Splicing line seamless processing method and device during post-stack seismic data volume splicing

Abstract

The invention provides a splicing line seamless processing method and device during splicing of post-stack seismic data volumes, and relates to the technical field of oil exploration earthquakes, windowing is carried out near splicing lines of the post-stack spliced data volumes, data in a window range forms data subsets, a multivariate spline fitting smoothing method is introduced to process the data subsets, and the splicing line seamless processing method has the advantages that the splicing line seamless processing efficiency is improved; according to the method, a new data subset is formed, wave mixing is carried out on a new data volume and an original data volume according to the requirement by taking the distance between a target line and a splicing line position as a weighting factor, a processing effect of eliminating a splicing line trace when a plurality of blocks of data are spliced to form a whole splicing result is finally obtained, and high fidelity of data outside a window range is ensured. And the data in the window also has high fidelity, and plays an important role in subsequently forming a basin-level (region or zone-level) result data body formed by splicing multiple blocks.

Description

Technical Field

[0001] This application relates to the field of seismic technology in petroleum exploration, and in particular to a method and apparatus for seamless splicing of splicing lines during post-stack seismic data volume splicing. Background Technology

[0002] With the development of seismic exploration, exploration targets are becoming increasingly in-depth and detailed. Clarifying the overall patterns of reservoir development, fracture development characteristics, and source rock development is becoming increasingly important for a comprehensive understanding of the entire area. Currently, when analyzing seismic data, in addition to pre-stack contiguous processing, multiple data volumes within a basin (zone or region) can be stitched together to form a unified, contiguous data volume. This eliminates closure errors and boundary issues inherent in segmented processing, allowing for analysis using the entire data volume. While this method achieves slightly lower consistency than contiguous processing, it offers greater advantages in terms of cost savings and effectiveness.

[0003] Stitching together seismic data for analysis can serve basin (zone or region) seismic interpretation and regional geological research, improving the accuracy and efficiency of seismic interpretation. However, directly stitching together multiple data volumes results in a single data volume with closure errors. Even after closure error correction, stitching marks remain. Data volumes without these stitching marks cause significant inconvenience for subsequent seismic attribute analysis and structural horizon interpretation, further leading to judgment errors caused by stitching marks in attribute and structural interpretation, and reducing interpretation accuracy and analysis efficiency.

[0004] Based on this, this application proposes a method for seamlessly processing splicing marks when splicing multiple post-stack seismic data. Summary of the Invention

[0005] This application provides a method and apparatus for seamless stitching of stitched seismic data volumes to solve the above-mentioned problems.

[0006] In a first aspect of this application, a method for seamless stitching of stitched seismic data volumes is proposed, the method comprising: Acquire multi-block seismic data, and based on the coordinate overlap area of ​​the multi-block seismic data, select a splicing line. After performing closure error correction on the seismic data to be corrected with the splicing line as the boundary, form a whole spliced ​​data volume with the splicing line as the splicing boundary. For the entire spliced ​​data body, with the splicing line as the center, a first time window and a second time window are selected in the first and second directions around the splicing line, and the data in the first and second time windows form a subset of the original data body; Multivariate spline fitting is performed on the original data volume subset to obtain a new data volume subset; The new data subset is mixed with the data in the first and second time windows using inverse distance weighting coefficients to obtain new data in the first and second time windows; The data in the first and second time windows are replaced with new data from the first and second time windows, and then merged with the original data of the entire spliced ​​data volume to form the entire spliced ​​result data.

[0007] Optionally, multivariate spline fitting is performed on the original data volume subset to obtain a new data volume subset, including: A loss function is formed based on the fitting term and the deformation term. The fitting term is used to measure the difference between the source point and the target point after deformation, and the deformation term is used to measure the magnitude of deformation. To achieve a smooth fit in the original data subset, for any given source point in the original data subset, the value of the smoothing function is calculated as the target point. All target points obtained by applying the smoothing function to all source points are used as a new subset of the data volume.

[0008] Optionally, the formula for calculating the loss function is as follows: , in, , , In the formula, E represents the loss function. Represents the fitted term, Represents the deformation term. Indicates the weighting coefficient. and Let i represent the i-th source point in the original data subset and the i-th target point in the new data subset, respectively. , , Source point in the original data volume subset coordinates, Represents a smoothing function. This refers to the range of discrete points used to form the smooth function.

