Seismic data reconstruction method based on binary tree structure, processor and storage medium
By using a seismic data reconstruction method based on a binary tree structure, target sampling points are quickly determined and polynomial interpolation is performed, which solves the problem of slow seismic data reconstruction speed in existing technologies and achieves fast and robust data reconstruction results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF PETROLEUM (BEIJING)
- Filing Date
- 2023-08-23
- Publication Date
- 2026-06-23
AI Technical Summary
Existing seismic data reconstruction methods are slow and unromantic when processing large amounts of data, and cannot reconstruct data quickly and effectively.
A seismic data reconstruction method based on a binary tree structure is adopted. By obtaining the coordinates of the sampling points, dividing the grid nodes, determining the target sampling points, and reconstructing the seismic data using polynomial interpolation coefficients.
It enables rapid and robust reconstruction of large amounts of seismic data, improving data processing efficiency and accuracy.
Smart Images
Figure CN117092698B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of earthquake analysis, specifically to a method for earthquake data reconstruction based on a binary tree structure, a processor, and a storage medium. Background Technology
[0002] The field of seismic data reconstruction is rapidly developing, and existing reconstruction methods include transform-based methods, wave equation-based methods, dip-scan-based methods, and others. However, each reconstruction method has its applicable conditions and scope, and certain drawbacks. For example, transform-based methods require high-quality seismic data; if the signal-to-noise ratio of the original data is low, high-frequency components are easily lost after reconstruction. Wave equation-based methods require prior knowledge of some subsurface medium information, and are complex and computationally expensive, making them unsuitable for large-scale production applications. Dip-scan-based methods require phase axes to have a certain dip angle distribution range, but their reconstruction effect is poor for seismic data with complex structures. Existing technologies cannot quickly and robustly reconstruct large amounts of data. Summary of the Invention
[0003] The purpose of this application is to provide a binary tree-based seismic data reconstruction method, processor, and storage medium that can rapidly reconstruct large amounts of seismic data.
[0004] To achieve the above objectives, embodiments of this application provide a seismic data reconstruction method based on a binary tree structure, characterized in that the method includes:
[0005] Acquire seismic data to be reconstructed from multiple sampling points. The seismic data to be reconstructed includes the sampling coordinates of the sampling points, wherein each sampling coordinate includes a sampling x-coordinate and a sampling y-coordinate.
[0006] The node coordinates corresponding to multiple grid nodes are determined according to preset rules;
[0007] Based on the binary tree structure, each sampling point is divided according to the horizontal and vertical coordinates to obtain multiple sampling point sets, and the horizontal and vertical coordinate intervals corresponding to each sampling point set are determined.
[0008] The target sampling point set corresponding to each grid node is determined based on the x-coordinate interval and the y-coordinate interval.
[0009] For each grid node, determine the distance between the grid node and all sampling points in the corresponding target sampling point set;
[0010] For each grid node, the sampling point with the shortest distance from the corresponding target sampling point set is determined as the target sampling point corresponding to the grid node;
[0011] For each grid node, the seismic data collected from the target sampling point is reconstructed to obtain the target seismic data corresponding to the grid node.
[0012] In this embodiment of the application, determining the node coordinates corresponding to multiple grid nodes according to preset rules includes: determining a target region based on the maximum, minimum, maximum, and minimum sampled x-coordinates, where all sampled points are within the target region; determining a minimum region including all sampled points based on the maximum, minimum, maximum, and minimum sampled x-coordinates, and using the minimum region as the target region; dividing the target region into multiple grids according to preset intervals; and determining the node coordinates corresponding to each grid node, where each node coordinate includes a node x-coordinate and a node y-coordinate.
[0013] In this embodiment, each sampling point is divided according to the horizontal and vertical coordinates based on a binary tree structure to obtain multiple sampling point sets. Determining the horizontal and vertical coordinate intervals corresponding to each sampling point set includes: determining the horizontal median value of all sampled horizontal coordinates and the vertical median value of all sampled vertical coordinates; identifying sampling points whose horizontal coordinates are less than the horizontal median value as the first sampling point set, and identifying sampling points whose horizontal coordinates are greater than or equal to the horizontal median value as the second sampling point set; identifying sampling points in the first sampling point set whose vertical coordinates are less than the vertical median value as the third sampling point set, and identifying sampling points whose vertical coordinates are greater than or equal to the vertical median value as the fourth sampling point set; identifying sampling points in the second sampling point set whose vertical coordinates are less than the vertical median value as the fifth sampling point set, and identifying sampling points whose vertical coordinates are greater than or equal to the vertical median value as the sixth sampling point set; and determining the horizontal and vertical coordinate intervals corresponding to the third, fourth, fifth, and sixth sampling point sets, respectively.
