
wherein J (m) represents an objective function, d t Actual seismic data, G (m), representing a t-th seismic trace gather t Forward synthetic data representing the t-th seismic trace set, B representing a second order differential matrix, m t Elastic parameters representing the t-th gather of seismic traces, [ lambda ] STV (m t ) Representing the regularization term of the STV,representing the data constraint regularization term, η, λ, β representing the regularization parameters.
5. The method of claim 2, wherein solving the objective function to obtain an inversion of the elastic parameter comprises:
and solving the objective function by adopting a Split-Bregman algorithm and a Levernberg-Marquardt algorithm based on the initial model of the elastic parameter and the actual seismic data to obtain an inversion result of the elastic parameter.
6. The method of claim 5, wherein solving the objective function using Split-Bregman algorithm and levenberg-Marquardt algorithm comprises:
converting the objective function into a first objective function by adopting a Split-Bregman algorithm;
and solving the first objective function by adopting a Levernberg-Marquardt algorithm.
7. A structure TV regularized joint inter-tract difference constraint pre-stack inversion apparatus, comprising:
the data acquisition module is used for acquiring original seismic data of a target work area and carrying out data processing on the original seismic data to obtain actual seismic data;
the data synthesis module is used for calculating a reflection coefficient sequence based on the actual seismic data and carrying out convolution calculation based on the reflection coefficient sequence to obtain forward synthetic data;
a constraint term determination module for constructing an STV regularization term and a data constraint regularization term based on the actual seismic data and the forward synthetic data;
the objective function determining module is used for constructing an objective function based on the STV regularization term, the data constraint regularization term and a residual L2 norm between the actual seismic data and the forward synthetic data;
and the inversion module is used for solving the objective function to obtain an inversion result of the elastic parameter.
8. The apparatus of claim 7, wherein the data synthesis module is further configured to: performing interpolation operation and extrapolation operation on the actual seismic data based on preset frequency to obtain an initial model of elastic parameters, and performing sub-angle superposition on the actual seismic data to obtain seismic wavelets;
calculating a reflection coefficient sequence by adopting a Zoeppritz equation;
and carrying out convolution calculation based on the reflection coefficient sequence, the initial model of the elastic parameter and the seismic wavelet to obtain forward synthetic data.
9. An electronic device comprising a processor and a memory, the memory storing computer executable instructions executable by the processor, the processor executing the computer executable instructions to implement the steps of the method of any one of claims 1 to 6.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor performs the steps of the method of any of the preceding claims 1 to 6.
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