A pseudo multi-channel adaptive subtraction method and system for protecting target zones

Abstract

This invention relates to the field of geophysical exploration technology, specifically to a pseudo-multichannel adaptive subtraction method and system for protecting target strata. The method includes combining a pseudo-multichannel layer protection model with a Mask, optimizing the layer protection pseudo-multichannel adaptive subtraction processing, and multichannel cumulative equalization processing. This method solves the problem that since the multiple waves and the primary waves in the original data cannot completely satisfy the orthogonality condition, the effective signal is usually damaged during the adaptive subtraction process. It has the technical effect of effectively protecting the primary waves of the target strata and achieving a significant optimization and improvement in the matching subtraction results.

Description

[0001] This application claims priority to Chinese Patent Application No. 2024119627333, filed on December 28, 2024, entitled "A Quasi-Multichannel Adaptive Subtraction Method and System for Protecting Target Layers", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of geophysical exploration technology, specifically to a pseudo-multichannel adaptive subtraction method and system for protecting target strata. Background Technology

[0003] In seismic exploration, multiples are one of the main factors affecting the signal-to-noise ratio and resolution of marine seismic data. They are usually regarded as noise and removed. Multiple suppression is one of the important steps in determining the success of seismic data processing and interpretation. The prediction-subtraction method based on the wave equation includes two processes: prediction and subtraction, and has a wider range of applicability. Since there are differences between the predicted multiples and the actual multiples in the original seismic data, an adaptive subtraction method is needed to match and subtract the predicted multiples from the original seismic data to achieve a good multiple suppression effect. In practical applications, the multiples and primary waves in the original data do not completely satisfy the orthogonality condition (i.e., they have a strict separability condition), and the effective signal is usually damaged during the adaptive subtraction process. Summary of the Invention

[0004] To address the technical problem of damage to effective signals during the matching subtraction process in quasi-multichannel adaptive subtraction methods, and specifically for scenarios requiring suppression of multiple waves through conventional wave equation-based predictive subtraction, this invention provides a quasi-multichannel adaptive subtraction method and system for protecting target layers.

[0005] In a first aspect, embodiments of the present invention provide a quasi-multichannel adaptive subtraction method for protecting target layers, comprising:

[0006] Multiple wave model prediction: Based on the stratigraphic data of the target layer, a predicted multiple wave model is obtained through a multiple wave prediction method;

[0007] Extracting the primary wave model: Based on the stratigraphic data of the target layer, the primary wave model corresponding to the target layer is extracted using the phase axis matching method to obtain the extracted original primary wave model;

[0008] Determine the protection range: Based on the original primary wave model, through exponential inverse processing after steepness adaptation, obtain the layer protection model Mask of the target layer to determine the protection range of the original primary wave model;

[0009] Protecting effective signals: Based on the original seismic data and the predicted multiple wave model, and by introducing the layer protection model Mask, the optimized layer protection pseudo-multi-channel adaptive subtraction processing after combining the pseudo-multi-channel layer protection model Mask is used to obtain the seismic data with layer protection after suppressing multiple waves, so as to protect the effective signals in the original seismic data.

[0010] Preferably, the steepness adaptation treatment includes:

[0011] Based on the original first-wave model, the original first-wave model is subjected to steepness optimization by configuring the scaling factor of the layer protection model function to obtain an amplified first-wave model.

[0012] Preferably, the steepness optimization process includes:

[0013] Initialize the scale factor configuration, where the initial scale factor configuration should be no less than 100, with a typical value of 100;

[0014] Based on the original primary wave model and the scale factor of the initial configuration, a visualized primary wave model is generated through the initial amplification process of the layer protection model function.

[0015] Based on the visualized, initially magnified primary wave model, determine its steepness suitability:

[0016] If its steepness is not suitable, the scale factor is adjusted to adapt its steepness to the needs of steepness optimization.

[0017] If the steepness is appropriate, then the amplified primary wave model is determined.

[0018] Preferably, the exponential inversion process includes:

[0019] Based on the amplified primary wave model, the amplified primary wave model is exponentially reversed by running the natural exponential function of the layer protection model function to form the layer protection model Mask, thereby determining the protection range of the original primary wave model.

[0020] Preferably, the layer protection model function is:

[0021] Mask = exp(-scale × Primarys) 2 )

[0022] Wherein, exp() is the natural exponential function; scale is a scale factor that adjusts the steepness of the Mask boundary of the layer protection model; Primaries is the original first-wave model; and Mask is the Mask of the layer protection model.

[0023] Preferably, the pseudo-multi-layer protection model Mask combination includes:

[0024] Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the combined multiple wave model are determined by combining the quasi-multichannel layer protection with the quasi-multichannel adaptive function of the layer protection. This allows the layer protection model Mask to be introduced into the original seismic data and the multiple wave model.

[0025] Preferably, the optimization layer protection pseudo-multichannel adaptive subtraction processing includes:

[0026] Based on combined seismic data and a pseudo-multichannel multiple wave combined model, the pseudo-multichannel layer protection optimization subtraction of the layer protection pseudo-multichannel adaptive function is used to obtain the seismic data after suppressed multiple waves with layer protection, so as to remove the pseudo-multichannel protection target layer data in the original seismic data during the multiple wave model process.

[0027] Preferably, the multi-channel cumulative equalization process includes:

[0028] Based on the accumulated data of each pseudo-multichannel protection target layer, the multichannel accumulated equalization of the layer protection pseudo-multichannel adaptive function is used to obtain the seismic data after the suppression of multiple waves with layer protection, so as to remove the multichannel protection target layer data in the process of removing multiple wave model from the original seismic data.

