Seismic record processing method and system, electronic equipment and storage medium

Abstract

The invention provides a seismic record processing method and system, electronic equipment and a storage medium. The method comprises the following steps: S1, spectrum analysis: calculating a logarithmic power spectrum of an input seismic record by adopting an autocorrelation method; s2, spectrum decomposition: decomposing the logarithmic power spectrum into five groups of component logarithmic power spectrums of a shot point, a detection point, a common midpoint, an offset distance and a global term by adopting a super-relaxation Jacobian iterative algorithm; obtaining an error spectrum through a difference value between the logarithmic power spectrum and a model logarithmic power spectrum; and S3, applying a deconvolution operator: converting the five groups of component logarithmic power spectrums and the error spectrum into autocorrelation coefficients, and obtaining a seismic record by using a prediction deconvolution method and an earth surface consistency amplitude compensation method. According to the invention, the longitudinal resolution of the seismic record can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-resolution processing of seismic records, and particularly relates to a seismic record processing method, system, electronic device and storage medium. Background Technique

[0002] Currently, deconvolution is an effective means to improve the vertical resolution of seismic records. Existing deconvolution methods mainly include least-squares deconvolution, impulse deconvolution, predictive deconvolution [1-2] , blind deconvolution [3-4] , surface-consistent deconvolution [5-8] . Among them, surface-consistent deconvolution is the most widely used. Surface-consistent deconvolution can not only eliminate the earth filtering effect, compress the seismic wavelet, but also eliminate the influence of excitation, reception and near-surface differences on the seismic wavelet waveform, and enhance the waveform consistency of seismic traces.

[0003] Although surface-consistent deconvolution has many advantages in improving the vertical resolution of seismic records, surface-consistent deconvolution decomposes different wavelet components by the least-squares method, and the solved predictive deconvolution operator is easily affected by noise, resulting in an increase in the energy of low-frequency surface waves and high-frequency random noise after surface-consistent deconvolution. To make up for the deficiencies of surface-consistent deconvolution, a large amount of time and effort are required to suppress noise, which limits its practicality. At the same time, overemphasis on high resolution will reduce the amplitude preservation of the processing results. Therefore, researching a surface-consistent constrained comprehensive deconvolution method with strong anti-noise ability and strong amplitude preservation is the development trend.

[0004] Through literature retrieval, there are 2 articles closely related to the present invention. Xu Leiming and Li Mei used the robust deconvolution method in Omega2 to conduct an application study on improving the resolution of seismic records in Well ZhaoX in the Sulige Gas Field [9]. The application results of actual records show that after using the robust deconvolution method, the resolution of seismic records is improved, which is better than the conventional surface-consistent deconvolution method used in the past, providing a data basis for the characterization of Paleozoic sand bodies, reservoir prediction and groove identification in this area. However, this literature mainly tests the existing robust deconvolution method in Omega2 and conducts a comparative analysis of the test results. Gao Qian et al. proposed a robust sparse deconvolution method that can overcome the interference of outliers

[10] . This method can model the outlier noise with heavy-tailed distribution and sparse reflection coefficients, and use the alternating iteration and linear programming solution algorithm. Experiments prove that this method can overcome the outlier noise and can realize the synchronous estimation of seismic wavelets and reflection coefficients. The obtained estimation can effectively eliminate the influence of heavy-tailed distribution outlier noise and improve the accuracy of seismic signal deconvolution processing. This deconvolution method is an improved Bayesian deconvolution method, which is only used to eliminate the influence of outlier noise, while the present invention is related to surface-consistent deconvolution and can not only eliminate the influence of outlier noise, but also process non-consistent noise.

[0005] The above technical problems need to be solved urgently. Summary of the Invention

[0006] To solve the above technical problems, the present invention proposes a seismic record processing method, system, electronic device and storage medium to solve the above technical problems.

[0007] The first aspect of the present invention discloses a seismic record processing method, and the method includes:

[0008] Step S1, spectral analysis: calculating the logarithmic power spectrum of the input seismic record by using the autocorrelation method;

[0009] Step S2, spectral decomposition: decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms by using the successive over-relaxation Jacobi iterative algorithm; obtaining the error spectrum through the difference between the logarithmic power spectrum and the model logarithmic power spectrum;

[0010] Step S3, deconvolution operator application: converting the five groups of component logarithmic power spectra and the error spectrum into autocorrelation coefficients, and obtaining the seismic record by using the predictive deconvolution method and the surface consistent amplitude compensation method.