[0009] Alternatively, the formula for calculating the smoothing function is as follows: , In the formula, Source point in the original data volume subset , It is any real number. This indicates the weighting of different radial bases, i.e., the degree to which deformation is allowed to occur. It is a radial basis function, and the calculation formula is: , , Indicates the number of control points. Source point in the original data volume subset The corresponding control points.

[0010] Optionally, the smoothing function satisfies the following constraints: , In the formula, Source point in the original data volume subset The corresponding control points.

[0011] Optionally, the calculation formula used for the mixing processing of the new data volume subset with the data in the first and second time windows according to the inverse distance weighting coefficient is as follows: , In the formula, This represents the amplitude value at the target point after wave mixing. The amplitude value at the source point. The values ​​are the source points after multivariate spline fitting. These are the inverse distance weighted coefficients.

[0012] A second aspect of this application provides a device for seamless stitching of stitched seismic data volumes, the device comprising: The acquisition and correction module is used to acquire multi-block seismic data, and select a splicing line based on the coordinate overlap area of ​​the multi-block seismic data. After performing closure error correction processing on the seismic data to be corrected with the splicing line as the boundary, the entire spliced ​​data body is formed with the splicing line as the splicing boundary. The original data body subset acquisition module is used to select a first time window and a second time window around the splicing line in a first direction and a second direction, with the splicing line as the center, and to form an original data body subset with the data in the first time window and the second time window; The fitting module is used to perform multivariate spline fitting on the original data volume subset to obtain a new data volume subset. The mixing module is used to perform mixing processing on the new data volume subset and the data in the first time window and the second time window according to the inverse distance weighting coefficient to obtain the new data in the first time window and the second time window; The splicing module is used to replace the data in the first and second time windows with the data in the new first and second time windows, and merge it with the original data of the whole splicing data body to form the whole splicing result data.

[0013] Optionally, the fitting module includes: A submodule is formed to generate a loss function based on a fitting term and a deformation term. The fitting term is used to measure the difference between the source point and the target point after deformation, and the deformation term is used to measure the magnitude of deformation. The first calculation submodule is used to calculate the value of the smoothing function for any given source point in the original data volume subset as the target point in order to achieve fitting smoothness for the original data volume subset. The second calculation submodule is used to take all target points obtained by calculating all source points through the smoothing function as a new subset of data.

[0014] In a third aspect of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the seamless stitching method for stitching post-stack seismic data volumes as described in any of the first aspects above.

[0015] In a fourth aspect of this application, a computer-readable storage medium is provided, on which a computer program / instruction is stored, which, when executed by a processor, implements the seamless stitching method for stitching post-stack seismic data volumes as described in any of the first aspects above.

[0016] This application has the following advantages: It proposes a method and apparatus for seamless stitching of stitching lines during post-stack seismic data volume stitching. By acquiring seismic data from multiple blocks, and selecting a stitching line based on the coordinate overlap area of ​​the multiple seismic blocks, the seismic data to be corrected is processed for closure error correction using the stitching line as the boundary. A complete stitched data volume is then formed using the stitching line as the stitching boundary. A first time window and a second time window are selected around the stitching line in a first and a second direction, respectively, forming an original data volume subset. Multivariate spline fitting is performed on the original data volume subset to obtain a new data volume subset. The new data volume subset is then mixed with the data in the first and second time windows using inverse distance weighting coefficients to obtain new data in the first and second time windows. The new data in the first and second time windows replaces the data in the first and second time windows, and is then merged with the original data of the complete stitched data volume to form the complete stitched result data.

