Method, device and equipment for predicting multiple wave and storage medium

Abstract

The application discloses a multiple wave prediction method and device, equipment and storage medium, and relates to the field of seismic exploration. The method comprises the following steps: acquiring a seismic data set of a target area, wherein the seismic data set comprises at least one seismic trace data; establishing a common square bin according to the target area, the common square bin is divided into at least one square grid, and each grid corresponds to predicting a multiple wave; determining substitute trace data corresponding to a first grid according to the position of the at least one seismic trace data and the position of the first grid; determining input trace data corresponding to the first grid according to the substitute trace data corresponding to each grid, wherein the input trace data comprises the substitute trace data corresponding to at least one grid; and performing convolution on the substitute trace data in the input trace data to obtain the multiple wave corresponding to the first grid. The application improves the prediction accuracy of the multiple wave corresponding to each grid by using the substitute trace data to represent the seismic trace data of the region corresponding to each grid in the target area.

Description

Technical Field

[0001] This application relates to the field of seismic exploration, and in particular to a method, apparatus, device, and storage medium for predicting multiple waves. Background Technology

[0002] In seismic exploration, the interaction between the sea level and the seabed generates multiple waves, which are mixed with the effective waves. These multiple waves reduce the resolution of seismic profiles. If multiple waves are not processed properly, they can easily confuse and interfere with primary waves, seriously affecting the accuracy and reliability of seismic imaging.

[0003] The relevant technology employs Three Dimensional Surface Relative Multiples Elimination (3D-SRME) to perform convolution operations on the gather data within each smallest grid range to obtain the multiples within that range.

[0004] However, the above method requires a corresponding shot point for each receiver point, which leads to a large prediction error for multiple waves. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for predicting multiple waves. The technical solution is as follows:

[0006] According to one aspect of the embodiments of this application, a method for predicting multiple waves is provided, the method comprising:

[0007] Obtain a seismic dataset for the target area, the seismic dataset including at least one seismic trace, the seismic trace referring to the data of seismic waves during the process from the shot point to the return receiver point;

[0008] Based on the target region, a common square element is established, which is divided into at least one square grid, and each grid corresponds to a predicted multiple wave.

[0009] Based on the location of the at least one seismic trace data and the location of the first grid, the alternative trace data corresponding to the first grid is determined, and the alternative trace data is used to indicate the seismic trace data of the area corresponding to the first grid;

[0010] Based on the alternative channel data corresponding to each of the grids, the input channel data corresponding to the first grid is determined, wherein the input channel data contains alternative channel data corresponding to at least one grid.

[0011] The alternative channel data in the input channel data is convolved to obtain the multiple waves corresponding to the first grid.

[0012] According to one aspect of the embodiments of this application, a prediction apparatus for multiple waves is provided, the apparatus comprising:

[0013] The data acquisition module is used to acquire the seismic dataset of the target area. The seismic dataset includes at least one seismic trace data, which refers to the data of seismic waves during the process from the shot point to the return to the receiver point.

[0014] A surface element creation module is used to create a common square surface element based on the target region. The common square surface element is divided into at least one square grid, and each grid corresponds to a predicted multiple wave.

[0015] An alternative trace determination module is used to determine the alternative trace data corresponding to the first grid based on the location of the at least one seismic trace data and the location of the first grid, wherein the alternative trace data is used to indicate the seismic trace data of the area corresponding to the first grid;

[0016] The input channel determination module is used to determine the input channel data corresponding to the first grid based on the alternative channel data corresponding to each grid, wherein the input channel data includes alternative channel data corresponding to at least one grid.

[0017] The multiple wave determination module is used to convolve the substitute channel data in the input channel data to obtain the multiple waves corresponding to the first grid.

[0018] According to one aspect of the embodiments of this application, a computer device is provided, the computer device including a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the above-described multiple wave prediction method.

[0019] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, the computer program being loaded and executed by a processor to implement the above-described method for predicting multiple waves.

[0020] According to one aspect of the embodiments of this application, a computer program product is provided, the computer program product including a computer program, the computer program being loaded and executed by a processor to implement the above-described multiple wave prediction method.

[0021] The technical solution provided in this application can bring the following beneficial effects:

[0022] By constructing the target region as square cells and determining the alternative trace data for each grid within the square cells, the input trace data for the first grid is determined based on the alternative trace data for each grid. This method uses alternative trace data to represent the seismic trace data of each grid in the target region more evenly, avoiding inaccurate multiple wave predictions for each grid due to uneven data distribution. It also reduces the repeated use of alternative trace data in the input trace data for each grid, improving computational efficiency and the prediction accuracy of multiple waves for each grid. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the implementation environment of a solution provided in one embodiment of this application;

[0024] Figure 2 This is a flowchart of a multiple wave prediction method provided in one embodiment of this application;

[0025] Figure 3 This is a schematic diagram of a common square surface element provided in one embodiment of this application;

[0026] Figure 4 This is a schematic diagram of an input data pool provided in one embodiment of this application;

[0027] Figure 5 This is a schematic diagram comparing the number of shot points and the number of detector points provided in one embodiment of this application;

[0028] Figure 6 This is a diagram illustrating the repeated use of seismic trace data provided by related technologies;

[0029] Figure 7 This is a schematic diagram illustrating the selection of a first preset range provided in one embodiment of this application;

[0030] Figure 8 This is a schematic diagram illustrating the correspondence between the number of optimal points and the seismic trace length during the Fourier transform process, provided in one embodiment of this application.

