Spectral inversion high-resolution processing method based on strong axis stripping

Abstract

The invention provides a spectrum inversion high-resolution processing method based on strong axis stripping, and relates to the field of seismic exploration, and the technical scheme is that the spectrum inversion high-resolution processing method based on strong axis stripping is characterized by comprising the following steps: inputting original seismic data, and extracting strong reflection wave event seismic data; subtracting the extracted strong reflection wave event seismic data from the original seismic data to obtain seismic data after strong reflection suppression; replacing the original seismic data with the seismic data subjected to strong reflection suppression, and establishing a spectrum inversion objective function; and solving the spectral inversion objective function to obtain a high-resolution seismic section which suppresses strong reflected waves and highlights weak signals. The method has the advantages that a spectral inversion high-resolution processing mechanism based on strong axis stripping is clear, and time domain iteration deconvolution stripping of the strong reflection wave event, spectral inversion high-resolution processing stripping of the strong reflection wave event, strong reflection suppression and spectral inversion high-resolution thin layer recognition are involved.

Description

Technical Field

[0001] This invention relates to the field of seismic geophysical exploration technology, and in particular to a high-resolution spectral inversion processing method based on strong axis stripping. Background Technology

[0002] As oil and gas resource exploration in my country deepens, the focus is shifting towards complex reservoirs such as thin-layer and unconventional reservoirs. Seismic exploration technology, as a highly effective exploration method, plays a crucial role in oil and gas exploration. However, due to factors such as thin-layer structural wave group interferometry and formation energy absorption, existing conventional seismic data has relatively low resolution, especially in thin-layer identification. Traditional seismic data attribute extraction and interpretation techniques are insufficient to meet the precise requirements of exploration and development.

[0003] The low resolution of conventional seismic data in thin-layer exploration is mainly due to the broadband characteristics of seismic wavelets, whose waveforms cannot provide sufficient detail. To address this issue, spectral inversion technology has become a widely studied method. By eliminating the influence of seismic wavelets, it obtains high-resolution seismic profiles, enabling accurate identification of thin layers.

[0004] However, when strong reflection waves are present near a thin layer, even high-resolution processing methods based on spectral inversion struggle to identify it. For example, strong reflection waves from coal seams may mask or overwhelm weak reflection signals from the target layer, affecting the identification of thin layers near the target stratum. Reflection of strong reflection waves between different underground media can lead to multipath effects, complicating data interpretation. The presence of strong reflection waves can blur or distort underground structures, reducing imaging resolution. In some cases, strong reflection waves may be reflected or attenuated at depth, causing distortion of deep targets and increasing the difficulty of deep exploration. The presence of strong reflection waves can complicate the interpretation of formations, especially in the presence of multiple nearby reflectors, further increasing the difficulty of formation interpretation.

[0005] Chinese patent application CN202111599576.0, entitled "A Seismic Inversion Method and System for Eliminating Strong Reflection Shielding Effect," mentions a similar strong reflection suppression method: acquiring post-stack seismic data and well logging impedance curves, performing well-seismic calibration, obtaining deshielded seismic data based on the seismic data, obtaining pseudo-impedance curves based on the well logging impedance curves, matching the pseudo-impedance curves with the synthesized seismic record and the deshielded seismic data, obtaining pseudo-impedance inversion data based on the deshielded seismic data and pseudo-impedance curves, and conducting comprehensive reservoir interpretation based on the pseudo-impedance inversion data. This invention specifically eliminates and weakens the influence of strong reflection energy shielding and the Gibbs effect, improving the resolution of seismic inversion and the accuracy of reservoir prediction. However, this application requires well-seismic calibration and the acquisition of well logging impedance curves, which necessitates additional exploration work and data processing, making it unsuitable for strong reflection suppression in areas with few or no wells.