[0029] Preferably, the layer protection pseudo-multichannel adaptive function is:

[0030]

[0031] The layer protection pseudo-multichannel adaptive function is a layer protection four-pseudo-channel adaptive function;

[0032] Wherein, d is the original seismic data vector; s1, s2, s3, and s4 are the corresponding adaptive matched filters for the four pseudo-traces; M1, M2, M3, and M4 are the corresponding predicted multiple wave models for the four pseudo-traces, and the multiple wave models are represented by multiple wave convolution matrices; ⊙ is the Hadamard product; and Mask is the layer protection model Mask.

[0033] Preferably, the multiple wave model is:

[0034]

[0035] Wherein, m(t) is the predicted multiple of a single channel; N is the length of the single channel data; and n is the length of the adaptive matched filter.

[0036] Preferably, the original seismic data is:

[0037] d(t)={d(0), d(1), d(2)…d(N)}

[0038] Where t is a natural number 1…N.

[0039] Preferably, the multiple prediction method includes the extended interlayer multiple prediction method XIMP and the inverse scattering method.

[0040] Preferably, the in-phase shaft matching method includes:

[0041] Based on the stratigraphic data of the target stratigraphic level, pulse data constrained by stratigraphic time is generated. Based on the adaptive matching of the pulse data and the original seismic data, the original first wave model of the target stratigraphic level is extracted.

[0042] Preferably, the method further includes the following before the predicted multiple wave model:

[0043] The raw seismic data is acquired and preprocessed to obtain a post-stack dataset. Based on the post-stack dataset, the stratigraphic data of the target layer is extracted. The preprocessing of the raw seismic data includes the data preprocessing process of observation system definition, short-path recovery, regularization, removal of direct waves, noise suppression, dynamic correction, static correction, stacking, and imaging.

[0044] On the other hand, the present invention also provides a quasi-multichannel adaptive subtraction system for protecting target layers, comprising:

[0045] The system is used to implement the above-mentioned quasi-multichannel adaptive subtraction method, and includes:

[0046] Multiple wave model prediction module: used to obtain the predicted multiple wave model based on the stratigraphic data of the target stratigraphic level through the multiple wave prediction method;

[0047] Primary wave model extraction module: Based on the stratigraphic data of the target stratigraphic level, the module extracts the primary wave model corresponding to the target stratigraphic level using the phase axis matching method to obtain the extracted original primary wave model;

[0048] The protection range determination module is used to obtain the layer protection model Mask of the target layer based on the original primary wave model through exponential inverse processing after steepness adaptation, so as to determine the protection range of the original primary wave model.

[0049] The module for protecting effective signals is used to obtain seismic data with layer protection after suppressing multiple waves based on the original seismic data and the predicted multiple wave model. This is achieved by introducing a layer protection model Mask and then using an optimized layer protection pseudo-multi-channel adaptive subtraction process after combining the pseudo-multi-channel layer protection model Mask with the original seismic data, thus protecting the effective signals in the original seismic data.

[0050] At least one embodiment of this application also provides a pseudo-multichannel adaptive subtraction device for protecting target layers, comprising:

[0051] The multiple wave model prediction module is used to obtain the predicted multiple wave model based on the stratigraphic data of the target stratigraphic level using a multiple wave prediction method.

[0052] The primary wave model extraction module is used to extract the primary wave model corresponding to the target layer based on the layer data of the target layer by using the phase axis matching method, so as to obtain the extracted original primary wave model.

[0053] The protection range determination module is used to obtain the layer protection model Mask of the target layer based on the original primary wave model through exponential inverse processing after steepness adaptation, so as to determine the protection range of the original primary wave model.

[0054] The effective signal protection module is used to obtain suppressed multiple seismic data with layer protection based on the original seismic data and the predicted multiple wave model. It introduces a layer protection model Mask and then performs optimized layer protection pseudo-multichannel adaptive subtraction processing after combining the pseudo-multichannel layer protection model Mask, so as to protect the effective signal in the original seismic data.

[0055] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described quasi-multichannel adaptive subtraction method for protecting target layers.

[0056] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described quasi-multichannel adaptive subtraction method for protecting target layers. Attached Figure Description

[0057] Figure 1 This is a general flowchart of an embodiment of the present invention;

[0058] Figure 2 This is a detailed flowchart of an embodiment of the present invention;

[0059] Figure 3 This is an exploded flowchart of an embodiment of the present invention;

[0060] Figure 4 This is a flowchart illustrating the process of an embodiment of the present invention;

[0061] Figure 5 This is a flowchart of the layer-protected pseudo-multichannel adaptive subtraction process according to an embodiment of the present invention;

[0062] Figure 6 is a set of comparison diagrams of actual seismic data tests according to an embodiment of the present invention;

[0063] Figure 6a It is a map of the original seismic data containing multiple waves;

[0064] Figure 6b It is a predicted multiple wave model;

[0065] Figure 6c It is the layer protection model Mask of the target layer;

[0066] Figure 7 is a comparison diagram of CMP gathers in an embodiment of the present invention;

[0067] Figure 7a It is a multi-channel adaptive subtraction CMP gather map of the unprotected target layer;

[0068] Figure 7b It is a multi-channel adaptive subtraction CMP gather map with protected target layers;

[0069] Figure 8 is a set of superimposed cross-sectional comparison diagrams of an embodiment of the present invention;

[0070] Figure 8a It is a multichannel adaptive subtraction stacked profile of the unprotected target layer;

[0071] Figure 8b It is a multi-channel adaptive subtraction superimposed profile of the protected target layer; Detailed Implementation

[0072] The present invention will be further described below with reference to specific embodiments, but the scope of protection of the present invention is not limited thereto.