[0031] Figure 9 This is a schematic diagram of a convolution process provided in one embodiment of this application;

[0032] Figure 10 This is a schematic diagram of a multiple wave suppression process provided in one embodiment of this application;

[0033] Figure 11 This is a flowchart of the prediction and suppression process of multiple waves provided in one embodiment of this application;

[0034] Figure 12 This is a block diagram of a multiple wave prediction device provided in one embodiment of this application;

[0035] Figure 13 This is a structural block diagram of a computer device provided in one embodiment of this application. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0037] In seismic exploration, the interaction between the sea level and the seabed generates multiple waves, which are mixed with the effective waves. These multiple waves reduce the resolution of seismic profiles. If multiple waves are not properly processed, they can easily confuse and interfere with primary waves, seriously affecting the accuracy and reliability of seismic imaging, misleading geological interpreters, and reducing the success rate of oil and gas drilling. Therefore, the effective prediction and suppression of multiple waves are crucial for oil and gas exploration.

[0038] Marine seismic exploration data is often affected by two strong reflection interfaces: the sea surface and the seabed, which generate a large number of strong multiples. The strongest multiples, especially those between the sea surface and the seabed, occur because the sea surface's reflection coefficient is almost -1, meaning that approximately 100% of the seismic wave's energy is reflected back below the sea level upon reaching it. The seabed is also a particularly strong reflection layer. These multiple reflections between the sea surface and the seabed create reverberation, severely interfering with subsequent normal reflections. Furthermore, reverberation is not only generated by reflections directly from the excitation point to the seabed; reflections from any subsurface strata, upon reaching the sea surface, are also reflected back to the seabed, continuing to generate new reverberations. These reverberations are often stronger than reflections from typical subsurface strata, severely masking normal reflection signals. These reverberations, caused by the sea surface and seabed, are collectively referred to in the oil and gas exploration industry as surface-relative multiples (SRMs). To effectively suppress SRMs, techniques such as correlation filtering, multichannel wavefield extrapolation, and resonance attenuation can be employed. Meanwhile, when collecting marine seismic data, careful planning of observation lines and selection of appropriate receiver layouts are essential to minimize the impact of multiple waves from the sea.

[0039] Please refer to Figure 1 The diagram illustrates an implementation environment for a solution provided in one embodiment of this application. This implementation environment can be implemented as a multi-wave prediction system. The implementation environment may include: a computer device 110 and a data acquisition system 120.

[0040] Computer device 110 is an electronic device used to process, analyze and interpret earthquake data. The computer device may be a device such as a PC (Personal Computer), tablet computer, smartphone, wearable device, intelligent robot and so on.

[0041] The data acquisition system 120 includes a ground excitation device 121 and a seismic detector 122. The ground excitation device 121 is used to excite seismic waves and transmit them underground. The seismic detector 122 is used to acquire and record the received seismic signals. One or more of the ground excitation device 121 and the seismic detector 122 can be used.

[0042] In this embodiment, the ground-based excitation device 121 and the seismic detector 122 are positioned on the sea surface. Multiple ground-based excitation devices 121 and seismic detectors 122 can be installed within the target area. The ground-based excitation device 121 excites seismic waves to propagate underwater. After multiple reflections from the seabed and sea surface, the seismic waves generate multiples, resulting in the seismic signal received by the seismic detector containing both normal reflections and multiples. The data acquisition system sends the seismic data from the ground-based excitation device and the seismic data from the seismic detector to the computer device 110. The computer device 110 performs multiple prediction on the seismic trace data, obtains the multiples, and then subtracts the predicted multiples from the seismic trace data to obtain the seismic trace data after suppressing the multiples.

[0043] Please refer to Figure 2 This document illustrates a flowchart of a multiple wave prediction method provided in one embodiment of this application. The execution entity for each step of this method can be a computer device. The method may include at least one of the following steps 210 to 250:

[0044] Step 210: Obtain the seismic dataset of the target area. The seismic dataset includes at least one seismic trace data, which refers to the data of seismic waves during the process from the shot point to the return receiver point.

[0045] In this embodiment of the application, the target area refers to any area on the sea level. Multiple shot points and multiple receiver points are set in the target area. Shot points refer to the locations where seismic waves are generated during geological exploration. Receiver points, also known as receiving points, refer to the locations where seismic waves returning from underground are received. Receiver points are the receiving points corresponding to each seismic trace distributed in a certain form on the arrangement of receiving seismic waves.

[0046] Seismic trace data consists of a series of seismic waveform traces. Each seismic trace is a one-dimensional signal trace, specifically referring to a series of data during the process of seismic waves being excited at the shot point and returning to the receiver point.

[0047] For example, seismic trace data includes shot location, receiver location, seismic wave data emitted from the shot, seismic wave data received by the receiver, and so on. Based on the shot location and receiver location, data such as the center point location and shot-receiver distance of the seismic trace can be obtained.

[0048] The seismic trace data in the earthquake dataset is preprocessed seismic trace data, used to improve the signal-to-noise ratio of the seismic trace data, thereby improving the accuracy of multiple wave prediction.

[0049] Step 220: Based on the target region, establish a common square grid. The common square grid is divided into at least one square grid, and each grid corresponds to a predicted multiple wave.

[0050] A Common Square Bin (CSB) is a region consisting of a grid of at least one square, such as... Figure 3 As shown, the target region is divided into several square grids, and each grid corresponds to a region that can predict one wave multiple. For example, the grid scale can be selected as the minimum scale using CMP (Common Middle Point) elements.