[0017] This application creates a window near the splicing line of the post-stitched data volume. The data within the window range forms a data subset. A multivariate spline fitting and smoothing method is used to process the data subset to form a new data subset. As needed, the new data volume and the original data volume are mixed using the distance between the target line and the splicing line position as a weighting factor. Finally, the processing effect of eliminating splicing line traces when splicing multiple data blocks into a whole spliced ​​result is obtained. It also ensures high fidelity of the data outside the window range and high fidelity of the data inside the window range. This plays an important role in the subsequent formation of basin-level (regional or zone-level) result data volumes spliced ​​from multiple blocks. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the steps of a method for seamless stitching of post-stack seismic data volumes proposed in an embodiment of this application. Figure 2 This is a schematic diagram of the location of a work area and seismic data in the same region, as proposed in an embodiment of this application; Figure 3 This is a schematic diagram of the stacked data display of the overlapping spatial locations of work area A and work area B, as proposed in an embodiment of this application. Figure 4 This is a schematic diagram of stitched seismic data from work areas A and B, as proposed in an embodiment of this application. Figure 5 This is a schematic diagram of seismic data from work areas A and B before closure error correction and seamless processing, as proposed in an embodiment of this application. Figure 6 This is a schematic diagram of seismic data from work areas A and B after closure error correction and seamless processing, as proposed in an embodiment of this application. Figure 7 This is a schematic diagram of a subset of the original data body proposed in an embodiment of this application; Figure 8 This is a schematic diagram showing a new data volume subset after fitting a subset of the original data volume and multivariate splines, as proposed in an embodiment of this application. Figure 9 This is a schematic diagram showing an original data volume subset, a data subset before mixing, and a new data volume subset after mixing, as proposed in an embodiment of this application. Figure 10This is a schematic diagram showing the result of merging the data after the mixing process with the original data of the whole stitched data volume, as proposed in an embodiment of this application. Figure 11 This is a schematic diagram showing partial details of the whole piece of spliced ​​data before and after seamless processing, according to an embodiment of this application. Figure 12 This is a schematic diagram of isochronous slices of the whole-block spliced ​​result data before and after seamless processing, as proposed in an embodiment of this application. Figure 13 This is a planar diagram showing the residual closure error of the whole-piece splicing result data before and after seamless processing, as proposed in an embodiment of this application. Figure 14 This is an architectural diagram of a device for seamless stitching of post-stack seismic data volumes, as proposed in an embodiment of this application. Figure 15 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] With the development of seismic exploration and the continuous increase in exploration intensity, exploration targets are becoming increasingly in-depth and detailed. Clarifying the overall patterns of reservoir development, fracture development characteristics, and source rock development is becoming increasingly important for a comprehensive understanding of the entire area. Basin (zone or region) level interpretation research has become both popular and essential. Various oilfields and oil and gas companies have accumulated rich 3D seismic data from different periods, using different observation systems, with different acquisition parameters, and varying in quality. By stitching together multiple data volumes within a basin (zone or region) using certain methods, a unified, contiguous data volume can be formed, eliminating boundary issues in segmented processing. This serves basin (zone or region) seismic interpretation research and regional geological studies, improving the accuracy and efficiency of seismic interpretation.

[0022] Current proposed methods for stitching multi-block seismic data do not consider eliminating stitching line marks when stitching multiple data blocks. Data blocks without stitching line marks cause great inconvenience to subsequent seismic interpretation attribute analysis and tectonic horizon interpretation, further leading to judgment errors caused by stitching line marks in attribute and tectonic interpretation, and reducing interpretation accuracy and analysis efficiency.

[0023] Based on this, this application proposes a method for seamlessly processing splicing marks when stitching multiple post-stack seismic data blocks. This method can be used to eliminate splicing marks when stitching multiple post-stack seismic data blocks. In this application, eliminating splicing marks means that there are no splicing marks on InLine, XLine, isochronous slices, and slices along layer attributes, and it is applicable to the use of stitching multiple post-stack data blocks.

[0024] In a first aspect of this application, a method for seamless stitching of stitched seismic data volumes is provided, as described in the following reference. Figure 1 , Figure 1 This is a flowchart illustrating the steps of a method for seamless stitching of post-stack seismic data volumes proposed in this application. The method includes the following steps: Step 101: Acquire multi-block seismic data, and based on the coordinate overlap area where the multi-block seismic data is located, select a splicing line, and perform closure error correction on the seismic data to be corrected using the splicing line as the boundary, and then form a whole spliced ​​data volume using the splicing line as the splicing boundary; Step 102: Taking the splicing line as the center, select a first time window and a second time window in the first and second directions around the splicing line of the entire spliced ​​data body, and form a subset of the original data body with the data in the first and second time windows; Step 103: Perform multivariate spline fitting on the original data volume subset to obtain a new data volume subset; Step 104: Perform a mixing process on the new data volume subset and the data in the first and second time windows using inverse distance weighting coefficients to obtain new data in the first and second time windows; Step 105: Replace the data in the first and second time windows with the data in the new first and second time windows, and merge them with the original data of the whole spliced ​​data body to form the whole spliced ​​result data.