[0073] Figure 1 , Figure 2 The figures show the overall flowchart and the detailed flowchart of the proposed multichannel adaptive subtraction method. Figure 1 , 2 As shown, an embodiment of the present invention includes a quasi-multichannel adaptive subtraction method for protecting target layers, comprising:

[0074] Multiple wave model prediction S101: Based on the stratigraphic data of the target layer, the predicted multiple wave model S201 is obtained through the multiple wave prediction method.

[0075] Extracting the primary wave model S102: Based on the stratigraphic data of the target layer, the primary wave model corresponding to the target layer is extracted using the phase axis matching method to obtain the extracted original primary wave model S202;

[0076] Determine the protection range S103: Based on the original primary wave model, through the exponential inverse processing after steepness adaptation, obtain the layer protection model Mask of the target layer to determine the protection range S203 of the original primary wave model.

[0077] Protecting the effective signal S104: Based on the original seismic data and the predicted multiple wave model, and by introducing the layer protection model Mask, the optimized layer protection pseudo-multi-channel adaptive subtraction processing after combining the pseudo-multi-channel layer protection model Mask is used to obtain the seismic data with layer protection after suppressing multiple waves, so as to protect the effective signal S204 in the original seismic data.

[0078] According to one embodiment of the present invention, such as Figure 1 , 2 As shown, the steepness adaptation process is as follows:

[0079] Based on the original first-wave model, the original first-wave model is subjected to steepness optimization by configuring the scaling factor of the layer protection model function to obtain an amplified first-wave model.

[0080] According to one embodiment of the present invention, such as Figure 3 , 4 As shown, the steepness optimization process includes:

[0081] Initialize the scale factor configuration, where the initial scale factor configuration should be no less than 100, with a typical value of 100;

[0082] Based on the original primary wave model and the scale factor of the initial configuration, a visualized primary wave model is generated through the initial amplification process of the layer protection model function.

[0083] Based on the visualized, initially magnified primary wave model, determine its steepness suitability:

[0084] If its steepness is not suitable, the scale factor is adjusted to adapt its steepness to the needs of steepness optimization.

[0085] If the steepness is appropriate, then the amplified primary wave model is determined.

[0086] According to one embodiment of the present invention, such as Figure 3 , 4 As shown, the exponential inverse processing includes:

[0087] Based on the amplified primary wave model, the amplified primary wave model is exponentially reversed by running the natural exponential function of the layer protection model function to form the layer protection model Mask, thereby determining the protection range of the original primary wave model.

[0088] According to one embodiment of the present invention, such as Figure 3 , 4 As shown, the layer protection model function is:

[0089] Mask = exp(-scale × Primarys) 2 )

[0090] Wherein, exp() is the natural exponential function; scale is a scale factor that adjusts the steepness of the Mask boundary of the layer protection model; Primaries is the original first-wave model; and Mask is the Mask of the layer protection model.

[0091] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the pseudo-multi-layer protection model Mask combination includes:

[0092] Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the combined multiple wave model are determined by combining the quasi-multichannel layer protection with the quasi-multichannel adaptive function of the layer protection. This allows the layer protection model Mask to be introduced into the original seismic data and the multiple wave model.

[0093] Specifically, the layer-protection quasi-multichannel adaptive function is a specially designed function to introduce a layer protection mechanism in multichannel seismic data processing, ensuring that primary wave data of the target layer is effectively protected during the removal of multiples. The quasi-multichannel layer-protection combination of the layer-protection quasi-multichannel adaptive function involves introducing a layer protection model mask into the original seismic data to obtain combined seismic data, and then introducing the layer protection model mask into the predicted multiples model to obtain a quasi-multichannel multiples combined model. This protects the primary wave data of the target layer while optimizing the multiples removal effect.

[0094] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the optimization layer protection pseudo-multichannel adaptive subtraction processing includes:

[0095] Based on combined seismic data and a pseudo-multichannel multiple wave combined model, the pseudo-multichannel layer protection optimization subtraction of the layer protection pseudo-multichannel adaptive function is used to obtain the seismic data after suppressed multiple waves with layer protection, so as to remove the pseudo-multichannel protection target layer data in the original seismic data during the multiple wave model process.

[0096] In this embodiment, the optimized layer-protected pseudo-multichannel adaptive subtraction processing is performed using a layer-protected pseudo-multichannel adaptive function, based on the combined seismic data and the pseudo-multichannel multiple wave combination model. The aim is to remove multiple waves while ensuring effective protection of the primary wave data at the target layer, thereby generating high-quality seismic data with layer protection.

[0097] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the multi-channel cumulative equalization process includes:

[0098] Based on the accumulated data of each pseudo-multichannel protection target layer, the multichannel accumulated equalization of the layer protection pseudo-multichannel adaptive function is used to obtain the seismic data after the suppression of multiple waves with layer protection, so as to remove the multichannel protection target layer data in the process of removing multiple wave model from the original seismic data.

[0099] In this embodiment, after introducing a layer protection mechanism, cumulative equalization processing is performed on the multichannel seismic data. This step aims to balance the differences between the data channels and improve the overall quality and consistency of the data. The target protected layer data (pseudo-multichannel protection target layer data) refers to the primary wave data of the target layer that needs to be protected during the processing. This embodiment obtains multichannel suppressed multiple wave seismic data with layer protection by performing multichannel cumulative equalization processing based on the accumulated target protected layer data of each pseudo-multichannel protection channel and utilizing a layer protection pseudo-multichannel adaptive function.

[0100] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the layer protection pseudo-multichannel adaptive function is:

[0101]

[0102] The layer protection pseudo-multichannel adaptive function is a layer protection four-pseudo-channel adaptive function;

[0103] Wherein, d is the original seismic data vector; s1, s2, s3, and s4 are the corresponding adaptive matched filters for the four pseudo-traces; M1, M2, M3, and M4 are the corresponding predicted multiple wave models for the four pseudo-traces, and the multiple wave models are represented by multiple wave convolution matrices; ⊙ is the Hadamard product; and Mask is the layer protection model Mask.