[0011] According to the method of the first aspect of the present invention, in the step S1, it further includes calculating weights according to the autocorrelation length and the seismic record length, and establishing a network to obtain the system matrix information by reading the header information of the seismic record.

[0012] According to the method of the first aspect of the present invention, in the step S2, the method of decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms by using the successive over-relaxation Jacobi iterative algorithm includes:

[0013] Step S21: decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms by minimizing the defined objective function;

[0014] Step S22: calculating the objective function by using the objective function formula;

[0015] Step S23: making the partial derivatives of all quantities to be solved with respect to the objective function equal to 0 to obtain the equation to be solved;

[0016] Step S24: solving the equation to be solved by using the successive over-relaxation Jacobi iterative algorithm to obtain the five groups of component logarithmic power spectra.

[0017] According to the method of the first aspect of the present invention, in the step S2, after the method of obtaining the error spectrum by the difference between the logarithmic power spectrum and the model logarithmic power spectrum, the following steps are further included: obtaining the error spectrum of each seismic trace, comparing the error spectrum of each seismic trace with the overall error distribution, examining whether there is excessive non-uniform noise, and marking the seismic traces with large noise.

[0018] According to the method of the first aspect of the present invention, in the step S3, the predictive deconvolution method includes constructing a predictive deconvolution equation by using the autocorrelation of five groups of components and obtaining a predictive deconvolution operator by using the Levinson algorithm.

[0019] According to the method of the first aspect of the present invention, in the step S3, the predictive deconvolution method includes constructing the predictive deconvolution equation by using the autocorrelation of the error spectrum and obtaining a capture operator by using the Levinson algorithm.

[0020] According to the method of the first aspect of the present invention, in the step S3, the surface consistent amplitude compensation method includes:

[0021] Step S31: Calculating the root mean square amplitude value of each seismic trace;

[0022] Step S32: Separately counting the amplitude energy of the shot point, geophone point, offset and common midpoint components of each seismic trace, calculating the amplitude influence of each component on the original seismic trace, and obtaining a compensation factor;

[0023] Step S33: Applying the compensation factor to perform amplitude compensation on the seismic trace.

[0024] The second aspect of the present invention discloses a system for seismic record processing, and the system includes:

[0025] A first processing module, configured to perform spectral analysis: calculating the logarithmic power spectrum of the input seismic record by using the autocorrelation method;

[0026] A second processing module, configured to perform spectral decomposition: decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot point, geophone point, common midpoint, offset, and global term by using the successive over-relaxation Jacobi iterative algorithm; obtaining an error spectrum by the difference between the logarithmic power spectrum and the model logarithmic power spectrum;

[0027] A third processing module, configured to apply a deconvolution operator: converting the five groups of component logarithmic power spectra and the error spectrum into autocorrelation coefficients, and obtaining a seismic record by using the predictive deconvolution method and the surface consistent amplitude compensation method.

[0028] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in a seismic record processing method according to any one of the first aspects of the present disclosure are implemented.

[0029] The fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a seismic record processing method according to any one of the first aspects of the present disclosure are implemented.

[0030] In summary, the solution proposed by the present invention improves the vertical resolution of seismic records by developing a surface-consistent constrained synthetic deconvolution technology with strong anti-noise ability and amplitude preservation ability, from aspects such as compressing seismic wavelets and enhancing the waveform consistency of wavelets, and can solve the problems of poor anti-noise ability and weak amplitude preservation of conventional surface-consistent deconvolution methods. Specifically, the solution proposed by the present invention can:

[0031] (a) Solve a predictive deconvolution operator that is not affected by noise in the case of very strong non-surface-consistent noise;

[0032] (b) Output a relatively amplitude-preserved result while improving the resolution of seismic records;

[0033] (c) Automatically suppress abnormal amplitude noise in non-surface-consistent noise regions. Description of the Drawings

[0034] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 It is a flowchart of a seismic record processing method according to an embodiment of the present invention;

