Seismic data processing method, device, equipment and storage medium

Abstract

This application discloses a seismic data processing method, apparatus, device, and storage medium, belonging to the field of seismic exploration technology. The method first predicts first seismic data using a first neural network model to obtain first predicted data. Based on the first seismic data and the first predicted data, some aliasing noise is separated from the first seismic data to obtain second seismic data. The second seismic data is then predicted using a second neural network model to obtain second predicted data. Based on the first seismic data and the second predicted data, aliasing noise is further separated from the first seismic data to obtain third seismic data. This process continues until the Nth neural network model in the target neural network model is used to obtain the data after removing aliasing noise. This method gradually separates aliasing noise through iterative prediction, overcoming the limitations of many assumptions and thus improving the separation effect of aliasing noise.

Description

Technical Field

[0001] This application relates to the field of seismic exploration technology, and in particular to a seismic data processing method, apparatus, equipment, and storage medium. Background Technology

[0002] Traditional seismic data acquisition involves sequential, shot-by-shot firing, which is inefficient. Currently, aliasing acquisition is widely used, which fires multiple shots consecutively within a single shot firing cycle, significantly improving acquisition efficiency. However, in aliasing acquisition, for each geophone, the signals from adjacent shots are superimposed, creating aliasing noise that interferes with each other and affects subsequent seismic data processing. Therefore, it is necessary to separate the aliased signals from adjacent shots.

[0003] The relevant technologies mainly use inversion methods to separate adjacent shot signals. This method extracts common receiver gather data from aliased seismic data, estimates aliasing noise through sparse inversion, and finally removes aliasing noise from the aliased seismic data to obtain the effective signal.

[0004] However, the methods in related technologies have many assumptions in the process of removing aliasing noise, such as linear approximation of aliased seismic data and sparse distribution of data characteristics, which limits the separation effect. Summary of the Invention

[0005] This application provides a seismic data processing method, apparatus, device, and storage medium that can progressively separate aliasing noise from seismic data, thereby improving the separation effect of aliasing noise. The technical solution is as follows:

[0006] On the one hand, a seismic data processing method is provided, the method comprising:

[0007] Acquire the first seismic data, which is the common receiver point gather data of the target work area;

[0008] The first earthquake data is input into the first neural network model in the target neural network model to obtain the first prediction data; wherein, the target neural network model is obtained through N iterations of training, one iteration of training yields one neural network model, and N is an integer greater than 1;

[0009] Based on the first earthquake data and the first prediction data, the second earthquake data is determined, which is the data after separating some of the aliasing noise;

[0010] The second earthquake data is input into the second neural network model in the target neural network model to obtain the second prediction data;

[0011] Based on the first earthquake data and the second prediction data, a third earthquake data is determined, which is the data after further separation of aliasing noise;

[0012] The process of inputting seismic data into the corresponding neural network model in the target neural network model is repeated until the target prediction data is obtained through the Nth neural network model in the target neural network model. The target prediction data is the data after removing aliasing noise.

[0013] In one possible implementation, determining the second earthquake data based on the first earthquake data and the first predicted data includes:

[0014] The first predicted data is subjected to aliasing and pseudo-separation to determine the first noisy data;

[0015] The first noise data is separated from the first seismic data to obtain the second seismic data.

[0016] In another possible implementation, the first seismic data includes seismic data corresponding to multiple seismic traces;

[0017] The step of performing aliasing and pseudo-separation on the first predicted data to determine the first noisy data includes:

[0018] Extract the excitation time corresponding to the seismic data of each seismic trace from the first seismic data;

[0019] Based on the excitation time corresponding to each seismic trace, the seismic data corresponding to the multiple seismic traces are superimposed on the first prediction data to obtain the fourth seismic data.

[0020] Subtracting the first predicted data from the fourth earthquake data yields the first noise data.

[0021] In another possible implementation, the training process of the target neural network model includes:

[0022] Acquire first sample seismic data and first sample label data; wherein, the first sample seismic data is common receiver point gather data, and the first sample label data is data after removing aliasing noise;

[0023] Based on the first sample seismic data and the first sample label data, the first iteration training is performed to obtain the first neural network model;

[0024] Based on the first sample seismic data and the first output data, the second sample seismic data is determined, where the first output data is the output data of the first neural network model.

[0025] Based on the second sample seismic data and the first sample label data, a second iteration of training is performed to obtain the second neural network model;

[0026] The steps of iterative model training are repeated until the Nth iteration is performed, resulting in the Nth neural network model.

[0027] In another possible implementation, the second iteration training based on the second sample seismic data and the first sample label data to obtain the second neural network model includes:

[0028] Based on the second sample seismic data and the first sample label data, a second iteration of training is performed on the first neural network model to obtain the second neural network model.

[0029] In another possible implementation, acquiring the first sample seismic data and the first sample label data includes:

[0030] Acquire the overlay shot collection data in the target work area;

[0031] The first sample seismic data is obtained by extracting common receiver gather data from the shot gather data;

[0032] The aliasing noise in the first sample seismic data is removed by using a sparse inversion method to obtain the first sample label data.

[0033] In another possible implementation, the method further includes:

[0034] Acquire first verification seismic data and first verification label data; wherein, the first verification seismic data is common receiver point gather data, and the first verification label data is data after removing aliasing noise;

[0035] Based on the first verification earthquake data and the first verification label data, the N neural network models included in the target neural network model are verified;

[0036] If the verification is successful, the step of inputting the first seismic data into the first neural network model in the target neural network model is performed.