[0104] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the multiple wave model is as follows:

[0105]

[0106] Wherein, m(t) is the predicted multiple of a single channel; N is the length of the single channel data; and n is the length of the adaptive matched filter.

[0107] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the original seismic data is as follows:

[0108] d(t)={d(0), d(1), d(2)…d(N)}

[0109] Where t is a natural number 1…N.

[0110] According to one embodiment of the present invention, such as Figure 1 , 2 As shown, the multiple prediction method includes the extended interlayer multiple prediction method XIMP and the inverse scattering method.

[0111] According to one embodiment of the present invention, such as Figure 1 , 2 As shown, the in-phase shaft matching method includes:

[0112] Based on the stratigraphic data of the target stratigraphic level, pulse data constrained by stratigraphic time is generated. Based on the adaptive matching of the pulse data and the original seismic data, the original first wave model of the target stratigraphic level is extracted.

[0113] According to one embodiment of the present invention, such as Figure 1 , 2 As shown, the predicted multiple wave model also includes:

[0114] The raw seismic data is acquired and preprocessed to obtain a post-stack dataset. Based on the post-stack dataset, the stratigraphic data of the target layer is extracted. The preprocessing of the raw seismic data includes the data preprocessing process of observation system definition, short-path recovery, regularization, removal of direct waves, noise suppression, dynamic correction, static correction, stacking, and imaging.

[0115] Meanwhile, the present invention also provides a quasi-multichannel adaptive subtraction system for protecting target layers, comprising:

[0116] The system is used to implement the above-mentioned quasi-multichannel adaptive subtraction method, and includes:

[0117] Multiple wave model prediction module A101: used to obtain the predicted multiple wave model S201 based on the stratigraphic data of the target stratigraphic level through the multiple wave prediction method;

[0118] The primary wave model extraction module A102 is used to extract the primary wave model corresponding to the target layer based on the layer data of the target layer by using the phase axis matching method, so as to obtain the extracted original primary wave model S202.

[0119] The protection range determination module A103 is used to obtain the layer protection model Mask of the target layer based on the original primary wave model through exponential inverse processing after steepness adaptation, so as to determine the protection range S203 of the original primary wave model.

[0120] The effective signal protection module A104 is used to obtain the suppressed multiple wave seismic data with layer protection based on the original seismic data and the predicted multiple wave model. It is based on the introduction of the layer protection model Mask and the optimized layer protection pseudo-multi-channel adaptive subtraction processing after combining the pseudo-multi-channel layer protection model Mask, so as to protect the effective signal S204 in the original seismic data.

[0121] The distinguishing technical features of this case are:

[0122] Compared with existing and similar technologies, the adaptive subtraction method used in this case is different, that is, layer protection function is added to each adaptive subtraction method to form a corresponding layer protection adaptive subtraction method;

[0123] Specifically, this case adopts a combination of the quasi-multi-channel layer protection model Mask and optimized layer protection quasi-multi-channel adaptive subtraction processing. In addition, the layer protection model function and the layer protection quasi-multi-channel adaptive function are used in the specific function model.

[0124] Among them, the layer protection model function is:

[0125] Mask = exp(-scale × Primarys) 2 )

[0126] Among them, the layer-protected quasi-multichannel adaptive function is:

[0127]

[0128] The following is combined Figure 1 , 2 The working principle of the proposed multichannel adaptive subtraction method shown in the embodiments will be explained.

[0129] like Figure 1 , 2As shown, this pseudo-multichannel adaptive subtraction method includes functional modules such as multiple wave model prediction, extraction of the primary wave model, determination of the protection range, and protection of the effective signal. Key processes include determining the protection range and protecting the effective signal. The "determining the protection range" process includes steepness adaptation processing and exponential inversion processing, while the "protecting the effective signal" process includes combining pseudo-multichannel layer protection model masks, optimizing layer protection pseudo-multichannel adaptive subtraction processing, and multichannel cumulative equalization processing. In practice, determining the protection range involves obtaining the layer protection model mask of the target layer based on the original primary wave model through exponential inversion processing after steepness adaptation processing, thereby determining the protection range of the original primary wave model. Protecting the effective signal involves combining the original seismic data and the predicted multiple wave model. Based on the introduction of the layer protection model Mask, this paper proposes an optimized layer protection quasi-multichannel adaptive subtraction processing method by combining the quasi-multichannel layer protection model Mask with the original data. This method obtains seismic data with layer protection after suppression of multiple waves, thus protecting the effective signals in the original seismic data. Addressing the problem of damage to effective signals during the matching subtraction process in conventional quasi-multichannel adaptive subtraction methods, this paper improves upon the conventional method by converting the primary wave of the target layer into a layer protection Mask and incorporating the Mask into the quasi-multichannel adaptive subtraction method. This achieves the goal of protecting the target layer during the matching subtraction process. In summary, actual data testing verifies that this method can effectively protect the primary wave of the target layer, and the matching subtraction results are significantly optimized and improved.

[0130] The following is combined Figure 3 , 4 The working principle of the proposed multichannel adaptive subtraction method shown in the embodiments will be explained.

[0131] like Figure 3 , 4 As shown, the "determining the protection range" process includes steepness adaptation processing and exponential inversion processing. "Stiffness adaptation processing" involves optimizing the original primary wave model by configuring the scale factor of the layer protection model function to obtain an amplified primary wave model; that is, adjusting the steepness of the Mask boundary by configuring the scale factor. Simultaneously, "exponential inversion processing" involves exponentially inverting the amplified primary wave model by running the natural exponential function of the layer protection model function to form a layer protection model Mask, thereby determining the protection range of the original primary wave model. Through these processes, the target stratum primary wave is transformed into a layer protection Mask; that is, the target stratum primary wave is extracted from the target stratum information and transformed into a layer protection Mask using the following formula:

[0132] Mask = exp(-scale × Primarys) 2 )

[0133] Where exp represents the natural exponential function; the scale factor is used to adjust the steepness of the mask boundary, the larger the value, the steeper the mask boundary; and Primarys are the primary waves of the extracted target layers.