[0014] In this embodiment of the application, determining the target sampling point set corresponding to each grid node based on the horizontal coordinate interval and the vertical coordinate interval includes: for each grid node, determining the target horizontal coordinate interval corresponding to the horizontal coordinate of the grid node and the target vertical coordinate interval corresponding to the vertical coordinate of the grid node; for each grid node, determining the target sampling point set corresponding to the grid node based on the target horizontal coordinate interval and the target vertical coordinate interval.
[0015] In this embodiment, the seismic data includes seismic amplitude and seismic waves. For each grid node, the seismic data collected by the target sampling point is reconstructed to obtain the target seismic data corresponding to the grid node. This includes: for each grid node, determining the distance between the grid node and the target sampling point and the propagation velocity of the seismic waves collected by the target sampling point in the formation medium of the target area; for each grid node, determining the reconstruction time point corresponding to the sampling time point of the target sampling point at the grid node based on the distance and propagation velocity; obtaining the first seismic amplitude collected by the target sampling point at the sampling time point; obtaining multiple second seismic amplitudes collected by the target sampling point at preset intervals based on the sampling time point; and determining the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and the multiple second seismic amplitudes.
[0016] In this embodiment of the application, the seismic data includes seismic amplitude. Based on the sampling time point, multiple second seismic amplitudes collected from the target sampling points are obtained at preset intervals. This includes: based on the sampling time point, obtaining the second seismic amplitudes of N target sampling points before the sampling time point and the second seismic amplitudes of M target sampling points after the sampling time point, where N and M are constants.
[0017] In this embodiment of the application, determining the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and multiple second seismic amplitudes includes: determining the polynomial interpolation coefficients of each second seismic amplitude; determining the product of each second seismic amplitude and the corresponding polynomial interpolation coefficients; and determining the sum of the multiple products and the sum of the first seismic amplitude as the node seismic amplitude corresponding to the grid node at the reconstruction time point.
[0018] In this embodiment of the application, the method further includes: when the distance between the grid node and the target sampling point is less than a preset distance, determining the first seismic amplitude as the node seismic amplitude corresponding to the grid node at the reconstruction time point.
[0019] A second aspect of this application provides a processor configured to execute a binary tree-based seismic data reconstruction method according to any one of the foregoing.
[0020] A third aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform a binary tree-based seismic data reconstruction method according to any one of the foregoing.
[0021] The above technical solution classifies target sampling points using a binary tree structure method, thereby quickly determining the target sampling points corresponding to grid nodes. This allows for the reconstruction of seismic data from the target sampling points to obtain the target seismic data corresponding to the grid nodes.
[0022] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description
[0023] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:
[0024] Figure 1 A flowchart illustrating a seismic data reconstruction method based on a binary tree structure according to an embodiment of this application is shown schematically.
[0025] Figure 2 An example diagram illustrating a grid node and its corresponding target sampling point according to an embodiment of this application is shown.
[0026] Figure 3 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation
[0027] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this application.
[0028] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.
[0029] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.
[0030] like Figure 1 The diagram illustrates a flowchart of a seismic data reconstruction method based on a binary tree structure according to an embodiment of this application. Figure 1As shown, a seismic data reconstruction method based on a binary tree structure is provided, including the following steps:
[0031] Step 101: Obtain seismic data to be reconstructed from multiple sampling points. The seismic data to be reconstructed includes the sampling coordinates of the sampling points, wherein each sampling coordinate includes a sampling x-coordinate and a sampling y-coordinate.
[0032] Step 102: Determine the node coordinates corresponding to multiple grid nodes according to preset rules;
[0033] Step 103: Based on the binary tree structure, divide each sampling point according to the horizontal and vertical coordinates to obtain multiple sampling point sets, and determine the horizontal and vertical coordinate intervals corresponding to each sampling point set.
[0034] Step 104: Determine the target sampling point set corresponding to each grid node based on the x-coordinate interval and y-coordinate interval;
[0035] Step 105: For each grid node, determine the distance between the grid node and all sampling points in the corresponding target sampling point set;
[0036] Step 106: For each grid node, determine the sampling point with the shortest distance from the corresponding target sampling point cluster as the target sampling point corresponding to the grid node;
[0037] Step 107: For each grid node, reconstruct the seismic data collected from the target sampling point to obtain the target seismic data corresponding to the grid node.
