A balanced multichannel adaptive subtraction method and system for protecting target layers

Abstract

This invention relates to the field of geophysical exploration technology, specifically to a balanced multi-channel adaptive subtraction method and system for protecting target strata. The method includes multi-channel equalization processing, layer protection model Mask combination, and optimized layer protection adaptive subtraction processing. This method solves the problem that since the multiple waves and the primary waves in the original data do not 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 filed on December 28, 2024, application number 2024119634515, entitled "A balanced 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 balanced 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 the equalized multichannel adaptive subtraction method, and specifically for scenarios requiring suppression of multiple waves by predictive subtraction based on the wave equation, this invention provides an equalized multichannel adaptive subtraction method and system for protecting the target layer.

[0005] In a first aspect, embodiments of the present invention provide a balanced 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, the seismic data after suppressing multiple waves with layer protection is obtained by first performing multi-channel equalization processing on the basis of introducing the layer protection model Mask, and then performing optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, 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 multichannel equalization process includes:

[0024] Based on the protection layer data of each protection target, a multi-channel equalization processing model with layer protection is obtained through multi-channel equalization processing of the layer protection equalization multi-channel adaptive function, so as to perform multi-channel equalization processing before removing multiple wave models from the original seismic data.

[0025] Preferably, the layer protection model Mask combination includes:

[0026] Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the multichannel balanced multiple wave combined model are determined by combining the layer protection with the layer protection equalization multichannel adaptive function, so as to introduce the layer protection model Mask into the original seismic data and the multiple wave model.

[0027] Preferably, the optimized layer protection adaptive subtraction process includes:

[0028] Based on combined seismic data and a multichannel balanced multiple wave combined model, single-channel layer protection optimization subtraction of the layer protection balanced multichannel adaptive function is used to obtain seismic data with layer protection after suppression of multiple waves, so as to remove the primary wave data of the multichannel balanced target layer in the original seismic data.

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

[0030]

[0031] Wherein, d is the original seismic data vector; s represents the adaptive matched filter; and M... i The predicted multiple wave model is represented by a multiple wave convolution matrix; ⊙ is the Hadamard product; and Mask is the layer protection model Mask.

[0032] Preferably, the multiple wave model is:

[0033]

[0034] 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.

[0035] Preferably, the original seismic data is:

[0036] d(t)={d(0), d(1), d(2)…d(N)}

[0037] Where t is a natural number 1…N.

[0038] Preferably, the multiple prediction method includes the extended interlayer multiple prediction method XIMP and the inverse scattering method.

[0039] Preferably, the in-phase shaft matching method includes:

[0040] 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.

[0041] Preferably, the method further includes the following before the predicted multiple wave model:

[0042] 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.

[0043] On the other hand, the present invention also provides a multi-channel adaptive subtraction system for protecting target layers, comprising:

[0044] The system is used to implement the above-mentioned equalization multichannel adaptive subtraction method, and includes:

[0045] 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;

[0046] 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;

[0047] 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.

[0048] The effective signal protection module is used to obtain suppressed multiple wave seismic data with layer protection based on the original seismic data and the predicted multiple wave model. It first performs multi-channel equalization processing on the basis of introducing the layer protection model Mask, and then performs optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, so as to obtain the effective signal in the original seismic data.

[0049] At least one embodiment of this application also provides a balanced multichannel adaptive subtraction device for protecting target layers, comprising:

[0050] 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.

[0051] 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.

[0052] 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.

[0053] The effective signal protection module 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. It introduces the layer protection model Mask, first performs multi-channel equalization processing, and then performs optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, so as to obtain the effective signal in the original seismic data.

[0054] 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 balanced multichannel adaptive subtraction method for protecting target layers.