[0134] Mask=exp(-scale×Primaries2)

[0135] like Figure 3 As shown, firstly, the seismic data is preliminarily processed; secondly, the horizon file is imported into the seismic data, and the primary wave of the corresponding horizon is extracted using the horizon prediction multiple wave model data; thirdly, the primary wave is converted into the corresponding horizon protection mask using the above formula; finally, the multiple wave is subtracted using the improved horizon protection pseudo-multichannel adaptive subtraction method; the verification with actual seismic data proves that this method can effectively protect the primary wave of the target horizon, and the matching subtraction results are significantly optimized and improved.

[0136] The proposed multichannel adaptive subtraction method involves the following layer protection model functions:

[0137] Mask = exp(-scale × Primarys) 2 )

[0138] To prepare for the next stage of processing the quasi-multi-channel protection model, which combines Mask with optimized layer protection quasi-multi-channel adaptive subtraction processing, the protection range of the original primary wave model was clarified.

[0139] Meanwhile, the "determining the protection range" process also includes a steepness optimization process. Simply put, the scale factor can generally be a positive real number greater than 100. The larger the value, the larger the protection range, but the steeper the boundary of the range. The choice of Mask must ensure that the protection range is as large as possible, but at the same time, the boundary of the range must have a certain smooth slope. Therefore, the setting of the scale value must balance these two effects, and the default value is 1000. The process includes: first, initializing the first scale factor, then generating an enlarged primary wave model, observing the enlarged primary wave model, and judging whether the steepness angle and smoothness meet the needs of "steepness adaptation processing" through engineering experience. If the requirements are met, the scale factor is determined, and the enlarged primary wave model based on the scale factor is determined. If the requirements are not met, within the compensation range, the scale factor is configured in an adjustment manner to adapt its steepness to the needs of steepness optimization processing, thereby finally forming a suitable protection range for the original primary wave model.

[0140] The following is combined Figure 3 ,4 The working principle of the proposed multichannel adaptive subtraction method shown in the embodiments will be explained.

[0141] like Figure 3 , 4 As shown, the "quasi-multi-channel layer protection model mask combination" process includes quasi-multi-channel layer protection model mask combination and optimized layer protection quasi-multi-channel adaptive subtraction processing. The "quasi-multi-channel layer protection model mask combination" involves combining the original seismic data and the predicted multiple wave model using a quasi-multi-channel layer protection adaptive function to determine the combined seismic data and the quasi-multi-channel multiple wave model, thus introducing the layer protection model mask into the original seismic data and multiple wave model. Simultaneously, the "optimized layer protection quasi-multi-channel adaptive subtraction processing" involves optimizing the subtraction of the combined seismic data and the quasi-multi-channel multiple wave model using a quasi-multi-channel layer protection adaptive function to obtain seismic data with layer protection after suppressing multiple waves, thereby removing the target protected layer data of the quasi-multi-channel protection from the original seismic data during the multiple wave model process. To achieve layer protection quasi-multi-channel adaptive subtraction, the layer protection mask is introduced into the conventional quasi-multi-channel adaptive subtraction method, and the layer protection quasi-multi-channel adaptive subtraction method can be derived. During the calculation, the layer protection mask needs to be applied separately to the original seismic data and the predicted multiple wave data; the conventional pseudo-multichannel adaptive subtraction method can be expressed as:

[0142]

[0143] Where d represents the original seismic record and s represents the adaptive matched filter.

[0144] The predicted multiple convolution matrix M is:

[0145]

[0146] Where m(t) is the predicted multiple wave and N is the length of the adaptive filter.

[0147] The original seismic data is as follows:

[0148] d(t)={d(0), d(1), d(2)…d(N)}

[0149] Where t is a natural number 1…N;

[0150] After introducing the layer protection mask, the improved layer protection pseudo-multichannel adaptive subtraction formula can be obtained:

[0151]

[0152] Where E is the least squares error function, Mask is the layer protection matrix, ⊙ is the Hadamard product, and the meanings of the other symbols are the same as in the above formula.

[0153] Through algorithm improvements, the layer protection pseudo-multichannel adaptive subtraction method can reasonably apply effective protection to the primary wave of the target layer and optimize the matching subtraction results.

[0154] like Figure 3 As shown, the entire process includes, firstly, the data preparation stage:

[0155] (1) Including but not limited to the following processing steps: observation system definition, short-path recovery, regularization, removal of direct waves, noise suppression, dynamic correction, static correction, stacking, and imaging.

[0156] (2) Extract the layers that need to be protected from the post-stack data, import the layer files into the seismic data, use the layer to predict the multiple wave model data M, and extract the primary waves of the corresponding layers.

[0157] Secondly, there is the layer-protected quasi-multichannel adaptive subtraction stage:

[0158] (1) The primary wave is converted into the corresponding layer protection mask using the method (layer protection model function) proposed in this patent;

[0159] (2) The improved layer protection pseudo-multichannel adaptive subtraction method (layer protection pseudo-multichannel adaptive function) is used to subtract multiple waves.

[0160] Finally, there is the result output stage: outputting the calculation results, including the first wave after suppressing multiple waves, the subtracted multiple waves, and the adaptive filter.

