Method, device and equipment for eliminating strong reflection signal in seismic data

Abstract

The invention discloses a method, device and equipment for eliminating a strong reflection signal in seismic data, and the method comprises the steps: carrying out the seismic interpretation of the seismic data of a target region, so as to obtain the horizon time corresponding to the strong reflection signal in the seismic data; intercepting first seismic data containing the strong reflection signal from the seismic data based on the horizon time corresponding to the strong reflection signal and a preset time window range; performing linear decomposition on the first seismic data to obtain a primary function component after decomposition of single-channel seismic data in the first seismic data; reconstructing a strong reflection signal of the single-channel seismic data based on the primary function component; and eliminating the strong reflection signal in the seismic data based on the reconstructed strong reflection signal. According to the method, the strong reflection can be effectively eliminated, and the problem of transverse discontinuity of the seismic section after the strong reflection is eliminated can be solved. The method well eliminates the shielding influence of strong reflection, highlights the weak signals of the reservoir, improves the prediction precision of the reservoir, and is of great significance to oil exploration.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing in geophysics, and in particular to a method, device and equipment for eliminating strong reflection signals in seismic data. Background Art

[0002] In oil and gas exploration, when there are two sets of strata with large differences in wave impedance, a strong reflection interface is usually generated. Strong seismic reflection signals will appear in seismic records, and weak reflection signals of oil and gas reservoirs will be submerged in the strong reflection seismic information, reducing the recognition accuracy of effective reservoir signals. Therefore, it is of great significance to effectively separate strong reflection signals from seismic records to highlight weak reservoir signals.

[0003] The traditional strong reflection elimination method is mainly based on the strong reflection elimination technology of matching pursuit, which uses different matching wavelets to decompose the seismic signal and regards the matching wavelet with the largest energy as the strong reflection seismic signal, so as to eliminate the strong reflection signal. Summary of the invention

[0004] In order to solve the technical problems of incomplete elimination of strong reflection signals and lateral discontinuity of seismic profiles after elimination, and thereby enrich process routes and increase selection space, a method, device and equipment for eliminating strong reflection signals in seismic data are provided in an embodiment of the present invention.

[0005] In a first aspect, an embodiment of the present invention provides a method for eliminating strong reflection signals in seismic data, which may include:

[0006] Performing seismic interpretation on the seismic data of the target area to obtain the layer time corresponding to the strong reflection signal in the seismic data;

[0007] Based on the layer time corresponding to the strong reflection signal and a preset time window range, intercepting the first seismic data containing the strong reflection signal from the seismic data;

[0008] Linearly decomposing the first seismic data to obtain basis function components of single-channel seismic data in the first seismic data;

[0009] reconstructing a strong reflection signal of single-channel seismic data based on the basis function components;

[0010] The strong reflection signal in the seismic data is eliminated based on the reconstructed strong reflection signal.

[0011] Optionally, the linearly decomposing the first seismic data to obtain basis function components after decomposition of single-channel seismic data in the first seismic data may include:

[0012] According to the signal sparse decomposition theory, the first seismic data is linearly decomposed to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions;

[0013] The linear combination includes a plurality of basis function components, and the basis function is constructed based on the Ricker wavelet.

[0014] Optionally, the linear decomposition of the first seismic data based on the signal sparse decomposition theory to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions may include:

[0015] According to the signal sparse decomposition theory, R with a length of N N The first seismic data in the space is linearly decomposed to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions;

[0016] Where N is the number of sample points of single-channel seismic data, and the linear combination of multiple basis functions is as follows:

[0017]

[0018] In the formula, ψ q (q=1,2,...,Q) is R N A set of basis functions of the space, θ q (q=1,2,...,Q) is the sparse transformation coefficient of the basis function, ψ=[ψ1,...,ψ Q ] represents the basis function matrix, θ=[θ1,...,θ Q ] represents a vector of coefficients.

[0019] Optionally, the basis function constructed based on the Ricker wavelet is expressed in the time domain as:

[0020]

[0021] Where c is the center time of the basis function and f is the frequency.

[0022] Optionally, reconstructing the strong reflection signal of the single-channel seismic data based on the basis function components may include:

[0023] Based on the basis function components and the smoothing function of the strong reflection signal, the strong reflection signal of the single-channel seismic data is reconstructed.