[0055] 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 balanced multichannel adaptive subtraction method for protecting target layers. Attached Figure Description

[0056] Figure 1 This is a general flowchart of an embodiment of the present invention;

[0057] Figure 2 This is a detailed flowchart of an embodiment of the present invention;

[0058] Figure 3 This is an exploded flowchart of an embodiment of the present invention;

[0059] Figure 4 This is a flowchart illustrating the process of an embodiment of the present invention;

[0060] Figure 5 This is a flowchart of the layer protection equalization multichannel adaptive subtraction process according to an embodiment of the present invention;

[0061] Figure 6 is a set of comparison diagrams of actual seismic data tests according to an embodiment of the present invention;

[0062] Figure 6a It is a map of the raw seismic data containing multiple waves;

[0063] Figure 6b It is a predicted multiple wave model;

[0064] Figure 6c It is the layer protection model Mask of the target layer;

[0065] Figure 7 is a comparison diagram of CMP gathers in an embodiment of the present invention;

[0066] Figure 7a It is a multi-channel adaptive subtraction CMP gather map of the unprotected target layer;

[0067] Figure 7b It is a multi-channel adaptive subtraction CMP gather map with protected target layers;

[0068] Figure 8 is a set of superimposed cross-sectional comparison diagrams of an embodiment of the present invention;

[0069] Figure 8a It is a multichannel adaptive subtraction stacked profile of the unprotected target layer;

[0070] Figure 8b It is a multi-channel adaptive subtraction superimposed profile of the protected target layer; Detailed Implementation

[0071] 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.

[0072] Figure 1 , Figure 2 The following are the overall flowchart and detailed flowchart of this balanced multichannel adaptive subtraction method. Figure 1 , 2 As shown, an embodiment of the present invention includes a balanced multichannel adaptive subtraction method for protecting target layers, comprising:

[0073] 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.

[0074] 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;

[0075] 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.

[0076] Protecting the effective signal S104: Based on the original seismic data and the predicted multiple wave model, and with the introduction of the layer protection model Mask, the data is first processed by multi-channel equalization, and then by optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, 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.

[0077] According to one embodiment of the present invention, such as Figure 1 , 2 As shown, the steepness adaptation process is as follows:

[0078] 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.

[0079] According to one embodiment of the present invention, such as Figure 3 , 4 As shown, the steepness optimization process includes:

[0080] Initialize the scale factor configuration, where the initial scale factor configuration should be no less than 100, with a typical value of 100;

[0081] 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.

[0082] Based on the visualized, initially magnified primary wave model, determine its steepness suitability:

[0083] If its steepness is not suitable, the scale factor is adjusted to adapt its steepness to the needs of steepness optimization.

[0084] If the steepness is appropriate, then the amplified primary wave model is determined.

[0085] According to one embodiment of the present invention, such as Figure 3 , 4 As shown, the exponential inverse processing includes:

[0086] 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.

[0087] According to one embodiment of the present invention, such as Figure 3 , 4 As shown, the layer protection model function is:

[0088] Mask = exp(-scale × Primarys) 2 )

[0089] 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.

[0090] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the multi-channel equalization process includes:

[0091] Based on the protection layer data of each protection target, a multi-channel equalization processing model with layer protection is obtained through multi-channel equalization processing of the layer protection equalization multi-channel adaptive function, so as to perform multi-channel equalization processing before removing multiple wave models from the original seismic data.

[0092] In this embodiment, "protected target layer data for each trace" refers to the primary wave data of the target layer that is specifically protected for each recording channel (each trace) during seismic data processing. The purpose of equalization processing is to adjust the energy differences between different traces, making the seismic data more spatially balanced, thus facilitating subsequent processing and interpretation. The layer protection equalization multi-trace adaptive function is a specially designed function used to introduce a layer protection mechanism in multi-trace equalization processing, ensuring that the primary wave data of the target layer is effectively protected.

[0093] In other words, multichannel equalization processing uses a layer-protected equalization adaptive function to equalize the raw seismic data, while simultaneously introducing a layer-protection model (Mask) to ensure effective protection of primary wave data at the target layer. The purpose of this step is to improve the overall quality of the seismic data while removing multiple waves, thus protecting the valid signals and providing a better data foundation for subsequent processing.