[0161] The layer-protected quasi-multichannel adaptive subtraction method involved in this method includes:

[0162]

[0163] To obtain seismic data with layered protection after suppressed multiples, and to remove the target protected layer data from the original seismic data during the multiple modeling process, the goal is to protect the target layer during the matching subtraction process based on the seismic data with layered protection after suppressed multiples.

[0164] At the same time, this case once again combines Figure 5 As shown, the entire layer protection pseudo-multi-channel adaptive subtraction process is further described:

[0165] The specific implementation steps of layer protection quasi-multichannel adaptive subtraction are as follows:

[0166] Step 1: Preliminary processing of seismic data to obtain stacked or migrated data volumes;

[0167] Step 2: Extract the target layer from the stacked or migrated data volume and import it into the seismic data;

[0168] Step 3: Predict the multiple wave model of the target layer, and extract the primary wave of the target layer at the same time;

[0169] Step 4: Use the layer protection model function to convert the primary wave of the target layer into a layer protection mask;

[0170] Step 5: Load the seismic data, multiple wave model, and Mask into the layer protection pseudo-multichannel adaptive subtraction algorithm (layer protection pseudo-multichannel adaptive function) to obtain the final layer protection multiple wave suppression result.

[0171] The following is combined Figure 1 , 2 The working principle of the proposed multichannel adaptive subtraction method shown in the embodiments will be explained.

[0172] like Figure 1 , 2 As shown, the proposed multichannel adaptive subtraction method also includes a data preparation stage, which includes multiple wave model prediction and primary wave model extraction. The purpose of these two processes is to obtain the predicted multiple wave model and the extracted primary wave model. The "multichannel wave model prediction" involves obtaining the predicted multiple wave model based on the stratigraphic data of the target layer using the multiple wave prediction method. Meanwhile, the primary wave model extraction involves obtaining the primary wave model corresponding to the target layer based on the stratigraphic data of the target layer using the phase axis matching method to obtain the extracted primary wave model. The above methods complete the data preparation work of the data preparation stage, providing a data foundation for the next stage of steepness adaptive processing and exponential inverse processing.

[0173] like Figure 1 , 2As shown, the proposed multichannel adaptive subtraction method also includes a data preprocessing stage, which includes: acquiring raw seismic data and preprocessing the raw seismic data to obtain a post-stack dataset; and extracting the layer data of the target layer based on the post-stack dataset. The preprocessing of the raw seismic data includes the data preprocessing process of observation system definition, near-path recovery, regularization, removal of direct waves, noise suppression, dynamic correction, static correction, stacking, and imaging. The true multiples in the raw data refer to the original data containing both primary waves (effective signals) and multiples (noise). The so-called "true multiples" refers to the multiples contained in the raw data. The predicted multiples refer to the multiples calculated by a certain prediction method, such as model-driven or data-driven methods, which have certain differences from the true multiples in the raw seismic data. Furthermore, the original data (containing primary waves + true multiples) and the predicted multiples are adaptively subtracted to obtain the primary waves. The data preprocessing stage includes, but is not limited to, observation system definition, short-path recovery, regularization, removal of direct waves, noise suppression, dynamic correction, static correction, stacking, and imaging. Then, the layers to be protected are extracted from the post-stack data, and finally, the layer files are imported into the seismic data.

[0174] The working principle of the proposed multichannel adaptive subtraction method will be explained below with reference to the embodiments shown in Figures 6, 7, and 8.

[0175] In existing technologies, based on different assumptions and prior conditions, multiple suppression methods can be broadly classified into three categories: filtering methods, wave equation prediction subtraction methods, and machine learning methods. Direct filtering methods based on signal analysis mainly utilize digital signal filtering techniques to suppress multiples based on the characteristic differences between primary and multiple waves in a specific domain, such as predictive deconvolution, FK filtering, Radon transform, and cluster filtering. Wave equation prediction subtraction methods involve both prediction and subtraction processes and have wider applicability. Multiple prediction can be performed in two ways: model-driven and data-driven. Model-driven methods require prior knowledge of the subsurface model's parameters and are often used for processing seabed-related multiples in marine seismic data. Data-driven methods, such as Surface Correlated Multiple Removal (SRME) and Backscatter Surface Multiple Removal, do not require prior information from subsurface models and directly utilize raw seismic data to suppress multiples. Machine learning methods focus on extracting features from seismic data through trained neural networks; once the neural network is successfully trained, multiples can be removed quickly and effectively. Since the predicted multiples differ from the actual multiples in the raw seismic data, adaptive subtraction methods are needed to match and subtract the predicted multiples from the raw seismic data to achieve good multiple suppression. Commonly used adaptive subtraction methods for multiples include independent component analysis, mode matched filtering, non-steady-state linear regression, higher-order sparse Radon transform, and least squares adaptive matched filtering. Because the multiples and primary waves in the raw data do not completely satisfy the orthogonality condition (i.e., have strict separability), the effective signal is usually damaged during adaptive subtraction. This invention improves the conventional quasi-multichannel adaptive subtraction method by converting the primary wave of the target layer into a layer protection mask and introducing the mask into the quasi-multichannel adaptive subtraction method, achieving the goal of protecting the target layer during the matching subtraction process.

[0176] As mentioned above, this case provides a summary of the existing technology. Next, we will further describe the technical effects obtained by the proposed multichannel adaptive subtraction method based on the experimental results in conjunction with Figures 6, 7, and 8.