[0024] Optionally, reconstructing the strong reflection signal of single-channel seismic data based on the basis function component and the smoothing function of the strong reflection signal may include:

[0025] Measuring the sparsity of the first seismic data based on the zero norm to obtain an objective function of sparse optimization of the first seismic data in a basis function matrix;

[0026] Determine an optimal basis function based on the objective function, and solve for sparse coefficients of the optimal basis function;

[0027] Based on the optimal basis function, the sparse coefficient and the smoothing function, a strong reflection signal of single-channel seismic data is reconstructed.

[0028] Optionally, determining an optimal basis function based on the objective function and solving a sparse coefficient of the optimal basis function may include:

[0029] Setting a threshold value based on the maximum amplitude value of the single-channel seismic data in the first seismic data;

[0030] Based on the threshold value, determining the number and position of extreme values ​​in the single-channel seismic data in the first seismic data;

[0031] Determine the number of basis functions based on the number of extreme values, and determine the central time variation range of the basis functions based on the extreme value positions;

[0032] Determine a main frequency of the first seismic data based on the first seismic data, and determine a frequency variation range of the basis function based on the main frequency and a preset frequency variation range;

[0033] Based on the maximum correlation coefficients between the basis functions at different extreme values ​​and the first seismic data, determining the frequency corresponding to the maximum correlation coefficient of the central time at each extreme value;

[0034] Determine an optimal basis function based on the center time and the frequency corresponding to the maximum correlation coefficient;

[0035] The sparse coefficients of the optimal basis function are solved based on the damped least squares method to determine the sparse coefficients of the optimal basis function.

[0036] In a second aspect, an embodiment of the present invention provides a device for eliminating strong reflection signals in seismic data, which may include:

[0037] An interpretation module, used for performing seismic interpretation on the seismic data of the target area to obtain the layer time corresponding to the strong reflection signal in the seismic data;

[0038] An interception module, configured to intercept first seismic data containing the strong reflection signal from the seismic data based on the layer time corresponding to the strong reflection signal and a preset time window range;

[0039] A decomposition module, used for linearly decomposing the first seismic data to obtain basis function components after decomposition of single-channel seismic data in the first seismic data;

[0040] A reconstruction module, used for reconstructing a strong reflection signal of a single-channel seismic data based on the basis function components;

[0041] The elimination module is used to eliminate the strong reflection signal in the seismic data based on the reconstructed strong reflection signal.

[0042] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for eliminating strong reflection signals in seismic data as described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for eliminating strong reflection signals in seismic data as described in the first aspect is implemented.

[0044] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0045] The embodiment of the present invention provides a method, device and equipment for eliminating strong reflection signals in seismic data. The method no longer uses a single matching atom to eliminate strong reflections, but uses multiple basis functions to match strong reflections. The method can not only effectively eliminate strong reflections, but also improve the problem of lateral discontinuity of seismic profiles after eliminating strong reflections. In practical applications, it can well eliminate the shielding effect of strong reflections, highlight weak signals of reservoirs, and improve the prediction accuracy of reservoirs, which is of great significance for seismic oil exploration and production.

[0046] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 A flow chart of a method for eliminating strong reflection signals in seismic data provided in an embodiment of the present invention;

[0050] Figure 2 A flowchart of a method for eliminating strong reflection signals in detailed seismic data provided in an embodiment of the present invention;

[0051] Figure 3 The specific execution flow chart of step S25;

[0052] Figure 4 is the strong reflection signal obtained based on the traditional matching pursuit strong reflection elimination method;

[0053] Figure 5 is a strong reflection signal obtained based on the method in the embodiment of the present invention;

[0054] Figure 6 A deep-domain coal seam strong reflection model provided in an embodiment of the present invention;

[0055] Figure 7 A seismic record synthesized by a 30 Hz Ricker wavelet provided in an embodiment of the present invention;

[0056] Figure 8 The simulation provided in the embodiment of the present invention Figure 6 Seismic records of real sand body response in the medium;

[0057] Fig. 9 This is the effect of eliminating strong reflection signals based on the traditional matching pursuit method

[0058] Fig.10 This is an effect diagram of a strong reflection signal based on the matching effect of multiple basis functions provided in an embodiment of the present invention;

[0059] Fig.11 The original seismic data of a certain work area provided in the embodiment of the present invention;

[0060] Fig.12 for Fig.11 The result of the conventional matching pursuit method after eliminating the strong reflection signal;