[0037] On the other hand, a seismic data processing apparatus is provided, the apparatus comprising:

[0038] The first acquisition module is used to acquire the first seismic data, which is the common receiver point gather data of the target work area.

[0039] The first input module is used to input the first earthquake data into the first neural network model in the target neural network model to obtain the first prediction data; wherein, the target neural network model is obtained through N iterations of training, one iteration of training yields one neural network model, and N is an integer greater than 1;

[0040] The first determining module is used to determine the second seismic data based on the first seismic data and the first predicted data, wherein the second seismic data is the data after separating part of the aliasing noise;

[0041] The second input module is used to input the second earthquake data into the second neural network model in the target neural network model to obtain the second prediction data;

[0042] The second determining module is used to determine third earthquake data based on the first earthquake data and the second prediction data, wherein the third earthquake data is data after further separation of aliasing noise; and so on, repeatedly executing the step of inputting earthquake data into the corresponding neural network model in the target neural network model, until the target prediction data is obtained through the Nth neural network model in the target neural network model, wherein the target prediction data is data after removing aliasing noise.

[0043] In one possible implementation, the first determining module is used to perform aliasing and pseudo-separation on the first predicted data to determine first noise data; and to separate the first noise data from the first seismic data to obtain the second seismic data.

[0044] In another possible implementation, the first seismic data includes seismic data corresponding to multiple seismic traces;

[0045] The first determining module is used to extract the excitation time corresponding to the seismic data of each seismic trace from the first seismic data; according to the excitation time corresponding to each seismic trace, superimpose the seismic data corresponding to the multiple seismic traces on the first predicted data to obtain the fourth seismic data; subtract the first predicted data from the fourth seismic data to obtain the first noise data.

[0046] In another possible implementation, the device further includes:

[0047] The second acquisition module is used to acquire first sample seismic data and first sample label data; wherein, the first sample seismic data is common receiver point gather data, and the first sample label data is data after removing aliasing noise;

[0048] The first training module is used to perform the first iteration of training based on the first sample seismic data and the first sample label data to obtain the first neural network model;

[0049] The third determining module is used to determine the second sample seismic data based on the first sample seismic data and the first output data, wherein the first output data is the output data of the first neural network model;

[0050] The second training module is used to perform a second iteration of training based on the second sample seismic data and the first sample label data to obtain the second neural network model; and so on, repeating the model iteration training steps until the Nth iteration training is performed to obtain the Nth neural network model.

[0051] In another possible implementation, the second training module is used to perform a second iteration of training on the first neural network model based on the second sample seismic data and the first sample label data to obtain the second neural network model.

[0052] In another possible implementation, the second acquisition module is used to acquire shot gather data collected under aliasing in the target work area; extract common receiver point gather data from the shot gather data to obtain the first sample seismic data; and remove aliasing noise in the first sample seismic data using a sparse inversion method to obtain the first sample label data.

[0053] In another possible implementation, the device further includes:

[0054] The third acquisition module is used to acquire first verification seismic data and first verification label data; wherein, the first verification seismic data is common receiver point gather data, and the first verification label data is data after removing aliasing noise;

[0055] The verification module is used to verify the N neural network models included in the target neural network model based on the first verification earthquake data and the first verification label data.

[0056] The first input module is used to input the first seismic data into the first neural network model in the target neural network model if the verification is successful.

[0057] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one piece of program code, which is loaded and executed by the processor to implement the seismic data processing method described in any of the preceding claims.

[0058] On the other hand, a computer-readable storage medium is provided, wherein at least one piece of program code is stored in the computer-readable storage medium, the at least one piece of program code being loaded and executed by a processor to implement the seismic data processing method described in any of the preceding claims.

[0059] On the other hand, a computer program product is provided, wherein at least one piece of program code is stored in the computer program product, the at least one piece of program code being loaded and executed by a processor to implement the seismic data processing method described in any of the preceding claims.

[0060] This application provides a seismic data processing method. The method first predicts first seismic data using a first neural network model within a target neural network model, obtaining first predicted data. Based on the first seismic data and the first predicted data, some aliasing noise is separated from the first seismic data to obtain second seismic data. The second seismic data is then predicted using a second neural network model within the target neural network model, obtaining second predicted data. Based on the first seismic data and the second predicted data, aliasing noise is further separated from the first seismic data to obtain third seismic data. This process continues until the Nth neural network model within the target neural network model is used to obtain data after removing aliasing noise. Therefore, this method utilizes the powerful extraction capability of neural network models for nonlinear features, iteratively predicts and gradually separates aliasing noise, ultimately obtaining data after removing aliasing noise. This overcomes many limitations imposed by assumptions, thereby improving the separation effect of aliasing noise.

[0061] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the implementation environment of a seismic data processing method provided in an embodiment of this application;

[0063] Figure 2 This is a flowchart of a seismic data processing method provided in an embodiment of this application;

[0064] Figure 3 This is a schematic diagram of first seismic data before aliasing noise removal, provided in an embodiment of this application.

[0065] Figure 4 This is a schematic diagram of aliasing noise obtained after the first iteration prediction provided in an embodiment of this application;

[0066] Figure 5 This is a schematic diagram of target prediction data after removing aliasing noise, provided in an embodiment of this application.

[0067] Figure 6This is a schematic diagram illustrating the removal of aliasing noise using a target neural network model, as provided in an embodiment of this application.