[0025] In specific implementation step 101, before performing seismic data stitching, multi-block seismic data of a basin, region, or zone are first acquired as the seismic data to be stitched. Since seismic data can only be effectively stitched together in overlapping areas, a stitching line is selected based on the coordinate overlap area of ​​the acquired multi-block seismic data to perform closure error correction. After correction, the corrected seismic data is then stitched together using the stitching line to form a complete data block.

[0026] For example, see Figure 2 , Figure 2 This is a schematic diagram of the location of a work area and seismic data within the same region, as proposed in an embodiment of this application. Figure 2As can be seen, the overlapping area of ​​coordinates between work areas A and B can be determined from their coordinate positions. Based on this overlapping area, the seismic data from work areas A and B are stitched together. The seismic data from work areas A and B are then arranged according to... Figure 2 After the coordinates overlap in the overlapping areas, the displayed post-stack seismic data is as follows: Figure 3 As shown ( Figure 3 This is a schematic diagram of the overlay data display of the overlapping spatial locations of work areas A and B, as proposed in an embodiment of this application. Figure 3 The left side of the box displayed shows seismic data for standard work area A, the right side shows seismic data for standard work area B, and the area inside the box shows post-stack data for the spatially overlapping portion of work areas A and B. Based on... Figure 3 The displayed stitching line stitches the seismic data from work areas A and B. The stitched seismic data from work areas A and B are shown below. Figure 4 As shown ( Figure 4 This is a schematic diagram of stitched seismic data from work areas A and B, as proposed in an embodiment of this application. Figure 4 The splicing line shown (the solid vertical line in the figure) cuts out the redundant seismic data in work areas A and B, and then splices them together to obtain the spliced ​​seismic data of work areas A and B. Figure 4 The data to the left of the center splicing line is for seismic area A, and the data to the right of the center splicing line is for seismic area B.

[0027] After stitching together seismic data, closure error correction is performed to improve the quality and interpretation accuracy of the stitched data, forming a complete stitched data volume. Specifically, closure error analysis is first performed on the stitched seismic data. Closure errors can be caused by various factors, such as differences in excitation factors and receiving conditions between seismic data acquired at different construction periods, as well as differences in processing systems, procedures, and parameters. These factors can all lead to closure errors in amplitude, time, and phase of the seismic data. Based on the results of the closure error analysis, an appropriate correction method is selected. Commonly used correction methods include time shift correction, phase rotation, and amplitude gain adjustment. These methods can be used individually or in combination to achieve the best correction effect. Closure error correction is usually an iterative process. After initial correction, the data quality needs to be checked again to evaluate the correction effect. If closure errors still exist, the correction process needs to be repeated until the data quality requirements are met.

[0028] For example, the seismic data obtained by performing closure error correction processing on the stitched seismic data of work area A and work area B is as follows: Figure 5 and Figure 6 As shown ( Figure 5This is a schematic diagram of seismic data from work areas A and B before closure error correction and seamless processing, as proposed in an embodiment of this application. Figure 6 This is a schematic diagram of seismic data from work areas A and B after closure error correction and seamless processing, as proposed in an embodiment of this application. It can be seen that the closure error correction process improves the stitching effect of the seismic data from work areas A and B.

[0029] In specific implementation step 102, to eliminate the traces of the splicing line, this application proposes to denote the data within a given window centered on the splicing line as a data subdomain. Then, the data samples are used to form a planar plate with a grid-based free boundary and energy information. Multiple planar plates satisfy the fitting conditions at the splicing line and minimize the bending energy, resulting in a smooth surface based on multivariate splines, thereby eliminating the traces at the splicing line. First, for the entire spliced ​​data volume, a first time window and a second time window are selected around the splicing line in a first direction and a second direction, respectively. The data within the first and second time windows form a subset of the original data volume. The first direction can be an inLine direction, and the second direction can be an XLine direction. The sizes of the first and second time windows can be different. For two-dimensional data, the splicing line of the entire spliced ​​data volume is used as the center; for three-dimensional data, the spliced ​​surface of the entire spliced ​​data volume is used as the center. For example, for the entire mosaicked data, windows are selected around the mosaicking line in the inLine (XLine) direction. Since the seismic data from the selected work areas A and B overlap in the inLine direction, the mosaicking is performed in the XLine direction. Twenty tracks on each side of the XLine direction are selected as the horizontal data time windows. The subset of the original data volume formed within the window is as follows: Figure 7 As shown ( Figure 7 This is a schematic diagram of a subset of the original data body proposed in an embodiment of this application.