[0061] Fig.13 for Fig.11 The results of the strong reflection signal effect based on the embodiment of the present invention;

[0062] Fig.14 It is a schematic diagram of the structure of a device for eliminating strong reflection signals in seismic data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0064] The inventors have discovered that, since the matching pursuit algorithm is performed on single-channel seismic data, and each channel uses a single matching atom for matching elimination, when the seismic strong reflection waveform changes rapidly laterally, that is, when the strong reflection waveform of each channel of seismic data changes greatly, there will be a problem of incomplete elimination of strong reflections, and after eliminating the strong reflections, the seismic profile will become laterally discontinuous, thereby affecting the reservoir prediction of seismic data. In general, the traditional strong reflection elimination method is only suitable for the elimination of strong reflections in relatively stable lateral changes of formations, and is still insufficient for the elimination of strong reflections in complex areas with rapid lateral changes. In view of the above problems, the present invention is proposed to provide a method, device and equipment for eliminating strong reflection signals in seismic data that overcomes the above problems or at least partially solves the above problems, which has important significance for seismic exploration and production.

[0065] The embodiment of the present invention provides a method for eliminating strong reflection signals in seismic data. Figure 1 As shown, the method may include the following steps:

[0066] Step S11, performing seismic interpretation on the seismic data of the target area to obtain the layer time corresponding to the strong reflection signal in the seismic data. This step determines the position of the strong reflection signal based on the seismic interpretation.

[0067] Step S12: based on the layer time corresponding to the strong reflection signal and the preset time window range, intercept the first seismic data containing the strong reflection signal from the seismic data.

[0068] Step S13, linearly decompose the first seismic data to obtain the basis function components of the single-channel seismic data in the first seismic data. This step is based on the linear decomposition method to establish the basis function of the seismic data to determine the linear combination of the strong reflection signal to obtain the basis function components of the single-channel seismic data.

[0069] Step S14: reconstruct the strong reflection signal of the single-channel seismic data based on the basis function components.

[0070] During the specific implementation of this step, the strong reflection signal of the single-channel seismic data is reconstructed based on the basis function components and the smoothing function of the strong reflection signal.

[0071] In the embodiment of the present invention, step S13 and step S14 are based on the linear decomposition method to reconstruct the strong reflection signal in the single-channel seismic data; in the specific implementation, the data of all seismic channels in the first seismic data can be reconstructed at the same time to obtain the strong reflection signals of all single-channel seismic data respectively; or, the strong reflection signals of the single-channel seismic data can be reconstructed one by one, and then steps S13 and S14 are repeated to complete the reconstruction of all the single-channel seismic data contained in the first seismic data. The embodiment of the present invention does not make any specific limitation on this.

[0072] Step S15: Eliminate the strong reflection signal in the seismic data based on the reconstructed strong reflection signal.

[0073] The method for eliminating strong reflection signals in the above-mentioned seismic data provided by the embodiment of the present invention no longer uses a single matching atom to eliminate strong reflections, but uses multiple basis functions to match strong reflections. This method can not only effectively eliminate strong reflections, but also improve the problem of lateral discontinuity of seismic profiles after eliminating strong reflections. In practical applications, it can well eliminate the shielding effect of strong reflections, highlight weak signals of reservoirs, and improve the prediction accuracy of reservoirs, which is of great significance for seismic oil exploration and production.

[0074] In a detailed embodiment, the present invention provides a detailed method for eliminating strong reflection signals in seismic data, referring to Figure 2 As shown, the following steps may be included:

[0075] Step S21, performing seismic interpretation on the seismic data of the target area to obtain the layer time corresponding to the strong reflection signal in the seismic data.

[0076] The seismic data in this step is the original seismic data. For example, the input time range is T start ~T end The total Ntrace seismic data S original (i) (i = 1, 2, ... Ntrace), and interpret the layer time T(i) corresponding to the strong reflection seismic signal from the seismic data horizon (i=1,2,…Ntrace).

[0077] Step S22: based on the layer time corresponding to the strong reflection signal and the preset time window range, first seismic data containing the strong reflection signal is intercepted from the seismic data.