[0036] Figure 2 It is a schematic diagram of the research roadmap of surface-consistent constrained synthetic deconvolution according to an embodiment of the present invention;

[0037] Figure 3 It is a schematic diagram of the principle of the predictive deconvolution method according to an embodiment of the present invention;

[0038] Figure 4 It is a comparison effect diagram of seismic records before and after deconvolution of two-dimensional simple model data according to an embodiment of the present invention;

[0039] Figure 5The comparison effect diagram of seismic records before and after deconvolution of two-dimensional frequency conversion model data according to an embodiment of the present invention;

[0040] Figure 6 The comparison effect diagram of the shot point autocorrelation difference section before and after deconvolution of two-dimensional frequency conversion model data according to an embodiment of the present invention;

[0041] Figure 7 The comparison effect diagram of seismic records before and after deconvolution of three-dimensional measured data according to an embodiment of the present invention;

[0042] Figure 8 The comparison effect diagram of the wavelet waveform consistency before and after deconvolution of three-dimensional measured data according to an embodiment of the present invention;

[0043] Figure 9 The comparison effect diagram of the deconvolution results before and after applying the capture operator to three-dimensional measured data according to an embodiment of the present invention;

[0044] Figure 10 The schematic diagram of the time window design module according to an embodiment of the present invention;

[0045] Figure 11 The schematic diagram of the sub-region application module according to an embodiment of the present invention;

[0046] Figure 12 The schematic diagram of the result output types and port descriptions according to an embodiment of the present invention;

[0047] Figure 13 The structural diagram of a seismic record processing system according to an embodiment of the present invention;

[0048] Figure 14 The structural diagram of an electronic device according to an embodiment of the present invention. Specific embodiments

[0049] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] The first aspect of the present invention discloses a seismic record processing method. Figure 1 The flowchart of a seismic record processing method according to an embodiment of the present invention, Figure 2 The schematic diagram of the research roadmap of surface consistency constrained comprehensive deconvolution according to an embodiment of the present invention, as Figure 1-2 shown, the method includes:

[0051] Step S1, spectral analysis: Calculate the logarithmic power spectrum of the input seismic record using the autocorrelation method;

[0052] Step S2, spectral decomposition: Decompose the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms using the successive over-relaxation Jacobi iterative algorithm; Obtain the error spectrum through the difference between the logarithmic power spectrum and the model logarithmic power spectrum;

[0053] Step S3, deconvolution operator application: Convert the five groups of component logarithmic power spectra and the error spectrum into autocorrelation coefficients, and obtain the seismic record using the predictive deconvolution method and the surface consistent amplitude compensation method.

[0054] In step S1, spectral analysis: Calculate the logarithmic power spectrum of the input seismic record using the autocorrelation method.

[0055] In some embodiments, in step S1, it further includes calculating weights according to the autocorrelation length and the seismic record length, and establishing a network to obtain the system matrix information by reading the header information of the seismic record.

[0056] In step S2, spectral decomposition: Decompose the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms using the successive over-relaxation Jacobi iterative algorithm; Obtain the error spectrum through the difference between the logarithmic power spectrum and the model logarithmic power spectrum.

[0057] In some embodiments, in step S2, the method of decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms using the successive over-relaxation Jacobi iterative algorithm includes:

[0058] Step S21: Decompose the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms by minimizing the defined objective function;

[0059] Step S22: Calculate the objective function using the objective function formula;

[0060] Step S23: Set the partial derivatives of the objective function with respect to all quantities to be solved to 0 to obtain the equation to be solved;

[0061] Step S24: Solve the equation to be solved using the successive over-relaxation Jacobi iterative algorithm to obtain the five groups of component logarithmic power spectra.

[0062] In some embodiments, in step S2, after obtaining the error spectrum by the difference between the logarithmic power spectrum and the model logarithmic power spectrum, the following steps are further included: obtaining the error spectrum of each seismic trace, comparing the error spectrum of each seismic trace with the overall error distribution, examining whether there is excessive non-uniform noise, and marking the seismic traces with large noise.