[0177] As shown in Figures 6, 7, and 8, in this data test, Figure 6 shows the actual seismic data test, in which... Figure 6a For raw seismic data containing multiple waves, Figure 6b For the predicted multiple wave model, Figure 6cTo extract the primary wave based on the layer and transform it using the layer protection model function, the target layer mask is obtained. The original seismic data, multiple wave model, and mask shown in Figure 6 are loaded into the layer protection pseudo-multichannel adaptive subtraction algorithm (layer protection pseudo-multichannel adaptive function) to obtain the final layer protection multiple wave suppression results, as shown in Figures 7 and 8. Figure 7 shows the layer protection pseudo-multichannel adaptive subtraction results on the CMP gather, where... Figure 7a , Figure 7b The figures show the multichannel adaptive subtraction results with and without layer protection, respectively. It can be seen that after applying layer protection, the energy and continuity of the phase axis of the target layer shown in the box are significantly enhanced. To further illustrate the advantages of the layer protection method, the matching subtraction results are superimposed and plotted as Figure 8, where... Figure 8a , Figure 8b These are the multichannel adaptive subtraction results for unprotected and protected layers, respectively. From the stacked profiles, it can be seen that the stacked profile with protected layers (…) Figure 8b The energy and continuity of the phase axis of the target layer shown in the box are significantly better than those of the stacked profile without layer protection. Figure 8a In summary, through verification with actual seismic data, this method can effectively protect the primary wave of the target layer, and the matching subtraction results are significantly optimized and improved.

[0178] Another embodiment of this application relates to a pseudo-multichannel adaptive subtraction device for protecting a target layer. The implementation details of the pseudo-multichannel adaptive subtraction device for protecting a target layer in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution. The pseudo-multichannel adaptive subtraction device for protecting a target layer in this embodiment includes a multiple wave model prediction module, a primary wave model extraction module, a protection range determination module, and an effective signal protection module.

[0179] The multiple wave model prediction module is used to obtain the predicted multiple wave model based on the stratigraphic data of the target stratigraphic level using a multiple wave prediction method.

[0180] The primary wave model extraction module is used to extract the primary wave model corresponding to the target layer based on the layer data of the target layer by using the phase axis matching method, so as to obtain the extracted original primary wave model.

[0181] The protection range determination module is used to obtain the layer protection model Mask of the target layer based on the original primary wave model through exponential inverse processing after steepness adaptation, so as to determine the protection range of the original primary wave model.

[0182] The effective signal protection module is used to obtain suppressed multiple seismic data with layer protection based on the original seismic data and the predicted multiple wave model. It introduces a layer protection model Mask and then performs optimized layer protection pseudo-multichannel adaptive subtraction processing after combining the pseudo-multichannel layer protection model Mask, so as to protect the effective signal in the original seismic data.

[0183] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0184] In some embodiments, the protection scope determination module is used to:

[0185] Based on the original first-wave model, the original first-wave model is subjected to steepness optimization by configuring the scaling factor of the layer protection model function to obtain an amplified first-wave model.

[0186] In some embodiments, the protection scope determination module is further configured to:

[0187] Initialize the scale factor configuration, where the initial scale factor configuration should be no less than 100, with a typical value of 100;

[0188] Based on the original primary wave model and the scale factor of the initial configuration, a visualized primary wave model is generated through the initial amplification process of the layer protection model function.

[0189] Based on the visualized, initially magnified primary wave model, determine its steepness suitability:

[0190] If its steepness is not suitable, the scale factor is adjusted to adapt its steepness to the needs of steepness optimization.

[0191] If the steepness is appropriate, then the amplified primary wave model is determined.

[0192] In some embodiments, the protection scope determination module is further configured to:

[0193] Based on the amplified primary wave model, the amplified primary wave model is exponentially reversed by running the natural exponential function of the layer protection model function to form the layer protection model Mask, thereby determining the protection range of the original primary wave model.

[0194] In some embodiments, the effective signal protection module is used for:

[0195] Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the combined multiple wave model are determined by combining the quasi-multichannel layer protection with the quasi-multichannel adaptive function of the layer protection. This allows the layer protection model Mask to be introduced into the original seismic data and the multiple wave model.

[0196] In some embodiments, the effective signal protection module is further configured to:

[0197] Based on combined seismic data and a pseudo-multichannel multiple wave combined model, the pseudo-multichannel layer protection optimization subtraction of the layer protection pseudo-multichannel adaptive function is used to obtain the seismic data after suppressed multiple waves with layer protection, so as to remove the pseudo-multichannel protection target layer data in the original seismic data during the multiple wave model process.

[0198] In some embodiments, the primary wave model extraction module is used for:

[0199] Based on the stratigraphic data of the target stratigraphic level, pulse data constrained by stratigraphic time is generated. Based on the adaptive matching of the pulse data and the original seismic data, the original first wave model of the target stratigraphic level is extracted.

[0200] In some embodiments, the device is further configured to:

[0201] The raw seismic data is acquired and preprocessed to obtain a post-stack dataset. Based on the post-stack dataset, the stratigraphic data of the target layer is extracted. The preprocessing of the raw seismic data includes the data preprocessing process of observation system definition, short-path recovery, regularization, removal of direct waves, noise suppression, dynamic correction, static correction, stacking, and imaging.

[0202] Another embodiment of this application relates to an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the quasi-multichannel adaptive subtraction method for protecting target layers in the above embodiments.

[0203] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0204] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0205] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0206] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0207] The various embodiments in this invention are described in a progressive manner. For the same or similar parts between the various embodiments, please refer to each other. Each embodiment focuses on describing the differences from other embodiments.

[0208] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features.

[0209] The scope of protection of this invention is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its scope and spirit. If these modifications and variations fall within the scope of the claims of this invention and their equivalents, then the intent of this invention also includes these modifications and variations.