[0078] In this step, the layer time T(i) corresponding to the strong reflection seismic signal is used horizon (i=1,2,…Ntrace) as the center, open the upper and lower time windows T Up and T Down , the time window range is (T(i)horizon -T up )~(T(i) horizon +T down ) contains strong reflection seismic data, namely the first seismic data: S cut (i)(i=1,2,…Ntrace).

[0079] Step S23: linearly decompose the first seismic data according to the signal sparse decomposition theory, so as to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions.

[0080] In the specific implementation of this step, according to the signal sparse decomposition theory, the length of R N The first seismic data in the space is linearly decomposed to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions; in the embodiment of the present invention, the single-channel seismic data is decomposed into a linear combination of Q basis functions.

[0081] Where N is the number of sample points of single-channel seismic data, N = T Up +T Down +1, the linear combination of multiple basis functions is as follows:

[0082]

[0083] In the formula, ψ q (q=1,2,...,Q) is R N A set of basis functions of the space, θ q (q=1,2,...,Q) is the sparse transformation coefficient of the basis function, ψ=[ψ1,...,ψ Q ] represents the basis function matrix, θ=[θ1,...,θ Q ] represents a vector of sparse coefficients.

[0084] Wherein, the linear combination in this step includes multiple basis function components, and the basis function is constructed based on Ricker wavelet. In this step, the basis function is constructed using zero-phase Ricker wavelets of different center times and different frequencies in the first seismic data, wherein the expression of the Ricker wavelet basis function in the time domain is:

[0085]

[0086] Where c represents the center time of the basis function and f represents the frequency.

[0087] Step S24, measuring the sparsity of the first seismic data based on the zero norm to obtain the objective function of sparse optimization of the first seismic data in the basis function matrix.

[0088] This step uses the zero norm to measure the sparsity of seismic data, so for strong reflection seismic signals S cut (i) The objective function of the sparse optimization problem in the domain ψ is expressed as:

[0089]

[0090] Among them, δ is the threshold.

[0091] Step S25, determine the optimal basis function based on the objective function, and solve the sparse coefficients of the optimal basis function. This step is to seek the optimal basis function to solve and obtain the sparse coefficients of the optimal basis function.

[0092] In specific implementation, refer to Figure 3 As shown, the following steps may be included:

[0093] Step S251: Set a threshold value based on the maximum amplitude value of the single-channel seismic data in the first seismic data.

[0094] This step first calculates the single-channel strong reflection signal S cut (i) Maximum amplitude value V max , set the threshold value Threshold = V max *a1, the value range of a1 is generally (0.1~0.2).

[0095] Step S252: Based on the threshold value, determine the number and position of extreme values ​​in the single-channel seismic data in the first seismic data.

[0096] This step finds the first earthquake data S cut (i) The number of extreme values ​​n greater than Threshold and the size of the extreme values ​​V = [V1, V2…V n ] and the extreme position L=[L1,L2…L n ].

[0097] Step S253: determine the number of basis functions based on the number of extreme values, and determine the central time variation range of the basis functions based on the extreme value positions.

[0098] In this step, the number of basis functions ψ(u,ξ) is the number of extreme points n, and the central time variation range of the basis function ψ(c,f) is obtained as c=L=[L1, L2…L n ].

[0099] Step S254: determine the main frequency of the first seismic data based on the first seismic data, and determine the frequency variation range of the basis function based on the main frequency and a preset frequency variation range.

[0100] This step calculates the strong reflection signal S cut (i) The main frequency f main, set the frequency variation range f range , and the frequency variation range of the basis function ψ(c,f) is obtained:

[0101] f=[f main -f range ,f main -f range +1…f main +f range ]=[F1,F2…F m ]

[0102] Step S255: Based on the maximum correlation coefficients between the basis functions at different extreme values ​​and the first seismic data, determine the frequency corresponding to the maximum correlation coefficient at the center time of each extreme value.

[0103] Finally, the basis function ψ(c k ,f j )(k=1,2,…n;j=1,2,…m) and S cut (i) Maximum correlation coefficient:

[0104] |S cut T ψ(c k ,f)|=max|S cut T ψ(c k ,f)| (k=1,2,…n)

[0105] In the above formula, there are m basis functions at each extreme value, and there are also m correlation coefficients with the first seismic data. The maximum correlation coefficient is selected, and then the frequency corresponding to the maximum correlation coefficient at the center time of each extreme value is determined. It should be noted that the calculation method of the correlation coefficient between the basis function and the first seismic data in this step can refer to the existing correlation coefficient method, and the embodiment of the present invention does not specifically limit this.