[0030] In specific implementation step 103, after obtaining the original data volume subset, in order to make the multiple planar plates formed by the data samples meet the fitting conditions at the splicing line and minimize the bending energy, a smooth surface based on multivariate splines is obtained. Therefore, it is proposed to perform multivariate spline fitting on the original data volume subset to obtain a smoothed new data volume subset.

[0031] In an optional embodiment of this application, step 103 above, which involves performing multivariate spline fitting on the original data volume subset to obtain a new data volume subset, specifically includes the following process: A loss function is formed based on the fitting term and the deformation term. The fitting term is used to measure the difference between the source point and the target point after deformation, and the deformation term is used to measure the magnitude of deformation. The loss function is used to determine the magnitude of the difference from the original data, perform quality control, and automatically adjust parameters according to acceptance criteria. To achieve a smooth fit on the original data volume subset, for any given source point in the original data volume subset, the value of that arbitrary source point in the smoothing function is calculated as the target point. All target points obtained by applying the smoothing function to all source points are then used as the new data volume subset. Through the above process, all source points contained in the data volume subset formed by the data within the window are smoothed. Multivariate spline fitting achieves a good smoothing effect while making the smoothed data closely resemble the original data values.

[0032] In one optional embodiment of this application, to make the original sample values ​​of the original data volume subset Refitting and smoothing to form new sample values First, we define two terms: one is the fitting term. The first is the difference between the source point and the target point after deformation; the second is the deformation term. , to measure the magnitude of deformation. The weighting coefficients represent the degree of deformation allowed, forming the loss function. The formula for calculating this loss function is shown below: , in, , , In the formula, E represents the loss function. Represents the fitted term, Indicates the deformation term. This represents the weighting coefficient, which is selected based on the complexity of the structure. A smaller coefficient is chosen for structures with large dip angles. If it is a single-story structure, then choose a larger size. . and Let i represent the i-th source point in the original data subset and the i-th target point in the new data subset, respectively. , , Source point in the original data volume subset coordinates, Represents a smoothing function. This refers to the range of discrete points used to form the smooth function.

[0033] In one optional embodiment of this application, in order to achieve both better fitting and smoothing of the data while keeping the data close to the original values, the principle of minimizing bending energy in multivariate spline fitting is applied. Based on the theory of multivariate spline fitting, the original data points are fitted to obtain a smooth function that satisfies this condition: , In the formula, For any source point in the subset of the original data body, i.e., the source point , It is any real number. This indicates the weighting of different radial bases, i.e., the degree to which deformation is allowed to occur. It is a radial basis function, representing The deformation is affected by the deformation of the control points, and the calculation formula is: , , Indicates the number of control points. Source point in the original data volume subset The corresponding control points. In the embodiments of this application, the number of control points and source points may be the same or different.

[0034] In an optional embodiment of this application, in order for the smoothing function to obtain a unique solution, the smoothing function must satisfy the following constraints: , In the formula, Source point in the original data volume subset The corresponding control points, , This indicates the number of control points.

[0035] Assuming each control point corresponds to a height, that is, each control point... Each corresponds to one To solve this problem, the control points are represented as a matrix: , This is a control point matrix, where each row represents the coordinates of a control point. This is achieved by forming a plane from the original data subset according to the sampling point numbers, and then stitching together the control points set around the line. , This indicates the number of control points.

[0036] The attribute matrix is ​​represented as follows: , This represents the attribute matrix, which is the attribute value at each control point. Here, the amplitude value is used, and it is filled with 0 for consistency in calculation.

[0037] The radial basis function matrix is ​​represented as follows: , The matrix is ​​a basis function matrix, indicating that the deformation of each point on the surface is affected by the deformation of all control points. This indicates the distance between two control points. , This indicates the number of control points.

[0038] In summary, the above equations and constraints yield an equivalent system of equations: , in: , , This indicates the number of control points, for each... =0,1, N, where N is the number of samples in the time direction, solve this equation to obtain the unknowns. According to the solution obtained The formula for calculating the smoothing function is given. The target point. That is, once the smoothing function... It is known that, given any point in the original data, it can be interpolated to a smooth target data volume with positional information using a smoothing function. The union of subsets of the target data volume is the resulting new data volume.