[0063] Specifically, in the spectral decomposition stage, due to the inconsistency of the surface, the over-relaxed Jacobi iterative algorithm is used to decompose the logarithmic power spectrum into five groups of component logarithmic power spectra of shot point, geophone point, common midpoint, offset, and global term. In the case of strong non-uniform noise, this algorithm is more robust and faster than the least squares method and can produce results unaffected by noise. At the same time, the error spectrum is obtained by the difference between the logarithmic power spectrum and the model logarithmic power spectrum, and introducing the error spectrum can suppress the abnormal amplitude noise in the non-surface-consistent noise area.

[0064] The method of using the over-relaxed Jacobi iterative algorithm to decompose the logarithmic power spectrum into five groups of component logarithmic power spectra of shot point, geophone point, common midpoint, offset, and global term is specifically as follows:

[0065] The conventional surface-consistent deconvolution method uses the least squares method for spectral decomposition, and the decomposition result is easily affected by noise, resulting in the elevation of the low-frequency surface wave and high-frequency noise energy in the deconvolved seismic record, reducing the signal-to-noise ratio of the seismic record. The over-relaxed Jacobi iterative algorithm can decompose the logarithmic power spectrum into five groups of component logarithmic power spectra of shot point, geophone point, common midpoint, offset, and global term by minimizing the objective function in the case of strong noise.

[0066] J=min(||L(trace)-(L(src)+L(det)+L(cmp)+L(offset)+L(global))|| 2 ),

[0067] Lm(trace)=L(src)+L(det)+L(cmp)+L(offset)+L(global),

[0068] In the formula, J is the defined objective function, L(trace) is the logarithmic power spectrum obtained by spectral analysis, L(src), L(det), L(cmp), L(offset), and L(global) are the logarithmic power spectra of the five groups of components of shot point, geophone point, common midpoint, offset, and global term respectively, and Lm(trace) is the logarithmic power spectrum of the deconvolution model. The objective function formula is:

[0069]

[0070] Wherein, w is the weight, M is the system matrix, x is the quantity to be solved, b is the logarithmic power spectrum, i is the number of channels, j is the number of components, and h is the regularization term coefficient. The necessary condition for minimization is that the objective function J(x) with respect to all quantities x to be solved j has a partial derivative of 0, that is

[0071]

[0072] From this, the equation to be solved can be obtained as:

[0073] M t WMx = M t Wb,

[0074] The over-relaxed Jacobi iterative algorithm is used to solve this equation:

[0075]

[0076] Wherein, x 0 is the initial value of the quantity to be solved, which can be any matrix, and x n+1 is the result of the (n + 1)-th iteration, and ω is the over-relaxation factor.

[0077] The error spectrum is the difference between the logarithmic power spectrum and the model logarithmic power spectrum, that is

[0078] E(trace) = L(trace) - Lm(trace).

[0079] Wherein, L(trace) is the logarithmic power spectrum obtained by spectral analysis, and Lm(trace) is the logarithmic power spectrum of the deconvolution model. At the spectral decomposition stage, the error spectrum of each seismic trace is obtained, and then the error spectrum of each seismic trace is compared with the overall error distribution to examine whether there is excessive non-uniform noise, and the seismic traces with large noise are marked.

[0080] In step S3, the deconvolution operator is applied: The five groups of component logarithmic power spectra and error spectra are converted into autocorrelation coefficients, and the seismic record is obtained by using the predictive deconvolution method and the surface-consistent amplitude compensation method.

[0081] In some embodiments, in step S3, the predictive deconvolution method includes constructing a predictive deconvolution equation by using the autocorrelation of the five groups of components, and obtaining the predictive deconvolution operator by using the Levinson algorithm.

[0082] In some embodiments, in step S3, the predictive deconvolution method includes constructing a predictive deconvolution equation by using the autocorrelation of the error spectrum, and obtaining the capture operator by using the Levinson algorithm.

[0083] In some embodiments, in step S3, the surface-consistent amplitude compensation method includes:

[0084] Step S31: Calculate the root-mean-square amplitude value of each seismic trace;

[0085] Step S32: Statistically analyze the amplitude energy of the shot point, geophone point, offset, and common midpoint components of each seismic trace respectively, calculate the amplitude influence of each component on the original seismic trace, and obtain the compensation factor;

[0086] Step S33: Apply the compensation factor to perform amplitude compensation on the seismic trace.