[0106] In this step, each center time c is determined k The frequency corresponding to the maximum correlation coefficient at is:

[0107] f max_corr (k) (k = 1, 2, ... n), so the optimal basis function at each center position is:

[0108] ψ(c k ,f max_corr (k))(k=1,2,…n).

[0109] Step S256: Determine the optimal basis function based on the center time and the frequency corresponding to the maximum correlation coefficient.

[0110] There may be multiple optimal basis functions in this step, and the optimal basis function determined in this step is:

[0111] ψ(c k ,f max_corr (k))(k=1,2,…n)

[0112] Step S257: Solve the sparse coefficients of the optimal basis function based on the damped least squares method to determine the sparse coefficients of the optimal basis function.

[0113] The objective function of sparse optimization based on the first seismic data in the basis function st||θ||0≤δ, the optimal basis function ψ(c k ,f max_corr (k))(k=1,2,…n) sparse coefficient θ k The solution can be transformed into a damped least squares problem, namely:

[0114] θ k =[ψ T ψ+εI] -1 ψ T s cut (i)

[0115] Among them, ε is the damping coefficient, I is the unit matrix, and the sparse coefficient θ can be obtained by solving the above matrix equation using QR or singular value decomposition method. k .

[0116] Step S26: reconstruct the strong reflection signal of the single-channel seismic data based on the optimal basis function, the sparse coefficient and the smoothing function.

[0117] Get the sparse coefficient θ k Finally, in order to improve the lateral continuity after strong reflection removal, each optimal basis function ψ(c k ,f max_corr (k)) (k=1,2,…n) multiplied by the smoothing function λ k , smoothing function λ k is the basis function center time c k The extreme value V of the strong reflection signal at the position k Function:

[0118]

[0119] In the formula, R Low is the lower limit coefficient, generally taken as (0.5~0.8), R Up The upper limit coefficient is generally taken as (1.0~1.2).

[0120] Then the partial strong reflection signal can be expressed as:

[0121]

[0122] Step S27: Eliminate the strong reflection signal in the seismic data based on the reconstructed strong reflection signal.

[0123] In this step, the residual seismic signal after eliminating strong reflections from single-channel seismic data is:

[0124] S cut_residual (i) = S cut (i)-S cut_part_strong (i) (9)

[0125] In the embodiment of the present invention, the strong reflection is separated by iteration, that is, the total number of iterations Iter_number and the threshold Iter_threshold are set to V max *a2, a2 ​​value range is generally (0.1 ~ 0.2), when the number of iterations Iteration is less than Iter_number and S cut When the maximum value of (i) is greater than the threshold Iter_threshold, the residual signal S Residual (i) Continue separation, repeat the above steps S24 to S25, add the strong reflection signals separated each time, and finally obtain the strong reflection signal S of the whole channel. cut_strong (i).

[0126] That is, repeating the above steps can obtain strong reflection signals of all channels:

[0127] S cut_strong_all =[S cut_strong (1),S cut_strong (2) …S cut_strong (n)]

[0128] In the embodiment of the present invention, a filter function can also be used to filter the strong reflection signals S of all channels. strong_all Perform spatial smoothing to obtain the smoothed strong reflection signal S cut_strong_all_smooth Then, use the intercepted data S cut Subtract the strong reflection signal to get the data S after eliminating the strong reflection cut_nostrong_all , i.e., the seismic data after the strong radiation signal is eliminated (which may be referred to as the second seismic data in the embodiment of the present invention):

[0129] S cut_nostrong_all =S cut -S cut_strong_all_smooth

[0130] Finally, the second seismic data, that is, the strong reflection reduction data segment S cut_nostrong_all Instead of intercepting the intercepted data S containing strong reflection cut , put back the original earthquake data S original The seismic data S after the strong reflection is eliminated is obtained. nostrong_all.

[0131] In order to solve the problem that the traditional lateral fast-changing strong reflection elimination is not complete and the lateral continuity of seismic data is poor, the embodiment of the present invention proposes a strong reflection elimination method based on sparse decomposition according to the sparse decomposition theory of the signal. The method no longer uses a single matching atom to eliminate the strong reflection, but uses multiple basis functions to match the strong reflection. The method can better eliminate the strong reflection signal and improve the strong reflection elimination.