[0068] Figure 7 This is a flowchart of a training target neural network model provided in an embodiment of this application;

[0069] Figure 8 This is a schematic diagram illustrating iterative training based on sample seismic data and sample label data, provided in an embodiment of this application.

[0070] Figure 9 This is a schematic diagram of the structure of an earthquake data processing device provided in an embodiment of this application;

[0071] Figure 10 This is a structural block diagram of a terminal provided in an embodiment of this application;

[0072] Figure 11 This is a structural block diagram of a server provided in an embodiment of this application. Detailed Implementation

[0073] To make the technical solution and advantages of this application clearer, the embodiments of this application will be described in further detail below.

[0074] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0075] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the earthquake data, prediction data, and aliasing noise involved in this application were all obtained with full authorization.

[0076] Figure 1 This is a schematic diagram illustrating the implementation environment of a seismic data processing method provided in an embodiment of this application. See also... Figure 1The implementation environment includes electronic devices, which can be provided as terminal 101, or as a combination of terminal 101 and server 102, without specific limitations.

[0077] If the electronic device is provided as terminal 101, then terminal 101 processes the seismic data.

[0078] If the electronic device provides a terminal 101 and a server 102, the terminal 101 sends the seismic data to the server 102, the server 102 processes the seismic data, and then returns the processing result to the terminal 101.

[0079] The terminal 101 can be at least one of the following: mobile phone, tablet computer, PC (Personal Computer) device, intelligent voice interaction device, and vehicle terminal. The server 102 can be at least one of the following: a single server, a server cluster consisting of multiple servers, a cloud server, a cloud computing platform, and a virtualization center.

[0080] Figure 2 This is a flowchart of a seismic data processing method provided in an embodiment of this application, executed by an electronic device. See also... Figure 2 The method includes:

[0081] Step 201: The electronic device acquires the first earthquake data.

[0082] The first set of seismic data consists of common receiver gathers for the target seismic area, which is the region where the reservoir to be predicted is located.

[0083] The process of acquiring the first seismic data using electronic equipment can be as follows: The electronic equipment acquires shot gather data from aliased acquisitions in the target seismic area, and extracts multiple common-receiver gather data from the shot gather data. For each common-receiver gather data, that common-receiver gather data is designated as the first seismic data. Each common-receiver gather data, i.e., the first seismic data, includes seismic data corresponding to multiple seismic traces. See also... Figure 3 , Figure 3 A schematic diagram of the first seismic data before removing aliasing noise.

[0084] Step 202: The electronic device inputs the first earthquake data into the first neural network model in the target neural network model to obtain the first prediction data.

[0085] The target neural network model is obtained through N iterations of training. One iteration yields one neural network model, meaning the target neural network model includes N neural network models. The first neural network model is the model obtained through one iteration of training, where N is an integer greater than 1.

[0086] The electronic device inputs the first earthquake data into the first neural network model in the target neural network model, and obtains the first prediction data through the first neural network model.

[0087] Each neural network model in the target neural network model can be a convolutional neural network model U-Net and its variants, ResNet (residual network), a convolutional neural network using an attention mechanism, or other neural network models; no specific limitations are imposed. The training process of the target neural network model will be described in detail later and will not be elaborated here.

[0088] Step 203: The electronic device determines the second earthquake data based on the first earthquake data and the first prediction data.

[0089] The second set of earthquake data consists of data after separating some of the aliasing noise.

[0090] This step can be achieved through the following steps (1) to (2), including:

[0091] (1) The electronic device performs aliasing and pseudo-separation on the first prediction data to determine the first noise data.

[0092] The electronic device extracts the excitation time corresponding to the seismic data of each seismic trace from the first seismic data. Based on the excitation time corresponding to each seismic trace, it superimposes the seismic data corresponding to multiple seismic traces onto the first predicted data to obtain the fourth seismic data. Subtracting the first predicted data from the fourth seismic data yields the first noise data.

[0093] The electronic device sequentially superimposes the seismic data from the first seismic trace, the second seismic trace, the third seismic trace, and so on, based on the excitation time corresponding to each seismic trace in the first seismic data, until the seismic data from the last seismic trace is superimposed, to obtain the fourth seismic data. Then, the first predicted data is subtracted from the fourth seismic data to obtain the first noise data.

[0094] It should be noted that, according to the excitation time corresponding to each seismic trace, the first predicted data is superimposed with the seismic data corresponding to multiple seismic traces to form new data. Since the effective signal of the first predicted data remains unchanged after superposition, only aliasing noise is generated. Therefore, subtracting the first predicted data from the new data can separate the aliasing noise of the first iteration. See [link to relevant documentation] Figure 4 , Figure 4 This is a schematic diagram of the aliasing noise obtained after the first iteration of prediction.

[0095] In this step, the noise data obtained changes accordingly as the aliasing noise level in the predicted data changes. Therefore, each iteration separates adjacent-shot aliasing noise of different intensities, which is a multi-level characterization of adjacent-shot aliasing noise. After N iterations, a multi-level aliasing noise set consisting of N adjacent-shot aliasing noises of different levels is generated. The adjacent-shot multi-level aliasing noise is used to characterize the change in aliasing noise level with the number of iterations during the iterative aliasing separation process. The introduction of adjacent-shot multi-level aliasing noise can improve the accuracy of iterative aliasing separation, especially under conditions of low signal-to-noise ratio and weak signal development, and can significantly improve the quality of aliased data separation.

[0096] (2) The electronic device separates the first noise data from the first seismic data to obtain the second seismic data.