[0094] The layer protection model Mask combination includes:

[0095] Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the multichannel balanced multiple wave combined model are determined by combining the layer protection with the layer protection equalization multichannel adaptive function, so as to introduce the layer protection model Mask into the original seismic data and the multiple wave model.

[0096] Specifically, the raw seismic data is unprocessed seismic data, containing primary and secondary waves. The predicted secondary wave model is obtained through a pre-defined secondary wave prediction method and is used for subsequent secondary wave removal. The layer protection equalization multichannel adaptive function is used not only for multichannel equalization processing but also to introduce the layer protection model mask into both the raw seismic data and the predicted secondary wave model. That is, the layer protection model mask is introduced into the raw seismic data to obtain combined seismic data, and the layer protection model mask is introduced into the secondary wave model to obtain a multichannel equalized secondary wave combined model.

[0097] This embodiment combines layer protection with the generated combined seismic data and multichannel balanced multiple wave combined model, which can better adapt to the characteristics of the target layer and improve the effect of multiple wave removal.

[0098] The optimized layer protection adaptive subtraction process includes:

[0099] Based on combined seismic data and a multichannel balanced multiple wave combined model, single-channel layer protection optimization subtraction of the layer protection balanced multichannel adaptive function is used to obtain seismic data with layer protection after suppression of multiple waves, so as to remove the primary wave data of the multichannel balanced target layer in the original seismic data.

[0100] Specifically, the single-channel layer protection optimization subtraction of the layer protection equalization multi-channel adaptive function refers to the use of the layer protection equalization multi-channel adaptive function in this step to perform single-channel layer protection optimization subtraction, that is, to perform optimization subtraction processing on each channel separately to ensure that the primary wave data of the target layer in each channel is protected.

[0101] The subtraction process may include: in each trace, matching and subtracting the combined seismic data with a multi-trace equalized multiple wave combined model to remove multiple waves. An adaptive matched filter is used to dynamically adjust parameters during the subtraction process to optimize layer protection. During the subtraction process, a layer protection model mask is used to ensure that primary wave data at the target layer are not excessively suppressed.

[0102] According to one embodiment of the present invention, such as Figure 3 , 4 As shown in Figure 5, the layer protection equalization multichannel adaptive function is:

[0103]

[0104] Wherein, d is the original seismic data vector; s represents the adaptive matched filter; and M... i The predicted multiple wave model is represented by a multiple wave convolution matrix; ⊙ is the Hadamard product; and Mask is the layer protection model Mask.

[0105] 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:

[0106]

[0107] 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.

[0108] 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:

[0109] d(t)={d(0), d(1), d(2)…d(N)}

[0110] Where t is a natural number 1…N.

[0111] 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.

[0112] According to one embodiment of the present invention, such as Figure 1 , 2 As shown, the in-phase shaft matching method includes:

[0113] 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.

[0114] According to one embodiment of the present invention, such as Figure 1 , 2 As shown, the predicted multiple wave model also includes:

[0115] 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.

[0116] Meanwhile, the present invention also provides a multi-channel adaptive subtraction system for protecting target layers, comprising:

[0117] The system is used to implement the above-mentioned equalization multichannel adaptive subtraction method, and includes:

[0118] 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;

[0119] 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.

[0120] 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.

[0121] 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 first undergoes multi-channel equalization processing based on the layer protection model Mask, and then undergoes optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, so as to protect the effective signal S204 in the original seismic data.

[0122] The distinguishing technical features of this case are:

[0123] 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;

[0124] Specifically, this case adopts a multi-channel equalization process, followed by an optimized layer protection adaptive subtraction process after combining the layer protection model Mask. In addition, the layer protection model function and the layer protection equalization multi-channel adaptive function are used in the specific function model.

[0125] Among them, the layer protection model function is:

[0126] Mask = exp(-scale × Primarys) 2 )

[0127] Among them, the layer protection equalization multichannel adaptive function is:

[0128]

[0129] The following is combined Figure 1 , 2 The working principle of this balanced multichannel adaptive subtraction method, as shown in the embodiment, will be explained.