[0006] Chinese patent application CN202111290566.9, entitled "A Method and Apparatus for Removing Strong Seismic Reflections," mentions a similar method for suppressing strong reflections: A weighted average seismic trace data is calculated based on the current seismic trace and the seismic trace data of adjacent seismic traces in the seismic data. Seismic atomic parameters corresponding to the strongest amplitude positions are extracted from the weighted average seismic trace data. The seismic atoms corresponding to the seismic atomic parameters are subtracted from the weighted average seismic trace data to obtain the seismic trace data after removing strong reflections. This invention removes strong seismic reflections, avoiding the shielding of weak but useful signals, thereby improving the identification accuracy of strong reflection axes and providing guidance for the exploration and development of oil and gas reservoirs hidden beneath strong seismic reflection axes. However, for complex geological structures and strong reflection layering, this method may not completely eliminate all strong reflection energy, and a certain degree of shielding effect may still exist. Furthermore, it relies on the data of the current seismic trace and adjacent seismic traces in the seismic data; when the data is incomplete or missing, the accuracy of the seismic trace data after removing strong reflections will be affected. Therefore, this method is not suitable for complex strata or situations where nearby seismic traces are missing.

[0007] Chinese patent application CN202110164198.7, entitled "A Method and Apparatus for Predicting Reservoirs Under Strong Reflection Shielding," mentions a similar strong reflection suppression method: When the seismic response characteristics of a reservoir are completely covered by the strong reflection of the underlying strong reflection interface, the distance λ between the reservoir and the underlying strong reflection interface is determined. Based on the distance λ and the average velocity v of the reservoir, the frequency fd is obtained. The AC curve is reconstructed using the frequency fd to obtain the reconstructed AC curve. The wave impedance curve is obtained using the reconstructed AC curve and the DEN curve. The wave impedance curve is used for inversion to obtain the seismic inversion data volume. Reservoir prediction is then performed based on the seismic inversion data volume. This invention can accurately identify reservoirs whose seismic response characteristics are completely submerged in the strong reflection of the underlying strong reflection interface. However, this application requires determining the distance λ between the reservoir and the strong reflection interface and knowing the average velocity v of the reservoir. Errors in the distance λ and the average velocity v will directly lead to inaccurate processing results.

[0008] To address the aforementioned technical problems, we have invented a high-resolution spectral inversion processing method based on strong axis stripping. This method improves upon conventional spectral inversion processing by identifying strong reflection wave phase axis data within the data volume and suppressing strong reflection components in seismic data. This overcomes the shortcomings of conventional methods, improves the resolution of seismic data, and thus more precisely depicts subsurface geological structures, accurately predicts the thickness and spatial distribution of subsurface reservoirs, and solves the aforementioned technical issues. Summary of the Invention

[0009] This invention provides a high-resolution spectral inversion method based on strong axis stripping. Its innovation lies in extracting strong reflection wave phase axis seismic data, suppressing the original seismic data based on the extracted strong reflection wave phase axis, and performing high-resolution thin-layer identification through spectral inversion on the strong reflection suppressed seismic data to highlight weak reflection wave signals and improve the identification of thin layers near the target layer.

[0010] In a first aspect, the present invention provides a high-resolution spectral inversion processing method based on strong axis stripping, characterized in that it includes:

[0011] Input the raw seismic data and extract the strong reflection wave phase axis seismic data;

[0012] Subtracting the extracted strong reflection wave phase axis seismic data from the original seismic data yields the seismic data after strong reflection suppression.

[0013] Replace the original seismic data with the seismic data after strong reflection suppression, and establish a spectral inversion objective function;

[0014] Solve the spectral inversion objective function to obtain high-resolution seismic profiles.

[0015] Two methods are provided for extracting strong reflection wave phase axis seismic data. Method 1 uses time-domain iterative deconvolution and implements layer-time window constraints to extract strong reflection wave phase axis seismic data. Method 2 utilizes the sparsity characteristics of high-resolution spectral inversion to extract strong reflection wave phase axis seismic data.

[0016] Method 1 employs time-domain iterative deconvolution and applies layer-time window constraints to extract seismic data with strong reflection phase axes, including:

[0017] Input the seismic data to be processed, d a The maximum number of iterations (maxiter), the maximum allowable error (tol), and the source wavelet (w);

[0018] Combine the source wavelet w with the seismic data d a Perform cross-correlation and search for the location of the maximum cross-correlation; this location is the position of the reflectance coefficient. The specific calculation method is as follows:

[0019] max∫d a (t,t0)w(t-τ1,t0)dt(1)

[0020] Where, d a Input seismic data; t0 is the center position of the time window; τ1 is the time position of the searched reflection coefficient; w is the source wavelet; max is to find the maximum value.