[0087] Specifically, in the deconvolution operator application stage, convert the logarithmic power spectra and error spectra of the five groups of components output by the spectral decomposition module into autocorrelation coefficients, use the predictive deconvolution method to calculate the predictive deconvolution operator and the capture operator, convolve the predictive deconvolution operator and the capture operator with the original seismic record to obtain the deconvolved seismic record. Perform surface-consistent amplitude compensation on the deconvolved seismic record and output the amplitude-preserved seismic record.

[0088] In the deconvolution operator application stage, use the predictive deconvolution method to calculate the predictive deconvolution operator and the capture operator. As Figure 3 shown, Figure 3 is a schematic diagram of the principle of the predictive deconvolution method according to an embodiment of the present invention. Figure 3 The equation in it is the predictive deconvolution equation. The left side of the predictive deconvolution equation is a toplietz matrix composed of autocorrelations, and the autocorrelation matrix on the right side of the predictive deconvolution equation is known. Use the Levinson algorithm to solve the predictive deconvolution equation to obtain the prediction factor, and calculate the predictive deconvolution operator c(l) according to the prediction factor. Figure 3 In it, α is the prediction step size and m is the operator length.

[0089] The process of obtaining the capture operator is the same as that of the predictive deconvolution operator, except that when obtaining the predictive deconvolution operator, use the autocorrelations of the five groups of components to construct the predictive deconvolution equation; when obtaining the capture operator, use the autocorrelation of the error spectrum to construct the predictive deconvolution equation. The capture operator can transform the spectrum of the marked trace to the spectral center position of the adjacent deconvolved seismic trace. Doing so can not only effectively process abnormal wavelets but also reduce the influence of additional noise.

[0090] After the predictive deconvolution operator and the capture operator are convolved with the original seismic record, applying surface-consistent amplitude compensation can improve the amplitude continuity and output a relatively amplitude-preserved result. Surface-consistent amplitude compensation can eliminate the influence of the changes in shot points, geophone points, offsets, and common midpoints during data acquisition on the seismic wave amplitude. The specific implementation process includes three steps: First, calculate the root mean square amplitude value of each seismic trace; Second, separately count the amplitude energy of the shot point, geophone point, offset, and common midpoint components of each seismic trace, calculate the amplitude influence of each component on the original seismic trace, and obtain the compensation factor; Finally, apply the compensation factor to perform amplitude compensation on the seismic trace.

[0091] In summary, the solution proposed by the present invention improves the vertical resolution of seismic records in terms of compressing seismic wavelets and enhancing the waveform consistency of wavelets by developing a surface-consistent constrained comprehensive deconvolution technology with strong anti-noise ability and amplitude preservation ability, and can solve the problems of poor anti-noise ability and weak amplitude preservation of conventional surface-consistent deconvolution methods. Specifically, the solution proposed by the present invention can:

[0092] (a) Solve the predictive deconvolution operator that is not affected by noise in the case of very strong non-surface-consistent noise;

[0093] (b) Output a relatively amplitude-preserved result while improving the resolution of seismic records;

[0094] (c) Automatically suppress the abnormal amplitude noise in the non-surface-consistent noise area.

[0095] Example 1

[0096] To verify the effect of the solution proposed by the present invention, the solution of the present invention was used to perform deconvolution on three sets of seismic records. The deconvolution effect is as Figures 4-9 shown, and the results are as follows:

[0097] The first set is a 2D simple model seismic record, and its wavelet is a pulse signal with a main frequency of 20 Hz. The deconvolution result of this data is as Figure 4 shown, Figure 4 the left figure of Figure 4 is the seismic record before deconvolution, and the right figure of

[0098] is the seismic record after deconvolution. Compared with the seismic wavelet before deconvolution, the seismic wavelet after deconvolution is significantly compressed, similar to a pulse signal, and the event axis is clearer, with higher resolution. Figure 5 shown, Figure 5 the left figure of Figure 5The right figure shows the seismic record after deconvolution. Compared with the seismic wavelet before deconvolution, the seismic wavelet after deconvolution has also been significantly compressed, and the amplitude distribution of each seismic wavelet is more uniform. Figure 6 It shows the comparison result of the wavelet waveform consistency before and after deconvolution of two-dimensional frequency-variable model data. Figure 6 The left figure of [Figure number] is the shot autocorrelation difference profile before deconvolution. Figure 6 The right figure of [Figure number] is the shot autocorrelation difference profile after deconvolution, and each seismic wavelet shows the corresponding shot autocorrelation data. From Figure 6 it can be seen that the wavelet before deconvolution has relatively wide side lobes and obvious waveform differences. In contrast, the wavelet after deconvolution is significantly compressed, the side lobes disappear, and its waveform has good consistency.