[0130] like Figure 1 , 2As shown, this balanced 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 layer protection model mask combination, optimized layer protection adaptive subtraction processing, and multichannel equalization processing. Specifically, determining the protection range involves obtaining the layer protection model mask for the target layer based on the original primary wave model through steepness adaptation processing and exponential inversion processing, thus 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, and then introducing the layer protection model... Based on the existing mask, the seismic data is first processed through multi-channel equalization, and then optimized through layer protection adaptive subtraction after combining the layer protection model mask to obtain seismic data with layer protection after suppressing multiple waves. This process protects the effective signals in the original seismic data. Addressing the problem of damage to effective signals during the matching subtraction process in conventional equalization multi-channel adaptive subtraction methods, this method improves upon conventional equalization multi-channel adaptive subtraction by converting the primary wave of the target layer into a layer protection mask and incorporating the mask into the equalization multi-channel 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.

[0131] The following is combined Figure 3 , 4 The working principle of this balanced multichannel adaptive subtraction method, as shown in the embodiment, will be explained.

[0132] 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:

[0133] Mask = exp(-scale × Primarys) 2 )

[0134] 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.

[0135] Mask = exp(-scale × Primarys) 2 )

[0136] 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 equalization 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.

[0137] The layer protection model function involved in this balanced multichannel adaptive subtraction method is:

[0138] Mask = exp(-scale × Primarys) 2 )

[0139] To facilitate the next stage of processing, the Mask combination of the layer protection model and the adaptive subtraction processing of the optimization layer protection were used to clarify the protection range of the original primary wave model.

[0140] 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 100. 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.

[0141] The following is combined Figure 3 ,4 The working principle of this balanced multichannel adaptive subtraction method, as shown in the embodiment, will be explained.

[0142] like Figure 3 , 4 As shown, the "layer protection model mask combination" process includes layer protection model mask combination and optimized layer protection adaptive subtraction processing. The "layer protection model mask combination" involves determining the combined seismic data and the multi-channel balanced multiple wave combination model based on the original seismic data and the predicted multiple wave model through layer protection equalization multi-channel adaptive function, thus introducing the layer protection model mask into the original seismic data and the multiple wave model. Simultaneously, the "optimized layer protection adaptive subtraction processing" involves optimizing the single-channel layer protection subtraction based on the combined seismic data and the multi-channel balanced multiple wave combination model through layer protection equalization multi-channel adaptive function, obtaining seismic data with layer protection after suppression of multiple waves, thus removing the primary wave data of the multi-channel equalization target layer in the original seismic data. To achieve layer protection equalization multi-channel adaptive subtraction, the layer protection mask is introduced into the conventional equalization multi-channel adaptive subtraction method, and the layer protection equalization multi-channel adaptive subtraction method can be derived. During the calculation process, the layer protection mask needs to be applied to both the original seismic data and the predicted multiple wave data. The conventional equalization multi-channel adaptive subtraction method can be expressed as:

[0143]

[0144] Where d represents the original seismic record and s represents the adaptive matched filter.

[0145] The predicted multiple convolution matrix M is:

[0146]

[0147] Where m(t) is the predicted multiple wave and N is the length of the adaptive filter.

[0148] The original seismic data is as follows:

[0149] d(t)={d(0), d(1), d(2)…d(N)}

[0150] Where t is a natural number 1…N;

[0151] After introducing the layer protection mask, the improved layer protection equalization multichannel adaptive subtraction formula can be obtained:

[0152]

[0153] 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.

[0154] Through algorithm improvements, the layer protection equalization multi-channel adaptive subtraction method can reasonably apply effective protection to the primary wave of the target layer and optimize the matching subtraction results.

[0155] like Figure 3 As shown, the entire process includes, firstly, the data preparation stage:

[0156] (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.

[0157] (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.