[0021] The maximum cross-correlation value is the time position of the predicted reflection coefficient. Then, the amplitude r of the correlated reflection coefficient is calculated using the following formula:

[0022]

[0023] The reflection coefficients are convolved with the seismic wavelet to obtain the strong reflection wave phase axis seismic data. The specific calculation is as follows:

[0024] d(t0)=w(t0)*r. (3)

[0025] Where d represents the extracted strong reflection wave phase axis seismic data.

[0026] Method 2 utilizes the sparsity characteristics of high-resolution spectral inversion processing to solve the objective function of spectral inversion and extract seismic data of strong reflection wave phase axes, including:

[0027] Input the seismic data to be processed, d a The sparsity coefficient λ, the maximum allowable error tol, and the source wavelet w.

[0028] A wedge-shaped model library consisting of pairs of odd and even reflection coefficients is constructed, as follows:

[0029]

[0030] Where r is the constructed reflection coefficient wedge model library; t refers to the time point where the reflecting layer is located; the reflector kernel matrix of the reflection coefficient pair is constructed by moving the reflection coefficient pair along the time axis using mΔt, where the value of m ranges from the first data point to the total number of samples in the seismic trace; n refers to the number of samples between two reflectors; Δt refers to the layer thickness; a i and b i This refers to the scaling parameter of the odd / even reflection coefficient with respect to the components, expressed as a... i and b i These can be combined into any pair of reflection coefficients, where each pair of wedge matrices contains only one pair of scaling parameters 'a'. i and b i The value is non-zero and is used to adjust the reflection coefficient. All other scaling parameters should be zero, so the number of non-zero scaling parameters equals the number of wedge matrices; r 0m This refers to the odd-numbered reflection coefficient components with different layer thicknesses; This refers to the even-numbered reflection coefficient component for different layer thicknesses.

[0031] Constructing the expected inversion data. The constructed odd-even reflection coefficients are convolved with the wedge model library and the source wavelet to obtain the wedge model library of layer responses. Based on requirements, different layer responses are combined and arranged according to a certain rule. Rearranging these responses yields the expected inversion seismic data, represented using a matrix method. The specific calculation method is as follows:

[0032]

[0033] in, Is the source wavelet and The convolution of G; 0m It is the source wavelet and r 0m The convolution operation can be performed in the frequency domain. Therefore, when the computational load is large, matrix multiplication can be implemented as a convolution in the frequency domain, which can significantly improve computational efficiency.

[0034] The objective function for spectral inversion is designed using L1 regularization constraints. The original seismic data is subtracted from the expected inversion result to obtain the difference data. The objective function for spectral inversion is then designed based on this difference data. This objective function can be achieved by minimizing the L1 regularization constraint on the difference data, thus realizing the spectral inversion of the subsurface medium. The specific calculation method is as follows:

[0035] min(||d a -Gm||2+λ|m|1) (6)

[0036] Where, d aThe original seismic data is represented by Gm; the expected inverted seismic data is represented by m; the physical parameters to be solved are represented by λ; and the regularization parameter (i.e., the sparsity coefficient) is represented by λ. In actual calculations, an iterative algorithm is used to obtain the optimal physical parameter estimates.

[0037] By utilizing the sparsity characteristics of high-resolution spectral inversion processing, the objective function of spectral inversion is solved. The obtained parameter m is then combined with the constructed wedge model library to obtain the seismic data d of the strong reflection wave phase axis.

[0038] Furthermore, the original seismic data will be processed using the original seismic data d. a Subtracting the extracted seismic data d from the strong reflection phase axis yields the seismic data d after strong reflection suppression. h The calculation method is as follows:

[0039] d h =d a -d. (7)

[0040] Furthermore, the seismic data d after strong reflection suppression h Raw seismic data d a The objective function for spectral inversion is established, and the specific calculation method is as follows:

[0041] min(||d h -Gm||2+λ|m|1) (8)

[0042] Furthermore, the spectral inversion objective function is derived, and the obtained parameter m is combined with the constructed wedge model library to obtain a high-resolution seismic profile that suppresses strong reflection waves and highlights weak signals. The method of establishing the wedge model library is consistent with the method of establishing the wedge model library composed of odd and even reflection coefficient pairs in Method 2.