[0097] The electronic device subtracts the first noise data from the first seismic data to obtain the second seismic data with reduced aliasing noise, and uses the second seismic data as the input data for the next iteration prediction.

[0098] Another point to note is that, ideally, since only aliasing noise is subtracted, both effective and weak signals are retained in the second seismic data, and even complex and weak signals will not cause significant damage to the effective signal. However, in reality, the noise in the seismic data itself is superimposed on the aliasing noise of adjacent shots, resulting in insufficient learning by the neural network. Therefore, adjacent shot aliasing noise still exists in the second seismic data, requiring further iterative prediction to improve the separation effect of adjacent shot aliasing noise.

[0099] Step 204: The electronic device inputs the second earthquake data into the second neural network model in the target neural network model to obtain the second prediction data.

[0100] The second neural network model is the model obtained through two iterations of training.

[0101] The electronic device inputs the second earthquake data into the second neural network model, and obtains the second prediction data through the second neural network model.

[0102] Step 205: The electronic device determines the third earthquake data based on the first earthquake data and the second predicted data. This process is repeated sequentially, inputting earthquake data into the corresponding neural network model within the target neural network model, until the target predicted data is obtained through the Nth neural network model.

[0103] The electronic device performs aliasing and pseudo-separation on the second prediction data to determine the second noise data. The second noise data is separated from the first seismic data to obtain the third seismic data, which is the data after further separation of aliasing noise.

[0104] The process of determining the second noise data by performing aliasing and pseudo-separation on the second predicted data using electronic equipment is also based on the excitation time corresponding to each seismic trace. Multiple seismic data corresponding to different seismic traces are superimposed on the second predicted data to obtain the fifth seismic data. The second predicted data is then subtracted from the fifth seismic data to obtain the second noise data. This step is the same as step (1) in step 203, and will not be repeated here. The process of separating the second noise data from the first seismic data using electronic equipment is the same as step (2) in step 203, and will not be repeated here.

[0105] The electronic device inputs the third earthquake data into the third neural network model in the target neural network model, and obtains the third prediction data through the third neural network model, which is obtained through three iterations of training.

[0106] The electronic device performs aliasing and pseudo-separation on the third prediction data to identify the third noise data. The third noise data is separated from the first earthquake data to obtain the sixth earthquake data. The sixth earthquake data is then input into the next neural network model in the target neural network model to obtain the fourth prediction data.

[0107] This process is repeated, inputting seismic data into the corresponding neural network model within the target neural network model, until the target prediction data is obtained through the Nth neural network model. The target prediction data is the data after removing aliasing noise. See also... Figure 5 , Figure 5 This is a schematic diagram of the target prediction data after removing aliasing noise.

[0108] See Figure 6 , Figure 6 This diagram illustrates the process of removing aliasing noise using a target neural network model. The electronic device inputs the first seismic data into the first neural network model for the first iteration of prediction, obtaining the first predicted data. Based on the first predicted data and the first seismic data, the first noisy data is determined, and then the input data for the next iteration of prediction is constructed. It is determined whether the current number of iterations is less than N. If it is less than N, the next iteration of prediction is performed. If it is equal to N, the data obtained from the iterative prediction is output as the result.

[0109] This application combines the powerful nonlinear feature extraction capabilities of deep learning neural networks, the ability to characterize multi-level aliasing noise of varying strengths, and the ability to iteratively denoise and adaptively separate strong and weak noise. Since each iteration of prediction only subtracts aliasing noise from adjacent shots without damaging the effective signal, it can significantly improve the fidelity of the effective signal, especially providing more effective protection for weak signals in seismic data, thus providing high-quality de-aliased data for subsequent data processing. This is mainly reflected in the following aspects:

[0110] (1) Compared with the methods of related technologies, this application utilizes the powerful ability of neural networks to extract nonlinear features, which can effectively extract hyperbolic phase axis features such as seismic reflection and scattering, and overcome the limitations of local linear approximation assumptions. Therefore, the separation effect is better.

[0111] (2) By using iterative training and iterative prediction, adaptive estimation of aliasing noise is performed, and a corresponding neural network model is trained in a targeted manner. When removing aliasing noise from actual data, the neural network model corresponding to the current iteration is used for denoising at different iteration stages, which can better solve the problem of incomplete separation of aliasing noise and damage to effective signals, and retain weak signal information in complex seismic data to the maximum extent.

[0112] After obtaining the target prediction data, the electronic equipment can further process the target prediction data, such as removing random noise and removing multiple waves, and perform migration imaging based on the further processed data, and perform reservoir prediction based on the migration imaging results.

[0113] This application provides a seismic data processing method. The method first predicts first seismic data using a first neural network model within a target neural network model, obtaining first predicted data. Based on the first seismic data and the first predicted data, some aliasing noise is separated from the first seismic data to obtain second seismic data. The second seismic data is then predicted using a second neural network model within the target neural network model, obtaining second predicted data. Based on the first seismic data and the second predicted data, aliasing noise is further separated from the first seismic data to obtain third seismic data. This process continues until the Nth neural network model within the target neural network model is used to obtain data after removing aliasing noise. Therefore, this method utilizes the powerful extraction capability of neural network models for nonlinear features, iteratively predicts and gradually separates aliasing noise, ultimately obtaining data after removing aliasing noise. This overcomes many limitations imposed by assumptions, thereby improving the separation effect of aliasing noise.