[0158] Secondly, there is the layer protection equalization multi-channel adaptive subtraction stage:

[0159] (1) The primary wave is converted into the corresponding layer protection mask using the method (layer protection model function) proposed in this patent;

[0160] (2) The improved layer protection equalization multichannel adaptive subtraction method (layer protection equalization multichannel adaptive function) is used to subtract the multiple waves.

[0161] 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.

[0162] The layer-protected equalization multichannel adaptive subtraction method involved in this method includes the following:

[0163]

[0164] To obtain seismic data with layer protection after suppression of multiple waves, and to remove primary wave data of the multi-channel equilibrium target layer in the original seismic data during the multiple wave modeling process, the aim is to protect the primary wave data of the multi-channel equilibrium target layer during the matching subtraction process based on the seismic data with layer protection after suppression of multiple waves.

[0165] At the same time, this case once again combines Figure 5 As shown, the entire layer protection equalization multi-channel adaptive subtraction process is further described:

[0166] The specific implementation steps of layer protection equalization multichannel adaptive subtraction are as follows:

[0167] Step 1: Preliminary processing of seismic data to obtain stacked or migrated data volumes;

[0168] Step 2: Extract the target layer from the stacked or migrated data volume and import it into the seismic data;

[0169] Step 3: Predict the multiple wave model of the target layer, and extract the primary wave of the target layer at the same time;

[0170] Step 4: Use the layer protection model function to convert the primary wave of the target layer into a layer protection mask;

[0171] Step 5: Load the seismic data, multiple wave model, and Mask into the layer protection equalization multichannel adaptive subtraction algorithm (layer protection equalization multichannel adaptive function) to obtain the final layer protection multiple wave suppression result.

[0172] The following is combined Figure 1 , 2 The working principle of this balanced multichannel adaptive subtraction method, as shown in the embodiment, will be explained.

[0173] like Figure 1 , 2 As shown, this balanced 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 "multiple wave model prediction" includes obtaining the predicted multiple wave model based on the stratigraphic data of the target layer through the multiple wave prediction method. At the same time, the primary wave model extraction includes obtaining the primary wave model corresponding to the target layer based on the stratigraphic data of the target layer through 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.

[0174] like Figure 1 , 2As shown, this equalization multichannel adaptive subtraction method also includes a data preprocessing stage. This stage includes: acquiring raw seismic data and preprocessing it to obtain a post-stack dataset; extracting the layer data of the target layer based on the post-stack dataset. The preprocessing of the raw seismic data includes observation system definition, near-path recovery, regularization, direct wave removal, 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 using a prediction method, such as model-driven or data-driven methods, which differ somewhat 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.

[0175] The working principle of this balanced multichannel adaptive subtraction method will be explained below with reference to the embodiments shown in Figures 6, 7, and 8.

[0176] 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 backscattering 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 equalized multichannel adaptive subtraction method by converting the primary wave of the target layer into a layer protection mask and introducing the mask into the equalized multichannel adaptive subtraction method, achieving the goal of protecting the target layer during the matching subtraction process.

[0177] As mentioned above, this case provides a summary of the existing technology. Next, we will further describe the technical effects obtained by this balanced multi-channel adaptive subtraction method based on the experimental results in conjunction with Figures 6, 7, and 8.

[0178] 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 equalized multi-channel adaptive subtraction algorithm (layer protection equalized multi-channel 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 equalized multi-channel 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.

[0179] Another embodiment of this application relates to a balanced multichannel adaptive subtraction device for protecting target layers. The implementation details of the balanced multichannel adaptive subtraction device for protecting target layers 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.

[0180] The equalization multichannel adaptive subtraction device for the protected 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The effective signal protection module 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. It introduces the layer protection model Mask, first performs multi-channel equalization processing, and then performs optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, so as to obtain the effective signal in the original seismic data.

[0185] 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.

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

[0187] 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.

[0188] In some embodiments, the protection scope determination module is further configured to:

[0189] Initialize the scale factor configuration, where the initial scale factor configuration should be no less than 100, with a typical value of 100;

[0190] 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.