[0043] Secondly, embodiments of the present invention provide a high-resolution spectral inversion processing apparatus based on strong axis stripping, characterized in that it includes:

[0044] The extraction module receives raw seismic data and extracts strong reflection wave phase axis seismic data;

[0045] The calculation module subtracts the extracted strong reflection wave phase axis seismic data from the original seismic data to obtain the seismic data after strong reflection suppression.

[0046] The modeling module replaces the original seismic data with the seismic data after strong reflection suppression and establishes a spectral inversion objective function.

[0047] The solver module solves the spectral inversion objective function to obtain high-resolution seismic profiles.

[0048] Thirdly, embodiments of the present invention provide an electronic device, characterized in that it includes: a processor and a memory, wherein the processor is configured to execute a program for a high-resolution spectral inversion processing method based on strong axis stripping, so as to implement the high-resolution spectral inversion processing method based on strong axis stripping.

[0049] Fourthly, embodiments of the present invention provide a storage medium, characterized in that the storage medium stores one or more programs, which can be executed by one or more processors to implement the high-resolution processing method for spectral inversion based on strong axis stripping.

[0050] The beneficial effects of this invention are as follows: This invention provides two methods for extracting strong reflection wave phase axis seismic data. Method 1 fully utilizes time-domain iterative deconvolution and implements layer-time window constraints to extract strong reflection wave phase axis seismic data. This method uses cross-correlation analysis of waveform similarity to obtain the temporal location and amplitude of reflection coefficients. Method 2 utilizes the sparsity characteristics of high-resolution spectral inversion to extract strong reflection wave phase axes. Both methods are computationally simple, have better stability than conventional methods, and can effectively identify strong reflectors in the subsurface medium, i.e., stratigraphic interfaces with strong reflection waves. This helps to highlight the key features of the target layer in seismic data and improve the accuracy of geological structure interpretation.

[0051] The suppression of strong reflection waves is highly effective. By suppressing strong reflection waves in the raw seismic data, strong reflection signals in the seismic record can be effectively suppressed. This can reduce multipath effects, improve the accuracy of geological structure interpretation, more accurately reconstruct subsurface structures, improve imaging resolution, increase the detection depth of deep targets, and make the interpretation of strata more accurate.

[0052] Weak reflection wave signals are highlighted. After being suppressed by strong reflection waves, weak reflection wave signals that were originally masked by strong reflection waves are highlighted, thus revealing subtle changes in underground structures more clearly in seismic data. This is of great significance for discovering potential oil and gas reservoirs or other underground resources.

[0053] High-resolution thin-layer identification capability. This technical solution, through spectral inversion processing, can fully utilize the high-frequency information of seismic data to improve the identification resolution of subsurface thin layers. This enables more accurate identification of the thin-layer structure of target strata in oil and gas exploration, helping to pinpoint the location and boundaries of oil and gas reservoirs. Attached Figure Description

[0054] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings listed below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic flowchart of a high-resolution spectral inversion method based on strong axis stripping according to the present invention.

[0056] Figure 2 The time domain comparison shows Ricker subwavelengths of different frequencies, with the red line representing the 30Hz Ricker subwavelength and the black line representing the 90Hz Ricker subwavelength.

[0057] Figure 3 The left and right images show the seismic record synthesized using the 30Hz Ricker wavelet convolution reflection coefficient model, while the right image shows the conventional spectral inversion result of the synthesized seismic record.

[0058] Figure 4 The left figure shows the established reflection coefficient model; the middle figure shows the strong reflection wave phase axis seismic data extracted after one iteration of the reflection coefficient model using time-domain iterative deconvolution and layer-time window constraints; the right figure shows the strong reflection wave phase axis seismic data extracted after four iterations of the reflection coefficient model using time-domain iterative deconvolution and layer-time window constraints.

[0059] Figure 5 The left image shows the seismic record synthesized by the 30Hz Ricker wavelet convolution reflection coefficient model, and the right image shows the strong reflection wave phase axis seismic data extracted from the synthesized seismic record using the sparsity characteristics of the high-resolution spectral inversion processing.

[0060] Figure 6 The left image shows the synthetic seismic record after strong reflection suppression; the right image shows the seismic record after high-resolution spectral inversion of the synthetic seismic record after strong reflection suppression.

[0061] Figure 7 The original seismic data shows the in-phase axis of the strong reflection wave from the coal seam indicated by the red arrow.