[0051] Each grid may contain one seismic trace, multiple seismic traces, or none. The presence of a seismic trace within a grid can mean either that the shot point or receiver point of the seismic trace exists within the corresponding area of ​​the grid, or that the shot point or receiver point of the seismic trace exists on the grid line. If the shot point or receiver point of the seismic trace is located on the grid line, the seismic trace can be assigned to various grid points according to defined rules. For example, seismic traces on the upper and left grid lines can be assigned to that grid according to defined rules.

[0052] Figure 3 In the seismic trace data shown, the shot point and receiver point are located on grid points of different grids. Therefore, this seismic trace can belong to different grids. For example, a seismic trace on a grid point can be assigned to the grid located in the lower right corner. If the receiver point of this seismic trace belongs to grid A and the shot point belongs to grid B, then this seismic trace belongs to both grid A and grid B. The center point is the midpoint of the line connecting the shot point and the receiver point. Figure 3 Each grid point shown can be both a shot point and a receiver point.

[0053] The embodiments of this application are used to predict multiple waves in the region corresponding to each grid. When predicting the multiple waves corresponding to each grid, it is necessary to determine the node where each grid is predicted and the input channel data required for each grid prediction. A node refers to the processing interface in the computer device's operation process. For a grid in a target region, multiple nodes are needed to predict the multiple waves of the grid in the target region.

[0054] Each node is used to predict the multiple waves corresponding to at least one grid cell, and each node corresponds to one output channel. Each output channel is used to output the multiple waves corresponding to at least one grid cell that the corresponding node is to predict. The number of grid cells processed by each node can be the same or different.

[0055] In some embodiments, based on the grid corresponding to the location of at least one seismic trace data, at least one grid to be predicted for each node is determined, and the output trace corresponding to each node is used to output the multiple waves corresponding to at least one grid.

[0056] That is, based on the positions of each grid in the target region, the grid to be predicted by each node is determined, thereby determining which grids corresponding to the multiple waves to be output by each node's output channel. For example, each column of grids in the target region can be assigned to a node for prediction, each row of grids in the target region can be assigned to a node for prediction, multiple consecutive grids in each row or column of the target region can be assigned to a node for prediction, or an n×m grid in the target region can be assigned to a node for prediction, where n and m are positive integers. The n and m mentioned here can be the same or different; this application does not impose any limitations.

[0057] The grid corresponding to the location of the seismic trace data mentioned here can include the grid corresponding to the shot point location in the seismic trace data, as well as the grid corresponding to the receiver location in the seismic trace data.

[0058] Each node corresponds to an input data pool, and each input data pool includes the input channel data corresponding to at least one grid that the node is to predict. For example... Figure 4 As shown, the computer device contains N nodes. The input data pools corresponding to each node constitute the data pool for multiple wave prediction of the target area. For example, the input data pool 1 corresponding to node 1, the input data pool 2 corresponding to node 2, ..., the input data pool N corresponding to node N constitute the data pool for predicting the multiple waves corresponding to the square element.

[0059] In some embodiments, the maximum data volume of the input channel data for each grid to be predicted by the node is determined based on the memory of the input data pool corresponding to the node and the at least one grid to be predicted by the node, wherein the input data pool contains the input channel data of at least one grid to be predicted by the node; and a second preset range is determined based on the maximum data volume.

[0060] The memory of the input data pool corresponding to each node is the same. The memory size of the input data pool corresponding to each node is related to the processing performance of the computer device, and this application does not limit it.

[0061] Based on the memory of the input data pool corresponding to the node and at least one grid to be predicted by the node, the maximum data volume of the input trace data for each grid is determined. The maximum data volume refers to the number of substitute trace data included in the input trace data of each grid. Substitute trace data is used to indicate the seismic trace data for the region corresponding to each grid. Based on the maximum data volume, a second preset range is determined. This second preset range is used to search the input trace data corresponding to each grid, that is, to determine the number of substitute trace data in the input trace data corresponding to each grid. Therefore, the larger the second preset range, the more substitute trace data there are in the input trace data corresponding to each grid; the smaller the second preset range, the fewer substitute trace data there are in the input trace data corresponding to each grid.

[0062] Step 230: Based on the location of at least one seismic trace data and the location of the first grid, determine the alternative trace data corresponding to the first grid. The alternative trace data is used to indicate the seismic trace data of the area corresponding to the first grid.

[0063] Ideally, each receiver point corresponds to a shot point. However, due to limitations imposed by acquisition costs and complex surface conditions during actual seismic data acquisition in the field, seismic data is often sparsely and irregularly sampled in spatial direction. Figure 5 As shown in the planar schematic diagram of the target area, shot points are sparsely arranged, while receiver points are arranged more densely. Therefore, each grid may contain many duplicate seismic traces or no seismic traces at all. It is necessary to filter the seismic trace data and determine the alternative trace data corresponding to each grid from the seismic dataset. The remaining non-alternative trace data will not participate in the subsequent calculation of multiple wave prediction.

[0064] Furthermore, in related technologies, during the process of determining the input trace data corresponding to each grid, there is significant overlap in the input traces for adjacent grids. In the calculation of multiple adjacent grids, a particular trace will be input multiple times. (See reference...) Figure 6 The diagram illustrates the repeated use of different seismic trace data. To reduce redundant input of seismic trace data, alternative trace data is used to indicate the seismic trace data for the corresponding area of ​​each grid.

[0065] The first grid refers to any grid within the common square array. If the first grid contains only one seismic trace, that trace is used as the substitute trace for the first grid. If the first grid contains multiple seismic traces, or if the first grid does not contain any seismic traces, refer to the steps below to determine the substitute trace for the first grid. For explanations regarding grids containing seismic traces, please refer to the above; they will not be repeated here.