[0114] The training process of the target neural network model is described below. The target neural network model can be trained on an electronic device or on another device and then deployed to the electronic device; there is no specific limitation. In this embodiment, only the example of training the target neural network model on an electronic device is used for illustration.

[0115] Figure 7 This is a flowchart illustrating a training target neural network model provided in an embodiment of this application, executed by an electronic device. See also... Figure 7 The method includes:

[0116] Step 701: The electronic device acquires the first sample seismic data and the first sample label data.

[0117] The process of acquiring the first sample seismic data by electronic equipment is similar to the process of acquiring the first seismic data. It involves extracting multiple common receiver point gathers from the shot gather data acquired by the target work area to obtain the first sample seismic data.

[0118] For the first sample label data, the electronic device can use methods from related technologies to remove aliasing noise from the first sample seismic data. For example, it can use sparse inversion to remove aliasing noise from the first sample seismic data to obtain the first sample label data. Of course, the electronic device can also use other methods, such as filtering and denoising, to remove aliasing noise from the first sample seismic data to obtain the first sample label data; there is no specific limitation on this. The number of first sample seismic data and first sample label data can be multiple.

[0119] For example, an electronic device extracts 300 common receiver gathers from aliased shot gather data. Each common receiver gather contains 201 seismic channels. From these 300 common receiver gathers, 80 are randomly selected. From these 80, 80% of the data, i.e., 64 common receiver gathers, are randomly selected as the first sample seismic data. For these 64 common receiver gathers, aliasing noise is removed using a sparse inversion method to obtain 64 sample label data, i.e., the first sample label data.

[0120] Step 702: The electronic device performs the first iteration of training based on the first sample seismic data and the first sample label data to obtain the first neural network model.

[0121] The electronic device inputs the first sample of seismic data and the first sample of label data into the initial neural network model for the first iteration of training. The model parameters of the initial neural network model can be initialized with random values, the epoch (number of rounds) is set, and then iterative training is performed.

[0122] In this embodiment, the neural network model can be represented by R, the model parameters by θ, and the training process of each iteration can be represented by the following formula (1):

[0123]

[0124] Where j = 1, 2, ..., N, N is the maximum number of iterations, and the first sample seismic data is represented as... After the first iteration, the first neural network model can be represented as R1, with its parameters denoted as θ1, and the first output data denoted as...

[0125] The epoch setting can be configured and changed as needed; for example, epoch can be set to 200 without any specific limitation.

[0126] The electronic device obtains the first output data through the first neural network model, determines the difference between the first output data and its corresponding first sample label data, then backpropagates the gradient and updates the neural network parameters, and uses the neural network parameters corresponding to the last epoch as the initial model parameters for the next iteration of training.

[0127] Step 703: The electronic device determines the second sample seismic data based on the first sample seismic data and the first output data.

[0128] The electronic device performs aliasing and pseudo-separation on the first output data to determine the first sample noise data. The first sample noise data is then separated from the first sample seismic data to obtain the second sample seismic data.

[0129] The process by which the electronic device determines the first sample noise data can be as follows: For each common receiver point gather data, the electronic device extracts the excitation time corresponding to the seismic data of each seismic trace from the common receiver point gather data. Based on the excitation time corresponding to each seismic trace, it superimposes the seismic data corresponding to multiple seismic traces on the first output data to obtain the third sample seismic data. The first output data is then subtracted from the third sample seismic data to obtain the first sample noise data. This process is similar to step (1) in step 203, and will not be repeated here.

[0130] For example, if each co-sensor gather contains 201 seismic data, then for 64 co-sensor gathers, there are a total of 64 * 201 = 12864 excitation times. The electronic equipment extracts these 12864 excitation times from the 64 co-sensor gathers.

[0131] Step 704: The electronic device performs a second iteration of training based on the second sample seismic data and the first sample label data to obtain the second neural network model; and so on, repeating the model iteration training steps until the Nth iteration of training is performed to obtain the Nth neural network model.

[0132] The electronic device performs a second iteration of training based on the second sample seismic data and the first sample label data, and obtains the second neural network model.

[0133] The electronic device obtains the second output data through the second neural network model, determines the difference between the second output data and its corresponding first sample label data, then backpropagates the gradient and updates the neural network parameters, using the neural network parameters corresponding to the last epoch as the initial model parameters for the next iteration of training. The electronic device performs aliasing and pseudo-separation on the second output data to identify the second sample noisy data, separating the second sample noisy data from the first sample seismic data to obtain the third sample seismic data. Based on the third sample seismic data and the first sample label data, a third iteration of training is performed on the second neural network model to obtain the third neural network model.

[0134] The electronic device obtains the third output data through the third neural network model, determines the difference between the third output data and its corresponding first sample label data, then backpropagates the gradient and updates the neural network parameters, using the neural network parameters corresponding to the last epoch as the initial model parameters for the next iteration of training. The electronic device performs aliasing and pseudo-separation on the third output data to identify the third sample noise data, separating the third sample noise data from the first sample seismic data to obtain the fourth sample seismic data. Based on the third sample seismic data and the first sample label data, the next iteration of training is performed. This process is repeated until the Nth iteration of training is performed, resulting in the Nth neural network model. The target neural network model includes the first neural network model, the second neural network model, the third neural network model, and so on up to the Nth neural network model.

[0135] See Figure 8 The electronic device performs iterative training based on the first sample of seismic data and the first sample of label data. After each iteration, it determines whether the current iteration count has reached the maximum iteration count, N. If N is reached, the iterative training stops, and the neural network models obtained from each iteration are combined to form the target neural network model. If N is not reached, the next iteration is performed until N is reached, thus obtaining the target neural network model.