[0132] In the application of actual data, it can effectively eliminate the shielding effect of strong reflection, highlight the weak signal of reservoir, and improve the prediction accuracy of reservoir, which is of great significance for seismic oil exploration and production. Figure 4 The traditional matching pursuit strong reflection elimination method shown in Figure 5 From the comparison of the strong reflection elimination method based on the embodiment of the present invention, it can be seen that the method for eliminating strong reflection signals in seismic data provided in the embodiment of the present invention can well eliminate strong reflection signals and highlight weak reservoir signals.

[0133] In the embodiment of the present invention, in order to better test the effect of eliminating the strong reflection of the method in the horizontal variation, the following simulation is designed: Figure 6 The depth domain model shown has a background mudstone velocity of 3600 m / s and a density of 2.25 g / cm 3 The velocity of the low-speed coal seam is 2700m / s and the density is 1.80g / cm 3 The thickness varies in the horizontal direction, with a maximum thickness of 20m. The velocity of the reservoir sand body is 4000m / s and the density is 2.50g / cm 3 , thickness is 20m, and the distance from the bottom interface of the coal seam is 20m.

[0134] The seismic records in the time domain are synthesized using Ricker wavelets with a main frequency of 30 Hz, such as Figure 7 As shown in the figure, there are 50 seismic records in total, with a time range of 0-115ms. The horizontal lines in the figure are interpreted strong reflection seismic layers. It can be seen that due to the influence of the coal seam, the seismic response of the sand body is shielded by the strong reflection of the coal seam, and the amplitude and waveform of the strong reflection isotropic axis are unstable in the horizontal direction and change rapidly. Figure 8 It is a simulated real sand body response, and on the profile it is a relatively stable and continuous seismic isotropic axis. Fig. 9 This is the result of conventional matching pursuit strong reflection elimination. Fig.10 This is the result of strong reflection elimination of this method. The time window of this method is set as T Up =15ms, the lower time window is T Down 20ms. The preset frequency range is 2Hz. The lower limit coefficient is R Low =0.75, the upper limit coefficient is RUp =1.1. Iter_number = 3; damping coefficient is 0.01; a1 = 0.1; a2 = 0.1; comparison Fig. 9 and Fig.10 It can be seen that the conventional method does not completely eliminate strong reflections, and the lateral continuity is poor after elimination, while the present method basically eliminates strong reflections and has good lateral continuity.

[0135] In the embodiment of the present invention, in order to further test the elimination effect of the strong reflection signal elimination method in the above seismic data on the elimination of lateral strong reflection, the actual data of a certain work area are used for application verification. The Longtan Formation overlying the Maokou Formation is a low-speed mudstone stratum, while the Maokou Formation is a high-speed limestone stratum. Due to the large difference in wave impedance, a strong reflection P2L is formed at the top interface of the Maokou Formation, and the weak reflection of the dolomite reservoir of the Maokou Formation is replaced by the strong reflection.

[0136] Radiation shielding increases the difficulty of reservoir prediction.

[0137] The original earthquake record is as follows Fig.11 As shown in the figure, the time range is 2500-2800ms, and the lateral line is the interpreted strong reflection seismic layer. It can be seen that due to the shielding effect of the low-velocity shale layer, the weak information of the dolomite reservoir is shielded by the strong reflection of the shale layer, such as Fig.11 As shown in the ellipse, the amplitude and waveform of the strong reflection co-axial are unstable in the horizontal direction and change rapidly. Fig.12 This is the result of conventional matching pursuit strong reflection elimination. It can be seen that the conventional method does not eliminate strong reflections thoroughly, such as Fig.12 As shown in the box in , the lateral continuity of the entire seismic profile is also poor after elimination. Fig.13 This is the result of strong reflection elimination of this method. The time window of this method is set as T Up =20ms, the lower time window is T Down 30ms. The preset frequency range is 3Hz. The lower limit coefficient is R Low =0.70, the upper limit coefficient is R Up =1.15. Iteration number Iter_number = 3; Damping coefficient is 0.01; a1 = 0.12; a2 = 0.1; Comparison Fig.11 and Fig.12 It can be seen that this method basically eliminates the strong reflection, highlights the weak information of the dolomite reservoir, and has better lateral continuity, such as Fig.12 The box and ellipse are shown in Figure 2.