[0039] For example, the new data volume subset obtained after multivariate spline fitting is as follows: Figure 8 As shown, Figure 8 This is a schematic diagram showing a new data volume subset after fitting a subset of the original data volume and multivariate splines, as proposed in an embodiment of this application. Figure 8 In Figure 'a', the data of the original data volume subset is displayed as a schematic diagram, and in Figure 'b', the data of the new data volume subset after multivariate spline fitting is displayed as a schematic diagram. It can be seen that the data of the data volume subset after multivariate spline fitting eliminates the traces of splicing lines.

[0040] In specific implementation step 104, the new data subset and the data in the first and second time windows are mixed using inverse distance weighting coefficients to obtain new data in the first and second time windows.

[0041] In an optional embodiment of this application, the calculation formula used for the mixing processing of the new data volume subset and the data in the first and second time windows according to the inverse distance weighting coefficient is as follows: , In the formula, This represents the amplitude value at the target point after wave mixing. The amplitude value at the source point. The values ​​are the source points after multivariate spline fitting. These are the inverse distance weighted coefficients.

[0042] For example, the data in the new first and second time windows after mixing processing are as follows: Figure 9 As shown, Figure 9 This is a schematic diagram illustrating the original data volume subset, the data subset before mixing, and the new data volume subset after mixing, as proposed in an embodiment of this application. Figure 9 The adjustable coefficient ρ for the mixing process shown in the image is 1, and j = 1, 2, 3, 4, 5. In actual processing, 5 channels are taken in each direction, which better ensures the fidelity of the data. Figure 9 In Figure 1, 'a' shows a schematic diagram of the data display of the original data volume subset, 'b' shows a schematic diagram of the data display of the new data volume subset, and 'c' shows a schematic diagram of the data display within the new first and second time windows. It can be seen that the data processed by inverse distance weighting coefficients effectively eliminates splicing artifacts.

[0043] In specific implementation step 105, the data within the first and second time windows are replaced with new data from the first and second time windows, and then merged with the original data of the entire spliced ​​data volume to form the entire spliced ​​result data. This application selects to operate within an hourly window near the splicing line, which not only ensures the fidelity of the data outside the time window, but also performs surface fitting and smoothing on the splicing traces of the data within the time window, eliminating splicing traces and facilitating subsequent structural interpretation and attribute analysis of the basin (region or zone).

[0044] For example, the above-mentioned data of the entire stitched result can be found in [reference]. Figure 10 , Figure 11 , Figure 12 ,and Figure 13 , Figure 10 This is a schematic diagram showing the result of merging the processed data with the original data of the entire stitched data volume, as proposed in an embodiment of this application. Figure 11 This is a schematic diagram showing partial details of the whole piece of stitched data before and after seamless processing, according to an embodiment of this application. Figure 12 This is a schematic diagram of isochronous slices of the whole-block stitched result data before and after seamless processing, according to an embodiment of this application. Figure 13This is a planar diagram showing the residual closure error statistics of the whole-block mosaicked data before and after seamless processing, as proposed in an embodiment of this application. It can be seen that the whole-block mosaicked data obtained after seamless processing shows no mosaicking traces on the InLine, XLine, isochronous slices, and slices along layer attributes. The residual closure error corresponding to the mosaicked seismic data should be less than the sampling rate. Figure 13 The remaining closure error of the splicing line is less than 1 ms at the sampling rate.

[0045] This application proposes a method for seamless stitching of stitching lines during post-stack seismic data volume stitching. The method involves acquiring seismic data from multiple blocks and selecting a stitching line based on the coordinate overlap area of ​​the seismic data blocks. After performing closure error correction on the seismic data to be corrected using the stitching line as the boundary, a complete stitched data volume is formed using the stitching line as the stitching boundary. A first time window and a second time window are selected around the stitching line in a first and a second direction, respectively, forming an original data volume subset. This original data volume subset is then subjected to multivariate spline fitting to obtain a new data volume subset. The new data volume subset is then mixed with the data in the first and second time windows using inverse distance weighting coefficients to obtain new data in the first and second time windows. Finally, the new data in the first and second time windows replaces the data in the first and second time windows and is merged with the original data in the complete stitched data volume to form the complete stitched result data.

[0046] This application creates a window near the splicing line of the post-stitched data volume. The data within the window range forms a data subset. A multivariate spline fitting and smoothing method is used to process the data subset to form a new data subset. As needed, the new data volume and the original data volume are mixed using the distance between the target line and the splicing line position as a weighting factor. Finally, the processing effect of eliminating splicing line traces when splicing multiple data blocks into a whole spliced ​​result is obtained. It also ensures high fidelity of the data outside the window range and high fidelity of the data inside the window range. This plays an important role in the subsequent formation of basin-level (regional or zone-level) result data volumes spliced ​​from multiple blocks.