[0136] N can be set and changed as needed. For example, if N=3, then 3 iterations of training will be performed. After 3 iterations, 3 neural network models trained for adjacent-shot aliasing noise at different scales will be obtained. In addition, for the first iteration, the model parameters can be initialized with random values. For the second and subsequent iterations, the model parameters can be trained based on the model parameters obtained in the previous iteration.

[0137] In the embodiments of this application, after obtaining the target neural network model, the electronic device can immediately verify the target neural network model. After verification, the target neural network model is then used to predict actual data to remove aliasing noise. Alternatively, the electronic device can also verify the target neural network model while making predictions about the actual data; there is no specific limitation on this.

[0138] The process of verifying the target neural network model by the electronic device can be as follows: the electronic device acquires the first verification earthquake data and the first verification label data, and verifies the N neural network models included in the target neural network model based on the first verification earthquake data and the first verification label data; if the verification is successful, the above step 202 is then executed.

[0139] In this implementation, the electronic device inputs the first verification seismic data into the first neural network model of the target neural network model to obtain the first verification data. Based on the first verification seismic data and the first verification data, the second verification seismic data is determined and input into the second neural network model of the target neural network model to obtain the second verification data. Based on the first verification seismic data and the second verification data, the third verification seismic data is determined and input into the third neural network model of the target neural network model to obtain the third verification data; this process is repeated, step by step, to input the verification seismic data into the corresponding neural network model of the target neural network model until the target verification data is obtained through the Nth neural network model of the target neural network model. The difference between the target verification data and the first verification label data is determined. If the difference is within a preset threshold range, the verification is deemed successful. If the difference is not within the preset threshold range, the verification is deemed unsuccessful.

[0140] If the verification fails, the electronic device can continue to perform iterative training, that is, increase the maximum number of iterations until the verification passes.

[0141] The process of determining the second verification seismic data based on the first verification seismic data and the first verification data is the same as step 203, and will not be repeated here.

[0142] For example, the electronic device uses the remaining 20% ​​of the data from 80 common receiver gathers, namely 16 common receiver gathers, as the first verification seismic data. For these 16 common receiver gathers, the sparse inversion method is used to remove aliasing noise, resulting in 16 verification label data, which are the first verification label data.

[0143] This application provides a training method for a target neural network model. It utilizes the powerful extraction capability of neural network models for nonlinear features and performs adaptive estimation of noise through iterative training. The corresponding neural network model is trained in a targeted manner. During the iterative training phase, considering that different iteration phases contain different levels of adjacent shot aliasing noise, the neural network model corresponding to the current iteration phase is trained. In this way, when removing aliasing noise through the trained neural network model, the problem of incomplete separation of aliasing noise and damage to effective signals can be better solved, and weak signal information in complex seismic data can be preserved to the maximum extent.

[0144] Figure 9 This is a schematic diagram of the structure of a seismic data processing device provided in an embodiment of this application. See also... Figure 9 The device includes:

[0145] The first acquisition module 901 is used to acquire the first seismic data, which is the common receiver point gather data of the target work area.

[0146] The first input module 902 is used to input the first earthquake data into the first neural network model in the target neural network model to obtain the first prediction data; wherein, the target neural network model is obtained through N iterations of training, one iteration of training yields one neural network model, and N is an integer greater than 1;

[0147] The first determining module 903 is used to determine the second earthquake data based on the first earthquake data and the first predicted data. The second earthquake data is the data after separating some of the aliasing noise.

[0148] The second input module 904 is used to input the second earthquake data into the second neural network model in the target neural network model to obtain the second prediction data.

[0149] The second determining module 905 is used to determine the third earthquake data based on the first earthquake data and the second prediction data. The third earthquake data is the data after further separation of aliasing noise. Similarly, the step of inputting earthquake data into the corresponding neural network model in the target neural network model is repeated until the target prediction data is obtained through the Nth neural network model in the target neural network model. The target prediction data is the data after removing aliasing noise.

[0150] In one possible implementation, the first determining module 903 is used to perform aliasing and pseudo-separation on the first predicted data to determine the first noise data; and to separate the first noise data from the first seismic data to obtain the second seismic data.

[0151] In another possible implementation, the first seismic data includes seismic data corresponding to multiple seismic traces;

[0152] The first determining module 903 is used to extract the excitation time corresponding to the seismic data of each seismic trace from the first seismic data; according to the excitation time corresponding to each seismic trace, the seismic data corresponding to multiple seismic traces are superimposed on the first predicted data to obtain the fourth seismic data; the first predicted data is subtracted from the fourth seismic data to obtain the first noise data.

[0153] In another possible implementation, the device also includes:

[0154] The second acquisition module is used to acquire first sample seismic data and first sample label data; wherein, the first sample seismic data is common receiver point gather data, and the first sample label data is data after removing aliasing noise;

[0155] The first training module is used to perform the first iteration of training based on the first sample earthquake data and the first sample label data to obtain the first neural network model;

[0156] The third determining module is used to determine the second sample seismic data based on the first sample seismic data and the first output data, wherein the first output data is the output data of the first neural network model;

[0157] The second training module is used to perform a second iteration of training based on the second sample seismic data and the first sample label data to obtain the second neural network model; and so on, repeating the model iteration training steps until the Nth iteration training is performed to obtain the Nth neural network model.

[0158] In another possible implementation, a second training module is used to perform a second iteration of training based on the second sample seismic data and the first sample label data, to obtain a second neural network model.