[0138] Based on the same inventive concept, an embodiment of the present invention further provides a device for eliminating strong reflection signals in seismic data, referring to Fig.14As shown, the device may include: an interpretation module 11, an interception module 12, a decomposition module 13, a reconstruction module 14 and an elimination module 15, and its working principle is as follows:

[0139] The interpretation module 11 is used to perform seismic interpretation on the seismic data of the target area to obtain the layer time corresponding to the strong reflection signal in the seismic data;

[0140] The interception module 12 is used to intercept the first seismic data containing the strong reflection signal in the seismic data based on the layer time corresponding to the strong reflection signal and the preset time window range;

[0141] The decomposition module 13 is used to perform linear decomposition on the first seismic data to obtain basis function components after decomposition of single-channel seismic data in the first seismic data;

[0142] The reconstruction module 14 is used to reconstruct the strong reflection signal of the single-channel seismic data based on the basis function components;

[0143] The elimination module 15 is used to eliminate the strong reflection signal in the seismic data based on the reconstructed strong reflection signal.

[0144] In an optional embodiment, the decomposition module 13 is specifically used for:

[0145] According to the signal sparse decomposition theory, the first seismic data is linearly decomposed to convert the first

[0146] Single-channel seismic data in seismic data is decomposed into a linear combination of multiple basis functions;

[0147] The linear combination includes a plurality of basis function components, and the basis function is constructed based on the Ricker wavelet.

[0148] In another optional embodiment, the decomposition module 13 is further configured to:

[0149] According to the signal sparse decomposition theory, R with a length of N N The first seismic data in the space is linearly decomposed to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions;

[0150] Where N is the number of sample points of single-channel seismic data, and the linear combination of multiple basis functions is as follows:

[0151]

[0152] In the formula, ψ q (q=1,2,...,Q) is R N A set of basis functions of the space, θ q (q=1,2,...,Q) is the sparse transformation coefficient of the basis function, ψ=[ψ1,...,ψQ ] represents the basis function matrix, θ=[θ1,...,θ Q ] represents a vector of coefficients.

[0153] In another optional embodiment, the reconstruction module 14 is used to reconstruct the strong reflection signal of the single-channel seismic data based on the basis function components and the smoothing function of the strong reflection signal.

[0154] In another optional embodiment, the reconstruction module 14 is specifically used for:

[0155] Measuring the sparsity of the first seismic data based on the zero norm to obtain an objective function of sparse optimization of the first seismic data in a basis function matrix;

[0156] Determine an optimal basis function based on the objective function, and solve for sparse coefficients of the optimal basis function;

[0157] Based on the optimal basis function, the sparse coefficient and the smoothing function, a strong reflection signal of single-channel seismic data is reconstructed.

[0158] In another optional embodiment, the reconstruction module 14 is further configured to:

[0159] Setting a threshold value based on the maximum amplitude value of the single-channel seismic data in the first seismic data;

[0160] Based on the threshold value, determining the number and position of extreme values ​​in the single-channel seismic data in the first seismic data;

[0161] Determine the number of basis functions based on the number of extreme values, and determine the central time variation range of the basis functions based on the extreme value positions;

[0162] Determine the main frequency of the first seismic data based on the first seismic data, and

[0163] A preset frequency variation range is used to determine the frequency variation range of the basis function;

[0164] Based on the maximum correlation coefficients between the basis functions at different extreme values ​​and the first seismic data, determining the frequency corresponding to the maximum correlation coefficient of the central time at each extreme value;

[0165] Determine an optimal basis function based on the center time and the frequency corresponding to the maximum correlation coefficient;

[0166] The sparse coefficients of the optimal basis function are solved based on the damped least squares method to determine the sparse coefficients of the optimal basis function.

[0167] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for eliminating strong reflection signals in the seismic data described above is implemented.

[0168] Based on the same inventive concept, a computer device is also provided in an embodiment of the present invention, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for eliminating strong reflection signals in the above-mentioned seismic data is implemented.

[0169] The principles of the problems solved by the above-mentioned devices, media, and related equipment in the embodiments of the present invention are similar to those of the above-mentioned methods, so their implementation can refer to the implementation of the above-mentioned methods, and the repeated parts will not be repeated.