[0066] Step 230 includes at least one of the sub-steps in steps 231 to 238 (not shown in the figure).

[0067] Step 231: Based on the first preset range, obtain the location of at least one first seismic trace data within the first preset range of the first grid.

[0068] The first seismic trace data refers to the seismic trace data within a first preset range centered on the first grid. Among at least one first seismic trace data within the first preset range, a substitute trace data corresponding to the first grid is determined. The location of the first seismic trace data refers to the shot point or receiver point location within the seismic trace data within the first preset range of the first grid.

[0069] Optionally, the first preset range can be a circular area, and the first preset range of the first grid can be an area centered on the center point of the first grid, such as... Figure 7 The location of at least one first seismic trace data within the first preset range 701 is obtained, as shown in the first preset range 701.

[0070] Optionally, the first preset range can also be a rectangular area. In this case, the first preset range of the first grid can be an area with the center point of the first grid as the center point of the rectangular area, such as... Figure 7 The first preset range 702 shown is a 5×5 square area, and the location of at least one first seismic trace data within the first preset range 702 is obtained.

[0071] The size of the first preset range can be set according to the actual needs of multiple wave prediction; this application does not impose any limitations.

[0072] Step 232: Based on the location of the first seismic trace data, obtain the shot point location and receiver location in the first seismic trace data.

[0073] If the location of the first seismic trace data is the shot point location in the first seismic trace data, then obtain the receiver location in the first seismic trace data; if the location of the first seismic trace data is the receiver location in the first seismic trace data, then obtain the shot point location in the first seismic trace data.

[0074] Step 233: Based on the shot point and receiver locations in the first seismic trace data, and the location of the first grid, construct an objective function. The objective function is a weighted sum of the differences in distance between the shot point locations of the first grid and the first seismic trace data, the differences in distance between the receiver locations of the first grid and the first seismic trace data, the differences in shot-receiver distances of the first grid and the first seismic trace data, the differences in azimuth angles of the first grid and the first seismic trace data, and the differences in distance between the center point locations of the first grid and the first seismic trace data.

[0075] The position of the first grid refers to any location within the region of the first grid. For example, it could be the center point of the first grid, a location on a grid line, or the position of a specific grid point. It's important to note that when constructing objective functions for different grids, the grid position used must be the same location on the grid. For example, the objective function can be constructed using the center point of the grid, or the position of the top-left grid point.

[0076] Optionally, the position of the first grid is used as the shot point position and receiver position corresponding to the first grid. Then, based on the shot point position in the first seismic trace data and the position of the first grid, the distance difference between the shot point positions of the first grid and the first seismic trace data is determined. Based on the receiver position in the first seismic trace data and the position of the first grid, the distance difference between the receiver positions of the first grid and the first seismic trace data is determined. Since the shot-receiver distance corresponding to the first grid is 0, the shot-receiver distance of the first seismic trace data is determined as the difference between the shot-receiver distances of the first grid and the first seismic trace data. A two-dimensional rectangular coordinate system is constructed based on a common square element. The azimuth angle of the first grid refers to the azimuth angle of the first grid position relative to the origin of the coordinate system, and the azimuth angle of the first seismic trace data refers to the azimuth angle of the center point position of the first seismic trace data relative to the origin of the coordinate system, thus obtaining the difference between the azimuth angles of the first grid and the first seismic trace data. Since the position of the center point corresponding to the first grid is the position of the first grid, the distance difference between the center point positions of the first grid and the first seismic trace data is determined based on the center point position of the first seismic trace data and the position of the first grid.

[0077] Optionally, the position of the first grid is used as the shot point position corresponding to the first grid, and the position of one grid surrounding the first grid is used as the receiver position corresponding to the first grid. For example, the position of the grid immediately to the left of the first grid can be used as the receiver position corresponding to the first grid, or the position of the grid one grid away to the left of the first grid can be used as the receiver position corresponding to the first grid. Based on the shot point position, receiver position, and the shot point position and receiver position corresponding to the first grid in the first seismic trace data, an objective function is constructed.

[0078] Optionally, the position of the first grid is used as the receiver position corresponding to the first grid, and the position of one grid surrounding the first grid is used as the shot position corresponding to the first grid. For example, the position of the grid immediately to the right of the first grid can be used as the shot position corresponding to the first grid, or the position of the grid one grid away to the right of the first grid can be used as the shot position corresponding to the first grid. Based on the shot position and receiver position in the first seismic trace data, as well as the shot position and receiver position corresponding to the first grid, an objective function is constructed.

[0079] For example, the objective function corresponding to the first grid can be expressed as:

[0080] O bj =W s *S d +W r *R d +W o *Offd d +W a *Azi d +W m *Mid d

[0081] Among them, S d R represents the distance difference between the normalized shot point locations of the first grid and the first seismic trace data. d Off represents the distance difference between the normalized receiver locations of the first grid and the first seismic trace data. d Azi represents the difference in normalized shot-receiver offset between the first grid and the first seismic trace data. d Mid represents the difference in normalized azimuth between the first grid and the first seismic trace data. d W represents the distance difference between the normalized center points of the first grid and the first seismic trace data. s W r W o W a W m These are pre-set weighting coefficients.

[0082] Then, based on the value of the objective function corresponding to at least one first seismic trace data, the alternative trace data corresponding to the first grid is determined.

[0083] Step 234: Obtain the first seismic trace data corresponding to the minimum value of the objective function, and use it as the original substitute trace data for the first grid.