[0062] Figure 8 This is the result of conventional spectral inversion processing of seismic data;

[0063] Figure 9 for Figure 8 Enlarged profile extracted from the results of conventional spectrum inversion;

[0064] Figure 10 This is the high-resolution result of spectral inversion after strong reflection wave suppression;

[0065] Figure 11 for Figure 10 The magnified profile extracted from the high-resolution spectral inversion process after suppressing medium-intensity reflected waves. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0067] Example 1:

[0068] like Figure 1 As shown, Figure 1 This is a flowchart of a high-resolution spectral inversion method based on strong axis stripping according to the present invention.

[0069] In step 101, the raw seismic data is input, and the strong reflection wave phase axis seismic data is extracted. In this embodiment, Figure 2 The middle section shows a time-domain comparison of Ricker wavelets with different dominant frequencies, where the red line represents the 30Hz Ricker wavelet and the black line represents the 90Hz Ricker wavelet. Seismic records synthesized from the 30Hz Ricker wavelet convolutional reflection coefficient model are also included. Figure 3 (Left figure) Perform conventional spectral inversion ( Figure 3 (See right figure) The sparsity coefficient λ = 0.05, and the weak reflection waves are not recovered. Therefore, it is necessary to improve the conventional spectral inversion processing method to suppress the strong reflection components in the seismic wavelet in order to overcome the defects of the conventional method and improve the resolution of the seismic data.

[0070] This step provides two methods. Step 101.1 uses time-domain iterative deconvolution and implements layer-time window constraints to extract strong reflection wave phase axis seismic data. This method cross-correlates the source wavelet with the seismic data and searches for the location of the maximum cross-correlation, which is the location of the reflection coefficient. The obtained reflection coefficient is convolved with the seismic wavelet to obtain the strong reflection wave phase axis seismic data. Alternatively, step 101.2 utilizes the sparsity characteristics of high-resolution spectral inversion to solve the objective function of spectral inversion and extract the strong reflection wave phase axis seismic data.

[0071] In step 102, the extracted strong reflection wave phase axis seismic data is subtracted from the original seismic data to obtain the seismic data after strong reflection suppression.

[0072] In step 103, the original seismic data is replaced with the seismic data after strong reflection suppression to establish the spectral inversion objective function.

[0073] In step 104, the spectral inversion objective function is solved, and the obtained parameters are combined with the constructed wedge model library to obtain a high-resolution seismic profile that suppresses strong reflection waves and highlights weak signals.

[0074] In step 101.1, the steps for extracting strong reflection wave phase axis seismic data using time-domain iterative deconvolution and layer-time window constraints are as follows:

[0075] Input the seismic data to be processed, d a The maximum number of iterations (maxiter), the maximum allowable error (tol), and the source wavelet (w) are also considered.

[0076] Combine the source wavelet w with the seismic data d a Perform cross-correlation to find the location of the maximum cross-correlation; this location is the position of the reflection coefficient. Then calculate the amplitude of the reflection coefficient. The specific calculation method is as follows:

[0077] max∫d a (t,t0)w(t-τ1,t0)dt(1)

[0078] Where, d a Input seismic data; t0 is the center position of the time window; τ1 is the time position of the searched reflection coefficient; w is the source wavelet; max is to find the maximum value.

[0079] The maximum cross-correlation value is the time position of the predicted reflection coefficient. Then, the amplitude r of the correlated reflection coefficient is calculated using the following formula:

[0080]

[0081] The reflection coefficients are convolved with the seismic wavelet to obtain the strong reflection wave phase axis seismic data. The specific calculation is as follows:

[0082] d(t0)=w(t0)*r (3)

[0083] Where d represents the extracted strong reflection wave phase axis seismic data. Figure 4 The left figure shows the established reflection coefficient model. The middle figure shows the strong reflection wave phase axis seismic data extracted after one iteration of the reflection coefficient model using time-domain iterative deconvolution and layer-time window constraints. The right figure shows the strong reflection wave phase axis seismic data extracted after four iterations of the reflection coefficient model using time-domain iterative deconvolution and layer-time window constraints.

[0084] In step 101.2, the following steps are taken to extract strong reflection wave phase axis seismic data by solving the objective function of spectral inversion and utilizing the sparsity characteristics of high-resolution spectral inversion processing:

[0085] Input the seismic data to be processed, d a The sparsity coefficient λ, the maximum allowable error tol, and the source wavelet w.