[0170] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0171] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0174] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for eliminating strong reflection signals in seismic data, characterized in that: include: Performing seismic interpretation on the seismic data of the target area to obtain the layer time corresponding to the strong reflection signal in the seismic data; Based on the layer time corresponding to the strong reflection signal and a preset time window range, intercepting the first seismic data containing the strong reflection signal from the seismic data; Linearly decomposing the first seismic data to obtain basis function components of single-channel seismic data in the first seismic data; reconstructing a strong reflection signal of single-channel seismic data based on the basis function components; The strong reflection signal in the seismic data is eliminated based on the reconstructed strong reflection signal.

2. The method according to claim 1, characterized in that The linearly decomposing the first seismic data to obtain basis function components of the decomposed single-channel seismic data in the first seismic data includes: According to the signal sparse decomposition theory, the first seismic data is linearly decomposed to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions; The linear combination includes a plurality of basis function components, and the basis function is constructed based on the Ricker wavelet.

3. The method according to claim 2, characterized in that The method of linearly decomposing the first seismic data based on the signal sparse decomposition theory to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions includes: According to the signal sparse decomposition theory, R with a length of N N The first seismic data in the space is linearly decomposed to decompose the single-channel seismic data in the first seismic data into a linear combination of multiple basis functions; Where N is the number of sample points of single-channel seismic data, and the linear combination of multiple basis functions is as follows: In the formula, ψ q (q=1,2,...,Q) is R N A set of basis functions of the space, θ q (q=1,2,...,Q) is the sparse transformation coefficient of the basis function, ψ=[ψ1,...,ψ Q ] represents the basis function matrix, θ=[θ1,...,θ Q ] represents a vector of coefficients.

4. The method according to claim 3, characterized in that The expression of the basis function constructed based on the Ricker wavelet in the time domain is: Where c is the center time of the basis function and f is the frequency.

5. The method according to claim 4, characterized in that The method of reconstructing a strong reflection signal of a single-channel seismic data based on the basis function component comprises: Based on the basis function components and the smoothing function of the strong reflection signal, the strong reflection signal of the single-channel seismic data is reconstructed.

6. The method according to claim 5, characterized in that The reconstructing the strong reflection signal of the single-channel seismic data based on the basis function component and the smoothing function of the strong reflection signal comprises: Measuring the sparsity of the first seismic data based on the zero norm to obtain an objective function of sparse optimization of the first seismic data in a basis function matrix; Determine an optimal basis function based on the objective function, and solve for sparse coefficients of the optimal basis function; Based on the optimal basis function, the sparse coefficient and the smoothing function, a strong reflection signal of single-channel seismic data is reconstructed.

7. The method according to claim 6, characterized in that The determining of the optimal basis function based on the objective function and solving the sparse coefficients of the optimal basis function includes: Setting a threshold value based on the maximum amplitude value of the single-channel seismic data in the first seismic data; Based on the threshold value, determining the number and position of extreme values ​​in the single-channel seismic data in the first seismic data; Determine the number of basis functions based on the number of extreme values, and determine the central time variation range of the basis functions based on the extreme value positions; Determine a main frequency of the first seismic data based on the first seismic data, and determine a frequency variation range of the basis function based on the main frequency and a preset frequency variation range; Based on the maximum correlation coefficients between the basis functions at different extreme values ​​and the first seismic data, determining the frequency corresponding to the maximum correlation coefficient of the central time at each extreme value; Determine an optimal basis function based on the center time and the frequency corresponding to the maximum correlation coefficient; The sparse coefficients of the optimal basis function are solved based on the damped least squares method to determine the sparse coefficients of the optimal basis function.

8. A device for eliminating strong reflection signals in seismic data, characterized in that: include: An interpretation module, used for performing seismic interpretation on the seismic data of the target area to obtain the layer time corresponding to the strong reflection signal in the seismic data; An interception module, configured to intercept first seismic data containing the strong reflection signal from the seismic data based on the layer time corresponding to the strong reflection signal and a preset time window range; A decomposition module, used for linearly decomposing the first seismic data to obtain basis function components after decomposition of single-channel seismic data in the first seismic data; A reconstruction module, used for reconstructing a strong reflection signal of a single-channel seismic data based on the basis function components; The elimination module is used to eliminate the strong reflection signal in the seismic data based on the reconstructed strong reflection signal.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for eliminating strong reflection signals in seismic data according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for eliminating strong reflection signals in seismic data according to any one of claims 1 to 7 is implemented.

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