[0047] In a second aspect of this application, a device for seamless stitching of stitched seismic data volumes is provided, see [reference]. Figure 14 , Figure 14 This is an architectural diagram of a device for seamless stitching of post-stack seismic data volumes, as proposed in an embodiment of this application. The device includes: The acquisition and correction module 1401 is used to acquire multi-block seismic data, and select a splicing line based on the coordinate overlap area where the multi-block seismic data is located. After performing closure error correction processing on the seismic data to be corrected with the splicing line as the boundary, the entire spliced ​​data body is formed with the splicing line as the splicing boundary. The original data body subset acquisition module 1402 is used to select a first time window and a second time window around the splicing line in a first direction and a second direction, with the splicing line as the center, and form an original data body subset with the data in the first time window and the second time window; The fitting module 1403 is used to perform multivariate spline fitting on the original data volume subset to obtain a new data volume subset; The mixing module 1404 is used to perform mixing processing on the new data volume subset and the data in the first time window and the second time window according to the inverse distance weighting coefficient to obtain the new data in the first time window and the second time window; The splicing module 1405 is used to replace the data in the first time window and the second time window with the data in the new first time window and the second time window, and merge it with the original data of the whole splicing data body to form the whole splicing result data.

[0048] The fitting module includes: A submodule is formed to generate a loss function based on a fitting term and a deformation term. The fitting term is used to measure the difference between the source point and the target point after deformation, and the deformation term is used to measure the magnitude of deformation. The first calculation submodule is used to calculate the value of the smoothing function for any given source point in the original data volume subset as the target point in order to achieve fitting smoothness for the original data volume subset. The second calculation submodule is used to take all target points obtained by calculating all source points through the smoothing function as a new subset of data.

[0049] The calculation formula for the loss function in the forming submodule is as follows: , in, , , In the formula, E represents the loss function. Represents the fitted term, Indicates the deformation term. Indicates the weighting coefficient. and Let i represent the i-th source point in the original data subset and the i-th target point in the new data subset, respectively. , , Source point in the original data volume subset coordinates, Represents a smoothing function. This refers to the range of discrete points used to form the smooth function.

[0050] The calculation formula for the smoothing function in the first calculation submodule is as follows: , In the formula, Source point in the original data volume subset , It is any real number. This indicates the weighting of different radial bases, i.e., the degree to which deformation is allowed to occur. It is a radial basis function, and the calculation formula is: , , Indicates the number of control points. Source point in the original data volume subset The corresponding control points.

[0051] The smoothing function in the first calculation submodule satisfies the following constraints: , In the formula, Source point in the original data volume subset The corresponding control points.

[0052] The calculation formula used in the mixing module is as follows: , In the formula, This represents the amplitude value at the target point after wave mixing. The amplitude value at the source point. The values ​​are the source points after multivariate spline fitting. These are the inverse distance weighted coefficients.

[0053] Based on the same concept, this application discloses an electronic device in a third aspect. Figure 15 A schematic diagram of an electronic device disclosed in an embodiment of this application is shown, such as... Figure 15 As shown, the electronic device 100 includes a memory 110 and a processor 120. The memory of the electronic device is not less than 1T, and the main frequency of the processor is not less than 2.4GHz. The memory 110 and the processor 120 are connected by a bus communication. The memory 110 stores a computer program, which can run on the processor 120 to implement a seamless stitching method for stitching post-stack seismic data volumes disclosed in the embodiments of this application.

[0054] Based on the same concept, this application discloses a computer-readable storage medium storing a computer program / instruction thereon in a fourth aspect. When the computer program / instruction is executed by a processor, it implements a method for seamless stitching of stitching lines during post-stack seismic data volume stitching disclosed in this application.