[0086] A wedge-shaped model library consisting of pairs of odd and even reflection coefficients is constructed, as follows:

[0087]

[0088] Where r is the constructed reflection coefficient wedge model library; t refers to the time point where the reflecting layer is located; the reflector kernel matrix of the reflection coefficient pair is constructed by moving the reflection coefficient pair along the time axis using mΔt, where the value of m ranges from the first data point to the total number of samples in the seismic trace; n refers to the number of samples between two reflectors; Δt refers to the layer thickness; a i and b i This refers to the scaling parameter of the odd / even reflection coefficient with respect to the components, expressed as a... i and b i These can be combined into any pair of reflection coefficients, where each pair of wedge matrices contains only one pair of scaling parameters 'a'. i and b i The value is non-zero and is used to adjust the reflection coefficient. All other scaling parameters should be zero, so the number of non-zero scaling parameters equals the number of wedge matrices; r 0m This refers to the odd-numbered reflection coefficient components with different layer thicknesses; This refers to the even-numbered reflection coefficient component for different layer thicknesses.

[0089] Constructing the expected inversion data. The constructed odd-even reflection coefficients are convolved with the wedge model library and the source wavelet to obtain the wedge model library of layer responses. Based on requirements, different layer responses are combined and arranged according to a certain rule. Rearranging these responses yields the expected inversion seismic data, represented using a matrix method. The specific calculation method is as follows:

[0090]

[0091] in, Is the source wavelet and The convolution of G; 0m It is the source wavelet and r 0m The convolution operation can be performed in the frequency domain. Therefore, when the computational load is large, matrix multiplication can be implemented as a convolution in the frequency domain, which can significantly improve computational efficiency.

[0092] The objective function for spectral inversion is designed using L1 regularization constraints. The original seismic data is subtracted from the expected inversion result to obtain the difference data. The objective function for spectral inversion is then designed based on this difference data. This objective function can be achieved by minimizing the L1 regularization constraint on the difference data, thus realizing the spectral inversion of the subsurface medium. The specific calculation method is as follows:

[0093] min(||d a -Gm||2+λ|m|1) (6)

[0094] Where, d aThe original seismic data is represented by Gm; the expected inverted seismic data is represented by m; the physical parameters to be solved are represented by λ; and the regularization parameter (i.e., the sparsity coefficient) is represented by λ. In actual calculations, an iterative algorithm is used to obtain the optimal physical parameter estimates.

[0095] By utilizing the sparsity characteristics of high-resolution spectral inversion, the objective function of spectral inversion (6) is solved. The obtained parameter m is combined with the constructed wedge model library to obtain the strong reflection wave phase axis seismic data d. Figure 5 The left-middle figure shows the seismic record synthesized from the 30Hz Ricker wavelet convolution reflection coefficient model. At lower dominant frequencies, the amplitude of strong reflection waves covers the adjacent weak reflection signals. Utilizing the sparsity characteristics of high-resolution spectral inversion processing, the objective function for spectral inversion is solved, with a sparsity coefficient λ = 5. Through this large sparsity coefficient, seismic data of the strong reflection wave phase axis are extracted, such as... Figure 5 As shown in the right figure, strong reflectors, i.e., stratigraphic interfaces with strong reflected waves, were effectively identified, highlighting the key features of the target layer in the seismic data.

[0096] In step 102, the original seismic data d a Subtracting the strong reflection wave phase axis seismic data d extracted in step 1, we obtain the seismic data d after strong reflection suppression. h ( Figure 6 (Left figure) This effectively reduces multipath effects and improves the accuracy of geological structure interpretation. The calculation method is as follows:

[0097] d h =d a -d (7)

[0098] In step 103, the seismic data d after strong reflection suppression is... h Replace the original seismic data d in step 101 a The objective function for spectral inversion is established, and the specific calculation method is as follows:

[0099] min(||d h -Gm||2+λ|m|1) (8)

[0100] In step 104, the spectral inversion objective function (8) from step 103 is solved, with a sparsity coefficient λ = 0.05 (to highlight weak reflection wave signals through small sparsity coefficients). The obtained parameter m is combined with the constructed wedge model library to obtain a high-resolution seismic profile that suppresses strong reflection waves and highlights weak signals. Figure 6 (Right figure). The method for establishing the wedge model library is the same as the method for establishing the wedge model library composed of odd and even reflection coefficient pairs in Method 2, specifically as follows:

[0101]

[0102] Where r is the constructed reflection coefficient wedge model library; t refers to the time point where the reflecting layer is located; the reflector kernel matrix of the reflection coefficient pair is constructed by moving the reflection coefficient pair along the time axis using mΔt, where the value of m ranges from the first data point to the total number of samples in the seismic trace; n refers to the number of samples between two reflectors; Δt refers to the layer thickness; a i and b i This refers to the scaling parameter of the odd / even reflection coefficient with respect to the components, expressed as a... i and b i These can be combined into any pair of reflection coefficients, where each pair of wedge matrices contains only one pair of scaling parameters 'a'. i and b i The value is non-zero and is used to adjust the reflection coefficient. All other scaling parameters should be zero, so the number of non-zero scaling parameters equals the number of wedge matrices; r 0m This refers to the odd-numbered reflection coefficient components with different layer thicknesses; This refers to the even-numbered reflection coefficient component for different layer thicknesses.

[0103] Figure 7 The original seismic data shows that the strong reflection wave phase axis of the coal seam indicated by the red arrow covers the nearby weak reflection wave signal, affecting the identification of thin layers near the target layer. Figure 8 This is the result of conventional spectral inversion processing of seismic data. Figure 9 for Figure 8 The magnified profile extracted from the conventional spectrum inversion results shows that the strong reflected waves in the profile mask the weak reflected signals of the target layer, affecting the identification of thin layers near the target layer. Figure 10 This is the high-resolution result of spectral inversion after strong reflection wave suppression. Figure 11 for Figure 10 The magnified profile extracted from the high-resolution spectral inversion after suppressing strong reflection waves more accurately reconstructs the subsurface structure and improves imaging resolution. Suppressing strong reflection waves highlights weak reflection signals that were previously masked by strong reflections, enhancing the ability to identify thin layers at high resolution and thus revealing subtle changes in the subsurface structure more clearly in seismic data.

[0104] The two methods provided by this technical solution for extracting seismic data with strong reflection wave phase axes can effectively identify strong reflectors in the subsurface medium, helping to highlight key features of target strata in seismic data; effectively suppress strong reflection signals in seismic records; reduce multipath effects, more accurately reconstruct subsurface structures, and improve imaging resolution; increase the detection depth for deep targets; and highlight weak reflection wave signals that were originally masked by strong reflection waves, thus more clearly showing subtle changes in subsurface structures in seismic data; through spectral inversion processing, high-frequency information of seismic data can be fully utilized to improve the identification resolution of thin subsurface layers. This enables more accurate identification of thin-layer structures of target strata in oil and gas exploration, helping to pinpoint the location and boundaries of oil and gas reservoirs.

[0105] Example 2:

[0106] This invention provides a high-resolution spectral inversion processing device based on strong axis stripping, characterized in that it includes:

[0107] The extraction module receives raw seismic data and extracts strong reflection wave phase axis seismic data;

[0108] The calculation module subtracts the extracted strong reflection wave phase axis seismic data from the original seismic data to obtain the seismic data after strong reflection suppression.

[0109] The modeling module replaces the original seismic data with the seismic data after strong reflection suppression and establishes a spectral inversion objective function.

[0110] The solver module solves the spectral inversion objective function to obtain high-resolution seismic profiles.

[0111] For the specific functions of each module, please refer to the relevant descriptions in the above method embodiments, which will not be repeated here.

[0112] Example 3:

[0113] This invention provides an electronic device, characterized in that it includes a processor and a memory, wherein the processor is used to execute a program for a high-resolution spectral inversion method based on strong axis stripping, so as to implement the high-resolution spectral inversion method based on strong axis stripping.

[0114] An electronic device includes at least one processor, memory, at least one network interface, and other user interfaces. The various components of the electronic device are coupled together via a bus system. It is understood that the bus system is used to enable communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0115] The user interface may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen). It is understood that the memory in this embodiment may be volatile memory or non-volatile memory, or may include both.

[0116] In this embodiment of the invention, the processor executes the method steps provided in each method embodiment by calling a program or instruction stored in the memory, specifically a program or instruction stored in an application program.

[0117] In some implementations, the memory stores elements such as executable units or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0118] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. The program implementing the method of this invention can be included in the application programs.