Claims

1. A quasi-multichannel adaptive subtraction method for protecting target layers, characterized in that, include: Multiple wave model prediction: Based on the stratigraphic data of the target layer, a predicted multiple wave model is obtained through a multiple wave prediction method; Extracting the primary wave model: Based on the stratigraphic data of the target layer, the primary wave model corresponding to the target layer is extracted using the phase axis matching method to obtain the extracted original primary wave model; Determine the protection range: Based on the original primary wave model, through exponential inverse processing after steepness adaptation, obtain the layer protection model Mask of the target layer to determine the protection range of the original primary wave model; Protecting effective signals: Based on the original seismic data and the predicted multiple wave model, and by introducing the layer protection model Mask, the optimized layer protection pseudo-multi-channel adaptive subtraction processing after combining the pseudo-multi-channel layer protection model Mask is used to obtain the seismic data with layer protection after suppressing multiple waves, so as to protect the effective signals in the original seismic data.

2. The pseudo-multichannel adaptive subtraction method according to claim 1, characterized in that, The steepness adaptation process: Based on the original first-wave model, the original first-wave model is subjected to steepness optimization by configuring the scaling factor of the layer protection model function to obtain an amplified first-wave model.

3. The pseudo-multichannel adaptive subtraction method according to claim 2, characterized in that, The steepness optimization process includes: Initialize the scale factor configuration, where the initial scale factor configuration should be no less than 100, with a typical value of 100; Based on the original primary wave model and the scale factor of the initial configuration, a visualized primary wave model is generated through the initial amplification process of the layer protection model function. Based on the visualized, initially magnified primary wave model, determine its steepness suitability: If its steepness is not suitable, the scale factor is adjusted to adapt its steepness to the needs of steepness optimization. If the steepness is appropriate, then the amplified primary wave model is determined.

4. The pseudo-multichannel adaptive subtraction method according to claim 1, characterized in that, The exponential inverse processing includes: Based on the amplified primary wave model, the amplified primary wave model is exponentially reversed by running the natural exponential function of the layer protection model function to form the layer protection model Mask, thereby determining the protection range of the original primary wave model.

5. The pseudo-multichannel adaptive subtraction method according to claim 2 or 4, characterized in that, include: The layer protection model function is: Mask=exp(-scale×Primaries 2 ) Wherein, exp() is the natural exponential function; scale is a scale factor that adjusts the steepness of the Mask boundary of the layer protection model; Primaries is the original first-wave model; and Mask is the Mask of the layer protection model.

6. The pseudo-multichannel adaptive subtraction method according to claim 1, characterized in that, The pseudo-multi-layer protection model Mask combination includes: Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the combined multiple wave model are determined by combining the quasi-multichannel layer protection with the quasi-multichannel adaptive function of the layer protection. This allows the layer protection model Mask to be introduced into the original seismic data and the multiple wave model.

7. The pseudo-multichannel adaptive subtraction method according to claim 6, characterized in that, The optimization layer protection pseudo-multichannel adaptive subtraction process includes: Based on combined seismic data and a pseudo-multichannel multiple wave combined model, the pseudo-multichannel layer protection optimization subtraction of the layer protection pseudo-multichannel adaptive function is used to obtain the seismic data after suppressed multiple waves with layer protection, so as to remove the pseudo-multichannel protection target layer data in the original seismic data during the multiple wave model process.

8. The pseudo-multichannel adaptive subtraction method according to claim 6 or 7, characterized in that, include: The layer protection pseudo-multichannel adaptive function is: The layer protection pseudo-multichannel adaptive function is a layer protection four-pseudo-channel adaptive function; Wherein, d is the original seismic data vector; s1, s2, s3, and s4 are the corresponding adaptive matched filters for the four pseudo-traces; M1, M2, M3, and M4 are the corresponding predicted multiple wave models for the four pseudo-traces, and the multiple wave models are represented by multiple wave convolution matrices; ⊙ is the Hadamard product; and Mask is the layer protection model Mask.

9. The pseudo-multichannel adaptive subtraction method according to claim 1, characterized in that, The multiple prediction methods include the extended interlayer multiple prediction method XIMP and the inverse scattering method.

10. The pseudo-multichannel adaptive subtraction method according to claim 1, characterized in that, The in-phase shaft matching method includes: Based on the stratigraphic data of the target stratigraphic level, pulse data constrained by stratigraphic time is generated. Based on the adaptive matching of the pulse data and the original seismic data, the original first wave model of the target stratigraphic level is extracted.

11. The pseudo-multichannel adaptive subtraction method according to claim 1, characterized in that, The predicted multiple wave model also includes: The raw seismic data is acquired and preprocessed to obtain a post-stack dataset. Based on the post-stack dataset, the stratigraphic data of the target layer is extracted. The preprocessing of the raw seismic data includes the data preprocessing process of observation system definition, short-path recovery, regularization, removal of direct waves, noise suppression, dynamic correction, static correction, stacking, and imaging.

12. A quasi-multichannel adaptive subtraction system for protecting target layers, characterized in that, include: The system is used to implement the quasi-multichannel adaptive subtraction method as described in any one of claims 1-11, and includes: Multiple wave model prediction module: used to obtain the predicted multiple wave model based on the stratigraphic data of the target stratigraphic level through the multiple wave prediction method; Primary wave model extraction module: Based on the stratigraphic data of the target stratigraphic level, the module extracts the primary wave model corresponding to the target stratigraphic level using the phase axis matching method to obtain the extracted original primary wave model; The protection range determination module is used to obtain the layer protection model Mask of the target layer based on the original primary wave model through exponential inverse processing after steepness adaptation, so as to determine the protection range of the original primary wave model. The module for protecting effective signals is used to obtain seismic data with layer protection after suppressing multiple waves based on the original seismic data and the predicted multiple wave model. This is achieved by introducing a layer protection model Mask and then using an optimized layer protection pseudo-multi-channel adaptive subtraction process after combining the pseudo-multi-channel layer protection model Mask with the original seismic data, thus protecting the effective signals in the original seismic data.

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