[0084] Obtain the objective function value of the first grid relative to at least one first seismic trace data. Use the first seismic trace data corresponding to the minimum objective function value as the original substitute trace data for the first grid. The original substitute trace data refers to the unprocessed first seismic trace data.

[0085] Step 235: Perform shot-receiver distance correction on the original alternative trajectory data to obtain the corrected alternative trajectory data.

[0086] By performing dynamic correction and reaction correction, the original alternative route data is corrected for shot-receiver distance to obtain the corrected alternative route data.

[0087] Step 236: Apply the compensation operator to the corrected alternative track data to obtain the compensated alternative track data.

[0088] For example, the compensated alternative lane data can be represented as:

[0089] f F (t)=e αt ·f(t)

[0090] Where f(t) represents the original substitute channel data, and α represents the compensation operator.

[0091] Step 237: Perform a fast Fourier transform on the compensated substitute channel data to obtain the transformed substitute channel data.

[0092] Step 238: Apply the inverse compensation operator to the transformed substitute trace data to obtain the substitute trace data corresponding to the first grid.

[0093] For example, the alternative track data corresponding to the first grid can be represented as:

[0094] f R (t)=e -αt ·f W (t)

[0095] Among them, f W (t) represents the transformed substitute channel data, and -α represents the inverse compensation operator.

[0096] By applying a compensation operator before the Fast Fourier Transform (FFT) to increase the reflection amplitude of the original substitute trace data, and then applying an inverse compensation operator after the FFT to restore the original amplitude, the problem of small amplitude distortion can be solved, the substitute trace data can be optimized, and the prediction accuracy of multiple waves can be improved.

[0097] Figure 8 The diagram illustrates the correspondence between the number of optimal points and the seismic trace length during the Fourier transform process. It shows that for a given seismic trace length, there is an optimal number of Fourier transform points and a suitable trace length. This optimization avoids the computational pitfalls of the Fourier transform, improving both computational efficiency and accuracy.

[0098] Step 240: Determine the input channel data corresponding to the first grid based on the alternative channel data corresponding to each grid. The input channel data contains at least one alternative channel data corresponding to a grid.

[0099] Step 230 determines a substitute channel data corresponding to each grid, thereby determining the input channel data corresponding to each grid based on the substitute channel data corresponding to each grid and the maximum data volume of the input channel data of each grid.

[0100] In some embodiments, at least one second alternative channel data within the second preset range of the first grid is obtained according to the second preset range; the at least one second alternative channel data is determined as the input channel data corresponding to the first grid.

[0101] The second alternative track data refers to the alternative track data corresponding to the grid within a second preset range centered on the first grid. The second preset range is a region range different from the first preset range.

[0102] Optionally, the second preset range can be a circular region or a rectangular region. If the second preset range is a circular region, the second substitution track data can be substitution track data corresponding to each complete grid within the second preset range, or substitution track data corresponding to the grids encompassed by the second preset range.

[0103] As described above, the second preset range is determined based on the maximum data volume of the input channel data for each grid. Therefore, the number of second alternative channel data within the second preset range is less than or equal to the maximum data volume of the input channel data corresponding to the first grid.

[0104] Step 250: Convolve the substitute channel data in the input channel data to obtain the multiple waves corresponding to the first grid.

[0105] In some embodiments, the input trace data is divided into common shot gathers and common receiver gathers; the alternative trace data in the common shot gathers and the alternative trace data in the common receiver gathers are convolved to obtain at least one convolved data; the at least one convolved data is superimposed to obtain the multiple waves corresponding to the first grid.

[0106] A common shot point gather refers to a gather of seismic data generated by the same shot point but received by different receiver points, while a common receiver gather refers to a gather of seismic data generated by the same receiver point but received by different shot points.

[0107] The input trace data is divided into common shot point gathers and common receiver point gathers. One alternative trace from each of the common shot point gathers and common receiver point gathers is arbitrarily selected, and convolution is performed on both to obtain one convolutional trace. At least one convolutional trace corresponding to each alternative trace from both the common shot point gathers and common receiver point gathers is obtained, and these are superimposed to obtain the multiple waves corresponding to the first grid.

[0108] The convolution process can be referenced. Figure 9As shown, the convolution of two alternative channel data is equivalent to connecting the ray paths of the two alternative channel data together, thereby generating multiple waves. The connection point of the two alternative channel data can also be called the multiple contribution point. The contribution point is the shot point of one channel and the receiver point of the other channel.

[0109] For example, the multiple waves corresponding to the first grid can be represented as:

[0110]

[0111] in, This represents the alternative trace data in the common receiver point set of the input trace data in the i-th grid. M represents the alternative trace data in the common shot point set of the input trace data of the i-th grid. (i) (x r ,y r ,x s ,y s ,ω) represents the multiple waves corresponding to the i-th grid.

[0112] The technical solution provided in this application establishes the target area as a square grid and determines the alternative trace data corresponding to each grid in the square grid. Based on the alternative trace data corresponding to each grid, the input trace data corresponding to the first grid is determined. By using alternative trace data, the originally unevenly distributed seismic trace data is represented relatively uniformly for each grid in the target area. This avoids inaccurate prediction of multiples for each grid due to uneven data distribution, reduces the repeated use of alternative trace data in the input trace data for each grid, improves computational efficiency, and enhances the prediction accuracy of multiples for each grid.

[0113] In some embodiments, after obtaining the multiples corresponding to the first grid, the least squares adaptive subtraction method is used to subtract the multiples corresponding to the first grid from the substitute trace data corresponding to the first grid, so as to obtain the seismic trace data after the suppression multiples of the region corresponding to the first grid.