[0119] Example 4:

[0120] Fourthly, embodiments of the present invention provide a storage medium, characterized in that the storage medium stores one or more programs, which can be executed by one or more processors to implement the high-resolution processing method for spectral inversion based on strong axis stripping.

[0121] The method steps described in conjunction with Embodiment 1 disclosed herein can be implemented using hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0122] The above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-resolution spectral inversion processing method based on strong axis stripping, characterized in that, include: Input the raw seismic data and extract the strong reflection wave phase axis seismic data; Subtracting the extracted strong reflection wave phase axis seismic data from the original seismic data yields the seismic data after strong reflection suppression. Replace the original seismic data with the seismic data after strong reflection suppression, and establish a spectral inversion objective function; Solve the spectral inversion objective function to obtain high-resolution seismic profiles.

2. The high-resolution spectral inversion method based on strong axis stripping according to claim 1, characterized in that, Strong reflection wave phase axis seismic data were extracted by time-domain iterative deconvolution and layer-time window constraints. Alternatively, by utilizing the sparsity characteristics of high-resolution spectral inversion processing, the objective function of spectral inversion can be solved to extract seismic data of strong reflection wave phase axes.

3. The high-resolution spectral inversion method based on strong axis stripping according to claim 2, characterized in that, Time-domain iterative deconvolution was employed, and layer-time window constraints were implemented to extract seismic data with strong reflection wave phase axes, including: Input the seismic data to be processed, d a The maximum number of iterations (maxiter), the maximum allowable error (tol), and the source wavelet (w); Combine the source wavelet w with the seismic data d a Perform cross-correlation and search for the location of the maximum cross-correlation. The location of the maximum cross-correlation value is the time position of the predicted reflection coefficient. Then, the amplitude r of the correlated reflection coefficient is calculated: By convolving the obtained reflection coefficient amplitude with the seismic wavelet, we obtain the seismic data of the strong reflection wave in phase axis.

4. The high-resolution spectral inversion processing method based on strong axis stripping according to claim 2, characterized in that, include: Input the seismic data to be processed, d a The sparsity coefficient λ, the maximum allowable error tol, and the source wavelet w; Construct a wedge model library consisting of odd and even reflection coefficient pairs; Based on the wedge model library and source wavelet, construct the expected inversion data: Based on the original seismic data, inversion data, and the application of L1 regularization constraints, the design spectrum inversion objective function is retrieved. Solve the spectral inversion objective function to obtain the seismic data d of the strong reflection wave phase axis.

5. The high-resolution spectral inversion method based on strong axis stripping according to claim 2, characterized in that, The formula for calculating the reflection coefficient amplitude r is: Where r is the amplitude of the reflection coefficient; d a The input is earthquake data; t0 is the center position of the time window; τ1 is the time position of the searched reflection coefficient; w is the source wavelet.

6. The high-resolution spectral inversion processing method based on strong axis stripping according to claim 4, characterized in that, Based on the wedge model library and source wavelet, the expected inversion data is constructed, including: The constructed odd and even reflection coefficients are convolved with the wedge model library and the source wavelet to obtain the wedge model library of layer responses; by combining different layer responses and arranging them according to a certain rule, the expected inversion seismic data can be obtained. Representing inverted seismic data using matrix methods.

7. The high-resolution spectral inversion method based on strong axis stripping according to claim 1, characterized in that, To solve the objective function of spectral inversion, the obtained parameter m needs to be combined with the constructed wedge model library.

8. A high-resolution spectral inversion processing device based on strong axis stripping, characterized in that, include: The extraction module receives raw seismic data and extracts strong reflection wave phase axis seismic data; The calculation module subtracts the extracted strong reflection wave phase axis seismic data from the original seismic data to obtain the seismic data after strong reflection suppression. The modeling module replaces the original seismic data with the seismic data after strong reflection suppression and establishes a spectral inversion objective function. The solver module solves the spectral inversion objective function to obtain high-resolution seismic profiles.

9. An electronic device, characterized in that, include: A processor and a memory, the processor being configured to execute a program for a high-resolution spectral inversion method based on strong axis stripping, to implement the high-resolution spectral inversion method based on strong axis stripping as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the high-resolution spectral inversion processing method based on strong axis stripping as described in any one of claims 1 to 7.

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