[0099] The third set is the three-dimensional common-shot gather measured data, and the deconvolution result of this data is as Figure 7 shown. Figure 7 The left figure of [Figure number] is the seismic record before deconvolution. Figure 7 The right figure of [Figure number] is the seismic record after deconvolution. From Figure 7 it can be seen that the surface-consistent constrained comprehensive deconvolution proposed by the present invention can significantly improve the continuity, clarity and amplitude distribution of the in-phase axis, thereby improving the resolution of the seismic record. Figure 8 It shows the comparison result of the wavelet waveform consistency before and after deconvolution of three-dimensional measured data. Figure 8 The upper figure of [Figure number] is the geophone autocorrelation difference profile before deconvolution. Figure 8 The lower figure of [Figure number] is the geophone autocorrelation difference profile after deconvolution, and each seismic wavelet shows its geophone autocorrelation data. From Figure 8 it can be seen that the waveform consistency of each seismic wavelet before deconvolution is poor, there are obvious amplitude differences and relatively wide side lobes, while the waveform consistency of each seismic wavelet after deconvolution is better, the amplitude is uniform, and the side lobes are significantly suppressed.

[0100] Figure 9 It shows the deconvolution results of three-dimensional measured data before and after applying the capture operator. Figure 9 The left figure of [Figure number] is the deconvolution result before applying the capture operator. Figure 9 The middle figure of [Figure number] is the deconvolution result after applying the capture operator. Figure 9 The right figure of [Figure number] is the comparison result of the deconvolution result before applying the capture operator and the deconvolution result after applying the capture operator. From Figure 9 it can be seen that the capture operator can well automatically suppress the abnormal amplitude noise in the non-surface-consistent noise area and improve the signal-to-noise ratio and resolution of the deconvolution result.

[0101] Example 2

[0102] As Figures 10-12As shown in the figure, in order to achieve better invention effects, the present invention provides a time window design function in the spectral analysis stage and a sub-region application function in the deconvolution operator application stage. The time window design function module is as Figure 10 shown, and it is necessary to set the number of time windows, the length of the time window, and the autocorrelation length. According to different time window settings, the logarithmic power spectrum and weight corresponding to each time window are calculated respectively.

[0103] The sub-region application module is as Figure 11 shown, and it includes the setting of the number of regions, the setting of the action length of each region, the design of the predictive deconvolution operator, and the setting of the capture operator parameters. According to different partition settings, the deconvolution results and intermediate results of each application region are calculated respectively. There are a total of 13 total output results, which are output at different ports, as Figure 12 shown. The deconvolution result is an item that must be output and is output at port 1. Other intermediate results can be selectively output. Among them, the autocorrelation coefficients of five groups of components are output at ports 2 - 6, the predictive deconvolution operators of five groups of components are output at ports 7 - 11, the autocorrelation coefficient of the error spectrum is output at port 12, and the capture operator is output at port 13.

[0104] The second aspect of the present invention discloses a seismic record processing system. Figure 13 It is a structural diagram of a seismic record processing system according to an embodiment of the present invention; as Figure 13 shown, the system 100 includes:

[0105] The first processing module 101 is configured to perform spectral analysis: calculate the logarithmic power spectrum of the input seismic record by using the autocorrelation method;

[0106] The second processing module 102 is configured to perform spectral decomposition: decompose the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms by using the over-relaxed Jacobi iterative algorithm; obtain the error spectrum through the difference between the logarithmic power spectrum and the model logarithmic power spectrum;

[0107] The third processing module 103 is configured to apply the deconvolution operator: convert the five groups of component logarithmic power spectra and the error spectrum into autocorrelation coefficients, and use the predictive deconvolution method and the surface consistent amplitude compensation method to obtain the seismic record.