[0114] The least squares adaptive subtraction method means that the seismic trace data after the suppression multiple waves in the region corresponding to the first grid is equal to the substitute trace data corresponding to the first grid minus λ * the multiple waves corresponding to the first grid, where λ represents the weighting coefficient.

[0115] The process of suppressing multiple waves can be referred to Figure 10 As shown, Figure 10 Figure (a) in the figure represents the alternative track data corresponding to the first grid. Figure 10 Figure (b) in the diagram represents the multiple waves corresponding to the first grid. Figure 10Figure (c) in the diagram represents the seismic trace data after suppressed multiple waves in the region corresponding to the first grid.

[0116] Figure 11 A flowchart illustrating the prediction and suppression process of multiple waves is shown.

[0117] Step S1: Input the earthquake dataset.

[0118] Step S2: Establish a common square grid cell and sort the seismic data using the common square grid cell. Determine the grid position of each seismic trace within the common square grid cell.

[0119] Step S3: By sorting the output channels, the working channels of each node are determined. Each grid is assigned to the corresponding node for multiple wave predictions, thus determining the grid to be predicted by each node.

[0120] Step S4: Allocate memory space based on the memory of the input data pool. Allocate corresponding memory space for each node based on the memory of the input data pool corresponding to each node.

[0121] Step S5: Loop through the output channels of the current node to determine the maximum number of input channels for each working channel. Based on the memory of the input data pool corresponding to the node and the number of grids to be predicted by the node, determine the maximum number of input channels in the input channel data corresponding to each grid.

[0122] Step S6: Establish the objective function and determine the alternative trace data for each grid. Using the grid as the center, search for alternative trace data for each grid within a first preset range. Based on the value of the objective function, determine the seismic trace data for that grid as the alternative trace data for that grid.

[0123] Step S7: If the input data pool is full, stop the output channel loop. Determine the input channel data for a grid within a second preset range, centered on the grid. When the data volume of the input channel data for a grid reaches its maximum, stop adding input channel data to that grid.

[0124] Step S8: Read the alternative trace data into the input data pool and perform shot-receiver distance correction. Add the input trace data of each grid to the input data pool of the corresponding node, and filter the repeatedly selected alternative trace data to obtain the input data pool of the node.

[0125] Step S9: Perform data correction using compensation and inverse compensation techniques. Compensation and inverse compensation techniques are applied to each substitute channel data in the input data pool to correct the small amplitude distortion in the substitute channel data and improve the prediction accuracy of multiple waves.

[0126] Step S10: Convolve the two data points in the input trace data of the grid. Divide the input trace data into common shot point gathers and common receiver point gathers, and convolve the alternative trace data in the common shot point gathers and the alternative trace data in the common receiver point gathers to obtain at least one convoluted data point.

[0127] Step S11: Overlay at least one convolutional data to obtain multiple waves of the grid.

[0128] Step S12: The predicted multiple waves are subtracted from the original data using the least squares adaptive subtraction method to obtain the seismic trace data after suppressing the multiple waves.

[0129] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0130] Please refer to Figure 12 This diagram illustrates a block diagram of a multiple prediction apparatus according to an embodiment of this application. The apparatus has the function of implementing the aforementioned multiple prediction method; this function can be implemented in hardware or by hardware executing corresponding software. The apparatus can be the computer device described above, or it can be installed within a computer device. For example... Figure 12 As shown, the device 1200 may include: a data acquisition module 1210, a surface element creation module 1220, a substitution channel determination module 1230, an input channel determination module 1240, and a multiple wave determination module 1250.

[0131] The data acquisition module 1210 is used to acquire a seismic dataset of a target area. The seismic dataset includes at least one seismic trace data, which refers to the data of seismic waves during the process from the shot point to the return receiving point.

[0132] The surface element creation module 1220 is used to create a common square surface element based on the target region. The common square surface element is divided into at least one square grid, and each grid corresponds to a predicted multiple wave.

[0133] The alternative trace determination module 1230 is used to determine the alternative trace data corresponding to the first grid based on the location of the at least one seismic trace data and the location of the first grid. The alternative trace data is used to indicate the seismic trace data of the area corresponding to the first grid.

[0134] The input channel determination module 1240 is used to determine the input channel data corresponding to the first grid based on the alternative channel data corresponding to each grid, wherein the input channel data includes at least one alternative channel data corresponding to a grid.

[0135] The multiple wave determination module 1250 is used to convolve the substitute channel data in the input channel data to obtain the multiple waves corresponding to the first grid.

[0136] In some embodiments, the alternative route determination module 1230 is configured to:

[0137] According to the first preset range, obtain the location of at least one first seismic trace data within the first preset range of the first grid;

[0138] Based on the location of the first seismic trace data, obtain the shot point location and receiver location in the first seismic trace data;

[0139] Based on the shot point and receiver locations in the first seismic trace data, and the location of the first grid, an objective function is constructed. The objective function is a function obtained by weighted summation of the distance differences between the shot point locations of the first grid and the first seismic trace data, the distance differences between the receiver locations of the first grid and the first seismic trace data, the difference between the shot-receiver distances of the first grid and the first seismic trace data, the difference between the azimuth angles of the first grid and the first seismic trace data, and the distance differences between the center point locations of the first grid and the first seismic trace data.

[0140] Based on the value of the objective function corresponding to the at least one first seismic trace data, the alternative trace data corresponding to the first grid is determined.