[0159] In another possible implementation, the second acquisition module is used to acquire shot gather data acquired in the target work area with aliasing; extract common receiver point gather data from the shot gather data to obtain the first sample seismic data; and use a sparse inversion method to remove aliasing noise from the first sample seismic data to obtain the first sample label data.

[0160] In another possible implementation, the device also includes:

[0161] The third acquisition module is used to acquire the first verification seismic data and the first verification label data; wherein, the first verification seismic data is common receiver point gather data, and the first verification label data is data after removing aliasing noise;

[0162] The verification module is used to verify the N neural network models included in the target neural network model based on the first verification seismic data and the first verification label data.

[0163] The first input module 902 is used to input the first earthquake data into the first neural network model in the target neural network model if the verification is successful.

[0164] This application provides an earthquake data processing apparatus. The apparatus first predicts first earthquake data using a first neural network model within a target neural network model, obtaining first predicted data. Based on the first earthquake data and the first predicted data, it separates some aliasing noise from the first earthquake data to obtain second earthquake data. Then, it predicts the second earthquake data using a second neural network model within the target neural network model, obtaining second predicted data. Based on the first earthquake data and the second predicted data, it further separates aliasing noise from the first earthquake data to obtain third earthquake data. This process continues until the Nth neural network model within the target neural network model is used to obtain data after removing aliasing noise. Therefore, this apparatus utilizes the powerful extraction capability of neural network models for nonlinear features, iteratively predicts and gradually separates aliasing noise, ultimately obtaining data after removing aliasing noise. This overcomes the limitations of many assumptions and conditions, thereby improving the separation effect of aliasing noise.

[0165] refer to Figure 10 , Figure 10 A structural block diagram of a terminal 1000 provided in an exemplary embodiment of this application is shown. The terminal 1000 may be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 1000 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0166] Typically, terminal 1000 includes a processor 1001 and a memory 1002.

[0167] Processor 1001 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1001 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1001 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1001 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 1001 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0168] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1002 are used to store at least one piece of program code, which is executed by the processor 1001 to implement the operations performed by the terminal in the seismic data processing method provided in the method embodiments of this application.

[0169] In some embodiments, the terminal 1000 may also optionally include a peripheral device interface 1003 and at least one peripheral device. The processor 1001, memory 1002, and peripheral device interface 1003 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1003 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 1004, a display screen 1005, a camera assembly 1006, an audio circuit 1007, and a power supply 1008.

[0170] Peripheral device interface 1003 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1001 and memory 1002. In some embodiments, processor 1001, memory 1002 and peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1001, memory 1002 and peripheral device interface 1003 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0171] The radio frequency (RF) circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1004 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1004 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 1004 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1004 can communicate with other terminals via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1004 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0172] Display screen 1005 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1005 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1001 for processing. In this case, display screen 1005 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 1005, disposed on the front panel of terminal 1000; in other embodiments, there may be at least two display screens, disposed on different surfaces of terminal 1000 or in a folded design; in still other embodiments, display screen 1005 may be a flexible display screen, disposed on a curved or folded surface of terminal 1000. Furthermore, display screen 1005 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0173] The camera assembly 1006 is used to acquire images or videos. Optionally, the camera assembly 1006 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1006 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0174] The audio circuit 1007 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1001 for processing, or input to the radio frequency circuit 1004 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the terminal 1000. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1007 may also include a headphone jack.

[0175] The power supply 1008 is used to power the various components in the terminal 1000. The power supply 1008 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When the power supply 1008 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, while a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0176] In some embodiments, the terminal 1000 further includes one or more sensors 1009. The one or more sensors 1009 include, but are not limited to: an acceleration sensor 1010, a gyroscope sensor 1011, a pressure sensor 1012, an optical sensor 1013, and a proximity sensor 1014.

[0177] Accelerometer 1010 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by terminal 1000. For example, accelerometer 1010 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 1001 can control display screen 1005 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1010. Accelerometer 1010 can also be used for games or for acquiring user motion data.

[0178] The gyroscope sensor 1011 can detect the orientation and rotation angle of the terminal 1000. The gyroscope sensor 1011 can work in conjunction with the accelerometer sensor 1010 to collect 3D motion data from the user on the terminal 1000. Based on the data collected by the gyroscope sensor 1011, the processor 1001 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0179] The pressure sensor 1012 can be disposed on the side bezel of the terminal 1000 and / or on the lower layer of the display screen 1005. When the pressure sensor 1012 is disposed on the side bezel of the terminal 1000, it can detect the user's grip signal on the terminal 1000, and the processor 1001 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 1012. When the pressure sensor 1012 is disposed on the lower layer of the display screen 1005, the processor 1001 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 1005. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0180] An optical sensor 1013 is used to collect ambient light intensity. In one embodiment, a processor 1001 can control the display brightness of a display screen 1005 based on the ambient light intensity collected by the optical sensor 1013. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the display screen 1005 is decreased. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of a camera assembly 1006 based on the ambient light intensity collected by the optical sensor 1013.

[0181] The proximity sensor 1014, also known as a distance sensor, is typically installed on the front panel of the terminal 1000. The proximity sensor 1014 is used to detect the distance between the user and the front of the terminal 1000. In one embodiment, when the proximity sensor 1014 detects that the distance between the user and the front of the terminal 1000 is gradually decreasing, the processor 1001 controls the display screen 1005 to switch from a screen-on state to a screen-off state; when the proximity sensor 1014 detects that the distance between the user and the front of the terminal 1000 is gradually increasing, the processor 1001 controls the display screen 1005 to switch from a screen-off state to a screen-on state.