[0108] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. When the processor executes the computer program stored in the memory, it implements the steps in any one of the seismic record processing methods in the first aspect disclosed by the present invention.

[0109] Figure 14 It is a structural diagram of an electronic device according to an embodiment of the present invention, as Figure 14As shown, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, near-field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0110] Those skilled in the art can understand that Figure 14 the structure shown in is only the structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0111] The fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a seismic record processing method according to any one of the first aspects disclosed in the present invention are implemented.

[0112] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combinations of these technical features do not conflict, they should all be considered as the scope described in this specification. The above embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

[0113] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for processing seismic records, characterized in that, The method includes: Step S1, spectral analysis: calculating the logarithmic power spectrum of the input seismic record using the autocorrelation method; Step S2, spectral decomposition: decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms using the successive over-relaxation Jacobi iterative algorithm; obtaining an error spectrum from the difference between the logarithmic power spectrum and the model logarithmic power spectrum; Step S3, deconvolution operator application: converting the five groups of component logarithmic power spectra and the error spectrum into autocorrelation coefficients, and obtaining a seismic record using the predictive deconvolution method and the surface-consistent amplitude compensation method.

2. The seismic recording processing method according to claim 1, characterized in that, In step S1, it further includes calculating weights according to the autocorrelation length and the seismic record length, and establishing a network to obtain system matrix information by reading the header information of the seismic record.

3. A method for processing seismic records according to claim 2, characterized in that, In step S2, the method of decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms using the successive over-relaxation Jacobi iterative algorithm includes: Step S21: decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms by minimizing the defined objective function; Step S22: calculating the objective function using the objective function formula; Step S23: setting the partial derivatives of the objective function with respect to all quantities to be solved to 0 to obtain the equation to be solved; Step S24: solving the equation to be solved using the successive over-relaxation Jacobi iterative algorithm to obtain the five groups of component logarithmic power spectra.

4. A method for processing seismic records according to claim 3, characterized in that, In step S2, after the method of obtaining the error spectrum from the difference between the logarithmic power spectrum and the model logarithmic power spectrum, it further includes: obtaining the error spectrum of each seismic trace, comparing the error spectrum of each seismic trace with the overall error distribution, examining whether there is excessive non-uniform noise, and marking the seismic traces with large noise.

5. A method for processing seismic records according to claim 4, characterized in that, In step S3, the predictive deconvolution method includes constructing a predictive deconvolution equation using the autocorrelation of five groups of components and obtaining a predictive deconvolution operator using the Levinson algorithm.

6. A method for processing seismic records according to claim 5, characterized in that, In step S3, the predictive deconvolution method includes constructing the predictive deconvolution equation using the autocorrelation of the error spectrum and obtaining a capture operator using the Levinson algorithm.

7. A method for processing seismic records according to claim 6, characterized in that, In step S3, the surface-consistent amplitude compensation method includes: Step S31: calculating the root mean square amplitude value of each seismic trace; Step S32: respectively statistically analyzing the amplitude energy of the shot point, geophone point, offset, and common midpoint components of each seismic trace, calculating the amplitude influence of each component on the original seismic trace, and obtaining a compensation factor; Step S33: applying the compensation factor to perform amplitude compensation on the seismic trace.

8. An earthquake recording processing system, characterized in that, The system includes: A first processing module configured to perform spectral analysis: calculating the logarithmic power spectrum of the input seismic record using the autocorrelation method; A second processing module configured to perform spectral decomposition: decomposing the logarithmic power spectrum into five groups of component logarithmic power spectra of shot points, geophone points, common midpoints, offsets, and global terms using the successive over-relaxation Jacobi iterative algorithm; obtaining an error spectrum from the difference between the logarithmic power spectrum and the model logarithmic power spectrum; The third processing module is configured to perform deconvolution operator application: convert the five groups of component logarithmic power spectra and the error spectrum into autocorrelation coefficients, and obtain seismic records by using the predictive deconvolution method and the surface-consistent amplitude compensation method.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. When the processor executes a computer program stored in the memory, the steps in a seismic record processing method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a seismic record processing method according to any one of claims 1 to 7 are implemented.

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