[0141] In some embodiments, the alternative route determination module 1230 is configured to:

[0142] The first seismic trace data corresponding to the minimum value of the objective function is obtained and used as the original substitute trace data corresponding to the first grid.

[0143] The original alternative trajectory data is corrected for shot-receiver distance to obtain the corrected alternative trajectory data;

[0144] The compensation operator is applied to the corrected alternative track data to obtain the compensated alternative track data;

[0145] Perform a Fast Fourier Transform on the compensated substitute track data to obtain the transformed substitute track data;

[0146] The inverse compensation operator is applied to the transformed alternative track data to obtain the alternative track data corresponding to the first grid.

[0147] In some embodiments, the input channel determination module 1240 is configured to:

[0148] According to the second preset range, at least one second alternative channel data within the second preset range of the first grid is obtained;

[0149] The at least one second alternative channel data is determined as the input channel data corresponding to the first grid.

[0150] In some embodiments, the multiple wave determination module 1250 is configured to:

[0151] The input channel data is divided into common shot point gathers and common receiver point gathers;

[0152] The alternative trace data in the common shot point trace set and the alternative trace data in the common receiver point trace set are convolved to obtain at least one convolved data.

[0153] The at least one convolutional data is superimposed to obtain the multiple waves corresponding to the first grid.

[0154] In some embodiments, the device 1200 further includes a pressing module, the pressing module being used for:

[0155] The least squares adaptive subtraction method is used to subtract the multiple wave corresponding to the first grid from the substitute trace data corresponding to the first grid, so as to obtain the seismic trace data after the suppression multiple wave in the region corresponding to the first grid.

[0156] In some embodiments, the apparatus 1200 further includes a node allocation module, the node allocation module being used for:

[0157] Based on the grid corresponding to the location of the at least one seismic trace data, determine at least one grid to be predicted for each node, and the output trace of each node is used to output the multiple waves corresponding to at least one grid;

[0158] Based on the memory of the input data pool corresponding to the node and at least one grid that the node wants to predict, determine the maximum data volume of the input channel data for each grid that the node wants to predict. The input data pool contains the input channel data of at least one grid that the node wants to predict.

[0159] A second preset range is determined based on the maximum data volume.

[0160] The technical solution provided in this application establishes the target area as a square grid and determines the alternative trace data corresponding to each grid in the square grid. Based on the alternative trace data corresponding to each grid, the input trace data corresponding to the first grid is determined. By using alternative trace data, the originally unevenly distributed seismic trace data is represented relatively uniformly for each grid in the target area. This avoids inaccurate prediction of multiples for each grid due to uneven data distribution, reduces the repeated use of alternative trace data in the input trace data for each grid, improves computational efficiency, and enhances the prediction accuracy of multiples for each grid.

[0161] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the content structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0162] Please refer to Figure 13 The diagram illustrates a structural block diagram of a computer device 1300 provided in one embodiment of this application. The computer device 1300 can be any electronic device capable of data calculation, processing, and storage. The computer device 1300 can be used to implement the multiple wave prediction method provided in the above embodiments.

[0163] Typically, computer device 1300 includes a processor 1301 and a memory 1302.

[0164] Processor 1301 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1301 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 1301 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 1301 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 1301 may also include an AI processor for handling computational operations related to machine learning.

[0165] The memory 1302 may include one or more computer-readable storage media, which may be non-transitory. The memory 1302 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 1302 are used to store a computer program configured to be executed by one or more processors to implement the aforementioned multiple wave prediction method.

[0166] Those skilled in the art will understand that Figure 13 The structure shown does not constitute a limitation on the computer device 1300, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0167] In an illustrative embodiment, a computer-readable storage medium is also provided, wherein a computer program is stored therein, which, when executed by a processor of a computer device, implements the aforementioned multiple wave prediction method. Optionally, the aforementioned computer-readable storage medium may be ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device, etc.

[0168] In an exemplary embodiment, a computer program product is also provided, comprising a computer program stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium and executes the computer program, causing the computer device to perform the aforementioned multiple-wave prediction method.

[0169] It should be noted that this application may display prompt interfaces, pop-ups, or output voice prompts before and during the collection of user data. These prompt interfaces, pop-ups, or voice prompts are used to inform users that their data is being collected. This ensures that the application only begins the steps for collecting user data after receiving confirmation from the user regarding the prompt interface or pop-up; otherwise (i.e., without receiving confirmation from the user), the steps for collecting user data end, meaning no user data is collected. In other words, all user data collected by this application is processed strictly in accordance with the requirements of relevant national laws and regulations. The informed consent or separate consent of the data subject is obtained only with the user's consent and authorization. Subsequent data use and processing are conducted within the scope of laws, regulations, and the data subject's authorization, and the collection, use, and processing of relevant user data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0170] It should be understood that "multiple" as used herein refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. Furthermore, the step numbers described herein are merely illustrative of one possible execution order. In some other embodiments, the steps may not be executed in numerical order, such as two steps with different numbers being executed simultaneously, or two steps with different numbers being executed in the reverse order of the illustration. This application does not limit this.