[0182] Those skilled in the art will understand that Figure 10 The structure shown does not constitute a limitation on terminal 1000 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0183] For a server structure diagram, please refer to [link / reference]. Figure 11The server 1100 can vary considerably depending on its configuration or performance. It may include a Central Processing Unit (CPU) 1101 and a memory 1102. The memory 1102 stores at least one line of program code, which is loaded and executed by the processor 1101 to perform the operations performed by the server in the aforementioned seismic data processing method. Of course, the server 1100 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 1100 may also include other components for implementing device functions, which will not be elaborated upon here.

[0184] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one piece of program code that is loaded and executed by a processor to implement the seismic data processing method in the above embodiments.

[0185] In an exemplary embodiment, a computer program product is also provided, which stores at least one piece of program code that is loaded and executed by a processor to implement the seismic data processing method in the above embodiments.

[0186] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0187] The above description is only for the purpose of enabling those skilled in the art to understand the technical solution of this application, and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A seismic data processing method, characterized in that, The method includes: Acquire first seismic data, which is the common receiver point gather data of the target work area, and the first seismic data includes seismic data corresponding to multiple seismic traces; The first earthquake data is input into the first neural network model in the target neural network model to obtain the first prediction data; wherein, the target neural network model is obtained through N iterations of training, one iteration of training yields one neural network model, and N is an integer greater than 1; Extract the excitation time corresponding to the seismic data of each seismic trace from the first seismic data; Based on the excitation time corresponding to each seismic trace, the seismic data corresponding to the multiple seismic traces are superimposed on the first prediction data to obtain the fourth seismic data. Subtracting the first predicted data from the fourth earthquake data yields the first noise data; Separate the first noise data from the first seismic data to obtain the second seismic data, which is the data after separating part of the aliasing noise; The second earthquake data is input into the second neural network model in the target neural network model to obtain the second prediction data; Based on the first earthquake data and the second prediction data, a third earthquake data is determined, which is the data after further separation of aliasing noise; The process of inputting seismic data into the corresponding neural network model in the target neural network model is repeated until the target prediction data is obtained through the Nth neural network model in the target neural network model. The target prediction data is the data after removing aliasing noise.

2. The method according to claim 1, characterized in that, The training process of the target neural network model includes: Acquire first sample seismic data and first sample label data; wherein, the first sample seismic data is common receiver point gather data, and the first sample label data is data after removing aliasing noise; Based on the first sample seismic data and the first sample label data, the first iteration training is performed to obtain the first neural network model; Based on the first sample seismic data and the first output data, the second sample seismic data is determined, where the first output data is the output data of the first neural network model. Based on the second sample seismic data and the first sample label data, a second iteration of training is performed to obtain the second neural network model; The steps of iterative model training are repeated until the Nth iteration is performed, resulting in the Nth neural network model.

3. The method according to claim 2, characterized in that, The second iteration training based on the second sample seismic data and the first sample label data to obtain the second neural network model includes: Based on the second sample seismic data and the first sample label data, a second iteration of training is performed on the first neural network model to obtain the second neural network model.

4. The method according to claim 2, characterized in that, The acquisition of the first sample seismic data and the first sample label data includes: Acquire the overlay shot collection data in the target work area; The first sample seismic data is obtained by extracting common receiver gather data from the shot gather data; The aliasing noise in the first sample seismic data is removed by using a sparse inversion method to obtain the first sample label data.

5. The method according to claim 1, characterized in that, The method further includes: Acquire first verification seismic data and first verification label data; wherein, the first verification seismic data is common receiver point gather data, and the first verification label data is data after removing aliasing noise; Based on the first verification earthquake data and the first verification label data, the N neural network models included in the target neural network model are verified; If the verification is successful, the step of inputting the first seismic data into the first neural network model in the target neural network model is performed.

6. A seismic data processing device, characterized in that, The device includes: The first acquisition module is used to acquire the first seismic data, which is the common receiver point gather data of the target work area, and the first seismic data includes seismic data corresponding to multiple seismic traces. The first input module is used to input the first earthquake data into the first neural network model in the target neural network model to obtain the first prediction data; wherein, the target neural network model is obtained through N iterations of training, one iteration of training yields one neural network model, and N is an integer greater than 1; The first determining module is used to extract the excitation time corresponding to the seismic data of each seismic trace from the first seismic data; according to the excitation time corresponding to each seismic trace, superimpose the seismic data corresponding to the multiple seismic traces on the first predicted data to obtain the fourth seismic data; subtract the first predicted data from the fourth seismic data to obtain the first noise data; separate the first noise data from the first seismic data to obtain the second seismic data, wherein the second seismic data is the data after separating part of the aliasing noise; The second input module is used to input the second earthquake data into the second neural network model in the target neural network model to obtain the second prediction data; The second determining module is used to determine third earthquake data based on the first earthquake data and the second prediction data, wherein the third earthquake data is data after further separation of aliasing noise; and so on, repeatedly executing the step of inputting earthquake data into the corresponding neural network model in the target neural network model, until the target prediction data is obtained through the Nth neural network model in the target neural network model, wherein the target prediction data is data after removing aliasing noise.

7. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one line of program code, which is loaded and executed by the processor to implement the seismic data processing method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to implement the seismic data processing method as described in any one of claims 1 to 5.

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