[0191] Based on the visualized, initially magnified primary wave model, determine its steepness suitability:

[0192] If its steepness is not suitable, the scale factor is adjusted to adapt its steepness to the needs of steepness optimization.

[0193] If the steepness is appropriate, then the amplified primary wave model is determined.

[0194] In some embodiments, the protection scope determination module is further configured to:

[0195] 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.

[0196] In some embodiments, the effective signal protection module is used for:

[0197] Based on the protection layer data of each protection target, a multi-channel equalization processing model with layer protection is obtained through multi-channel equalization processing of the layer protection equalization multi-channel adaptive function, so as to perform multi-channel equalization processing before removing multiple wave models from the original seismic data.

[0198] In some embodiments, the effective signal protection module is further configured to:

[0199] Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the multichannel balanced multiple wave combined model are determined by combining the layer protection with the layer protection equalization multichannel adaptive function, so as to introduce the layer protection model Mask into the original seismic data and the multiple wave model.

[0200] In some embodiments, the effective signal protection module is further configured to:

[0201] Based on combined seismic data and a multichannel balanced multiple wave combined model, single-channel layer protection optimization subtraction of the layer protection balanced multichannel adaptive function is used to obtain seismic data with layer protection after suppression of multiple waves, so as to remove the primary wave data of the multichannel balanced target layer in the original seismic data.

[0202] In some embodiments, the primary wave model extraction module is used for:

[0203] 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.

[0204] In some embodiments, the device is further configured to:

[0205] 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.

[0206] 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 equalized multichannel adaptive subtraction method for protecting target layers in the above embodiments.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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 balanced 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, the seismic data after suppressing multiple waves with layer protection is obtained by first performing multi-channel equalization processing on the basis of introducing the layer protection model Mask, and then performing optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, so as to protect the effective signals in the original seismic data.

2. The equalization 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 equalization 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 equalization 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 equalization multichannel adaptive subtraction method according to claims 2 and 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 equalization multichannel adaptive subtraction method according to claim 1, characterized in that, The multichannel equalization process includes: Based on the protection layer data of each protection target, a multi-channel equalization processing model with layer protection is obtained through multi-channel equalization processing of the layer protection equalization multi-channel adaptive function, so as to perform multi-channel equalization processing before removing multiple wave models from the original seismic data.

7. The equalization multichannel adaptive subtraction method according to claim 6, characterized in that, The layer protection model Mask combination includes: Based on the original seismic data and the predicted multiple wave model, the combined seismic data and the multichannel balanced multiple wave combined model are determined by combining the layer protection with the layer protection equalization multichannel adaptive function, so as to introduce the layer protection model Mask into the original seismic data and the multiple wave model.

8. The equalization multichannel adaptive subtraction method according to claim 7, characterized in that, The optimized layer protection adaptive subtraction process includes: Based on combined seismic data and a multichannel balanced multiple wave combined model, single-channel layer protection optimization subtraction of the layer protection balanced multichannel adaptive function is used to obtain seismic data with layer protection after suppression of multiple waves, so as to remove the primary wave data of the multichannel balanced target layer in the original seismic data.

9. The balanced multichannel adaptive subtraction method according to claim 6, 7, or 8, characterized in that, include: The layer protection equalization multichannel adaptive function is: Wherein, d is the original seismic data vector; s represents the adaptive matched filter; and M... i The predicted multiple wave model is represented by a multiple wave convolution matrix; ⊙ is the Hadamard product; and Mask is the layer protection model Mask.

10. The equalization 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.

11. The equalization 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.

12. The equalization 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.

13. A multi-channel adaptive subtraction system for protecting target layers, characterized in that, include: The system is used to implement the balanced multichannel adaptive subtraction method as described in any one of claims 1-12, 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 effective signal protection module is used to obtain suppressed multiple wave seismic data with layer protection based on the original seismic data and the predicted multiple wave model. It first performs multi-channel equalization processing on the basis of introducing the layer protection model Mask, and then performs optimized layer protection adaptive subtraction processing after combining the layer protection model Mask, so as to obtain the effective signal in the original seismic data.

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