[0055] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0056] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0059] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0060] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0061] The above provides a detailed description of the method and apparatus for seamless stitching of post-stack seismic data volumes. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for seamless stitching of stitching lines during post-stack seismic data volume stitching, characterized in that, The method includes: Acquire multi-block seismic data, and based on the coordinate overlap area of ​​the multi-block seismic data, select a splicing line. After performing closure error correction on the seismic data to be corrected with the splicing line as the boundary, form a whole spliced ​​data volume with the splicing line as the splicing boundary. For the entire spliced ​​data body, with the splicing line as the center, a first time window and a second time window are selected in the first and second directions around the splicing line, and the data in the first and second time windows form a subset of the original data body; Multivariate spline fitting is performed on the original data volume subset to obtain a new data volume subset; The new data subset is mixed with the data in the first and second time windows using inverse distance weighting coefficients to obtain new data in the first and second time windows; The data in the first and second time windows are replaced with the data in the new first and second time windows, and then merged with the original data of the whole spliced ​​data body to form the whole spliced ​​result data.

2. The method for seamless stitching of post-stack seismic data volumes according to claim 1, characterized in that, Multivariate spline fitting is performed on the original data volume subset to obtain a new data volume subset, including: A loss function is formed based on the fitting term and the deformation term. The fitting term is used to measure the difference between the source point and the target point after deformation, and the deformation term is used to measure the magnitude of deformation. To achieve a smooth fit in the original data subset, for any given source point in the original data subset, the value of the smoothing function is calculated as the target point. All target points obtained by applying the smoothing function to all source points are used as a new subset of the data volume.

3. The method for seamless stitching of post-stack seismic data volumes according to claim 2, characterized in that, The formula for calculating the loss function is as follows: ， in, , ， In the formula, E represents the loss function. Represents the fitted term, Indicates the deformation term. Indicates the weighting coefficient. and Let i represent the i-th source point in the original data subset and the i-th target point in the new data subset, respectively. , , Source point in the original data volume subset coordinates, Represents a smoothing function. This refers to the range of discrete points used to form the smooth function.

4. The method for seamless stitching of post-stack seismic data volumes according to claim 2, characterized in that, The formula for calculating the smoothing function is shown below: ， In the formula, Source point in the original data volume subset , It is any real number. This indicates the weighting of different radial bases, i.e., the degree to which deformation is allowed to occur. It is a radial basis function, and the calculation formula is: , , Indicates the number of control points. Source point in the original data volume subset The corresponding control points.

5. The method for seamless stitching of post-stack seismic data volumes according to claim 4, characterized in that, The smoothing function satisfies the following constraints: ， In the formula, Source point in the original data volume subset The corresponding control points.

6. The method for seamless stitching of post-stack seismic data volumes according to claim 1, characterized in that, The calculation formula used for the mixing processing of the new data volume subset with the data in the first and second time windows using inverse distance weighting coefficients is as follows: ， In the formula, This represents the amplitude value at the target point after wave mixing. The amplitude value at the source point. The values ​​are the source points after multivariate spline fitting. These are the inverse distance weighted coefficients.

7. A device for seamless stitching of stitched seismic data volumes, characterized in that, The device includes: The acquisition and correction module is used to acquire multi-block seismic data, and select a splicing line based on the coordinate overlap area of ​​the multi-block seismic data. After performing closure error correction processing on the seismic data to be corrected with the splicing line as the boundary, the entire spliced ​​data body is formed with the splicing line as the splicing boundary. The original data body subset acquisition module is used to select a first time window and a second time window around the splicing line in a first direction and a second direction, with the splicing line as the center, and to form an original data body subset with the data in the first time window and the second time window; The fitting module is used to perform multivariate spline fitting on the original data volume subset to obtain a new data volume subset. The mixing module is used to perform mixing processing on the new data volume subset and the data in the first time window and the second time window according to the inverse distance weighting coefficient to obtain the new data in the first time window and the second time window; The splicing module is used to replace the data in the first and second time windows with the data in the new first and second time windows, and merge it with the original data of the whole splicing data body to form the whole splicing result data.

8. The seamless stitching device for stitching post-stack seismic data volumes according to claim 7, characterized in that, The fitting module includes: A submodule is formed to generate a loss function based on a fitting term and a deformation term. The fitting term is used to measure the difference between the source point and the target point after deformation, and the deformation term is used to measure the magnitude of deformation. The first calculation submodule is used to calculate the value of the smoothing function for any given source point in the original data volume subset as the target point in order to achieve fitting smoothness for the original data volume subset. The second calculation submodule is used to take all target points obtained by calculating all source points through the smoothing function as a new subset of data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the seamless stitching method for stitching post-stack seismic data volumes as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, It stores a computer program / instruction, which, when executed by a processor, implements the seamless stitching method for stitching post-stack seismic data volumes as described in any one of claims 1-6.

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