[0171] The above description is merely an exemplary embodiment 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 method for predicting multiple waves, characterized in that, The method includes: Obtain a seismic dataset for the target area, the seismic dataset including at least one seismic trace, the seismic trace referring to the data of seismic waves during the process from the shot point to the return receiver point; Based on the target region, a common square element is established, which is divided into at least one square grid, and each grid corresponds to a predicted multiple wave. If the first grid contains only one seismic trace, the single seismic trace will be used as the substitute trace for the first grid. The first grid refers to any one of the square facets. If the first grid contains multiple seismic traces, or if the first grid does not contain seismic traces, the location of at least one first seismic trace within the first preset range of the first grid is obtained according to the first preset range. Based on the location of the first seismic trace data, obtain the shot point location and receiver location in the first seismic trace data; Based on the shot point and receiver locations in the first seismic trace data, and the location of the first grid, an objective function is constructed. The objective function is a function obtained by weighted summation of the distance differences between the shot point locations of the first grid and the first seismic trace data, the distance differences between the receiver locations of the first grid and the first seismic trace data, the difference between the shot-receiver distances of the first grid and the first seismic trace data, the difference between the azimuth angles of the first grid and the first seismic trace data, and the distance differences between the center point locations of the first grid and the first seismic trace data. The first seismic trace data corresponding to the minimum value of the objective function is obtained and used as the original substitute trace data corresponding to the first grid. The original alternative trajectory data is corrected by shot-receiver distance correction to obtain corrected alternative trajectory data; The compensation operator is applied to the corrected alternative track data to obtain the compensated alternative track data; Perform a Fast Fourier Transform on the compensated substitute track data to obtain the transformed substitute track data; Applying the inverse compensation operator to the transformed alternative track data yields the alternative track data corresponding to the first grid. Based on the alternative channel data corresponding to each of the grids, the input channel data corresponding to the first grid is determined, wherein the input channel data contains alternative channel data corresponding to at least one grid. The alternative channel data in the input channel data is convolved to obtain the multiple waves corresponding to the first grid.

2. The method according to claim 1, characterized in that, The step of determining the input channel data corresponding to the first grid based on the alternative channel data corresponding to each grid includes: According to the second preset range, at least one second alternative channel data within the second preset range of the first grid is obtained; The at least one second alternative channel data is determined as the input channel data corresponding to the first grid.

3. The method according to claim 1, characterized in that, The step of convolving the substitute channel data in the input channel data to obtain the multiple waves corresponding to the first grid includes: The input channel data is divided into common shot point gathers and common receiver point gathers; The alternative trace data in the common shot point trace set and the alternative trace data in the common receiver point trace set are convolved to obtain at least one convolved data; The at least one convolutional data is superimposed to obtain the multiple waves corresponding to the first grid.

4. The method according to claim 1, characterized in that, After convolving the substitute channel data in the input channel data to obtain the multiple waves corresponding to the first grid, the method further includes: The least squares adaptive subtraction method is used to subtract the multiple wave corresponding to the first grid from the substitute trace data corresponding to the first grid, so as to obtain the seismic trace data after the suppression multiple wave in the region corresponding to the first grid.

5. The method according to claim 1, characterized in that, After establishing the common square element based on the target region, the process further includes: Based on the grid corresponding to the location of the at least one seismic trace data, determine at least one grid to be predicted for each node, and the output trace of each node is used to output the multiple waves corresponding to at least one grid; Based on the memory of the input data pool corresponding to the node and at least one grid that the node wants to predict, determine the maximum data volume of the input channel data for each grid that the node wants to predict. The input data pool contains the input channel data of at least one grid that the node wants to predict. A second preset range is determined based on the maximum data volume.

6. A device for predicting multiple waves, characterized in that, The device includes: The data acquisition module is used to acquire the seismic dataset of the target area. The seismic dataset includes at least one seismic trace data, which refers to the data of seismic waves during the process from the shot point to the return to the receiver point. A surface element creation module is used to create a common square surface element based on the target region. The common square surface element is divided into at least one square grid, and each grid corresponds to a predicted multiple wave. The input trace determination module is used to, when only one seismic trace is contained within the first grid, use that single seismic trace as the substitute trace data for the first grid, where the first grid refers to any one of the square facets; when multiple seismic traces are contained within the first grid, or when no seismic traces are contained within the first grid, obtain the position of at least one first seismic trace within the first preset range of the first grid according to the first preset range; obtain the shot point position and receiver position in the first seismic trace based on the position of the first seismic trace; and construct an objective function based on the shot point position and receiver position in the first seismic trace, and the position of the first grid, wherein the objective function is the distance difference between the shot point position and the first seismic trace position of the first grid, the distance difference between the receiver position and the first seismic trace position of the first grid, and the distance difference between the first grid and the first seismic trace position of the first grid. The objective function is obtained by weighted summing the differences in shot-receiver offset between the first seismic trace data and the first grid, the differences in azimuth between the first grid and the first seismic trace data, and the differences in the distance between the center points of the first grid and the first seismic trace data. The first seismic trace data corresponding to the minimum value of the objective function is obtained as the original substitute trace data for the first grid. Shot-receiver offset correction is performed on the original substitute trace data to obtain corrected substitute trace data. A compensation operator is applied to the corrected substitute trace data to obtain compensated substitute trace data. A fast Fourier transform is performed on the compensated substitute trace data to obtain transformed substitute trace data. An inverse compensation operator is applied to the transformed substitute trace data to obtain the substitute trace data corresponding to the first grid. Based on the substitute trace data corresponding to each grid, the input trace data corresponding to the first grid is determined, and the input trace data contains at least one substitute trace data corresponding to a grid. The multiple wave determination module is used to convolve the substitute channel data in the input channel data to obtain the multiple waves corresponding to the first grid.

7. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program that is loaded and executed by the processor to implement the multiple wave prediction 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 a computer program, which is loaded and executed by a processor to implement the multiple wave prediction method as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes a computer program that is loaded and executed by a processor to implement the multiple wave prediction method as described in any one of claims 1 to 5.

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