[0038] The processor can acquire seismic data to be reconstructed from multiple sampling points. This data includes the sampling coordinates of the sampling points, with each coordinate consisting of an abscissa and a ordinate. The processor can divide the data into grids according to preset rules and determine the node coordinates of multiple grid nodes based on the resulting grids. The processor can divide each sampling point using a binary tree structure based on its abscissa and ordinate, thus obtaining multiple sampling point sets. After determining these sets, the processor can determine the abscissa and ordinate intervals for each set. Based on these intervals, the processor determines the target sampling point set for each grid node. The target sampling point set can include multiple sampling points. For each grid node, the processor can determine the distance between that grid node and each sampling point in the corresponding target sampling point set. For each grid node, the processor can identify the sampling point in the target sampling point set that is closest to that grid node as the target sampling point. After identifying the target sampling point for each grid node, the processor can reconstruct the seismic data acquired at that target sampling point to obtain the target seismic data corresponding to that grid node. For example... Figure 2 As shown, black dots represent the original data coordinates, which are also the coordinates corresponding to the sampling points. White dots represent grid regularization points, which are multiple grid nodes determined by the processor after dividing the grid according to preset rules. The processor can divide the sampling points (i.e., the original data coordinates in the figure) using a binary tree structure. For each grid node (i.e., the grid regularization point in the figure), the processor can determine the nearest sampling point to that grid node as the target sampling point (i.e., the original data coordinates in the figure), thereby determining the target sampling point (i.e., the corresponding data coordinates in the figure) corresponding to the grid node (i.e., the grid regularization point in the figure). Due to the uneven distribution of sampling points, there may be a situation where multiple grid nodes (i.e., the grid regularization points in the figure) correspond to the same target sampling point (i.e., the original data coordinates in the figure).
[0039] In one embodiment, determining the node coordinates corresponding to multiple grid nodes according to preset rules includes: determining a target region based on the maximum, minimum, maximum, and minimum sampled x-coordinates, where all sampled points are within the target region; determining a minimum region including all sampled points based on the maximum, minimum, maximum, and minimum sampled x-coordinates, and using the minimum region as the target region; dividing the target region into multiple grids according to preset intervals; and determining the node coordinates corresponding to each grid node, where each node coordinate includes a node x-coordinate and a node y-coordinate.
[0040] The processor can determine the maximum x-coordinate, minimum y-coordinate, maximum ordinate, and minimum ordinate of all sampled coordinates. Based on these coordinates, it determines the smallest region that can include all sampled points and designates this smallest region as the target region. After determining the target region, the processor can divide it into multiple grids according to a preset interval. This preset interval can be set based on user input and can include both horizontal and vertical spacing. After dividing the target region into grids according to the preset interval, the processor can determine the node coordinates corresponding to each grid node, where each grid node's coordinates include its x-coordinate and ordinate.
[0041] In one embodiment, each sampling point is divided according to the horizontal and vertical coordinates based on a binary tree structure to obtain multiple sampling point sets. Determining the horizontal and vertical coordinate intervals corresponding to each sampling point set includes: determining the horizontal median of all sampled horizontal coordinates and the vertical median of all sampled vertical coordinates; identifying sampling points whose horizontal coordinates are less than the horizontal median as the first sampling point set, and identifying sampling points whose horizontal coordinates are greater than or equal to the horizontal median as the second sampling point set; identifying sampling points in the first sampling point set whose vertical coordinates are less than the vertical median as the third sampling point set, and identifying sampling points whose vertical coordinates are greater than or equal to the vertical median as the fourth sampling point set; identifying sampling points in the second sampling point set whose vertical coordinates are less than the vertical median as the fifth sampling point set, and identifying sampling points whose vertical coordinates are greater than or equal to the vertical median as the sixth sampling point set; and determining the horizontal and vertical coordinate intervals corresponding to the third, fourth, fifth, and sixth sampling point sets, respectively.
[0042] The processor can partition each sampling point according to its x and y coordinates using a binary tree structure to obtain multiple sampling point sets. The processor can determine the x and y coordinates of each sampling point, and determine the horizontal median value based on all the x coordinates and the vertical median value based on all the y coordinates. The processor can define the sampling points whose x coordinates are less than the horizontal median value as the first sampling point set, and the sampling points whose x coordinates are greater than or equal to the horizontal median value as the second sampling point set. For example, suppose the processor determines the horizontal median value to be x. k The processor can sample coordinates as (x i <x k y i The sampling points at (x) are determined as the sampling points included in the first sampling point set, and the sampling coordinates are (x) i ≥x k yi The sampling points of the first and second sampling point sets are determined as the sampling points included in the second sampling point set. After determining the first and second sampling point sets, for the first sampling point set, the processor can determine the sampling points whose ordinate is less than the vertical median value as the third sampling point set, and the sampling points whose ordinate is greater than or equal to the vertical median value as the fourth sampling point set. For the second sampling point set, the processor can determine the sampling points whose ordinate is less than the vertical median value as the fifth sampling point set, and the sampling points whose ordinate is greater than or equal to the vertical median value as the sixth sampling point set. For example, suppose the processor determines the horizontal median value as y. k The processor can set the first sampling points together, with the sampling coordinates being (x... i <x k y i <y k The sampling points at (x) are determined as the sampling points included in the third sampling point set, and the sampling coordinates are (x) i <x k y i ≥y k The sampling points at (x) are determined as the sampling points included in the fourth sampling point set. For the second sampling point set, the processor can select sampling points with coordinates (x) i ≥x k y i <y k The sampling points at (x) are determined as the sampling points included in the fifth sampling point set, and the sampling coordinates are (x) i ≥x k y i ≥y k The sampling points of the sample point set are determined to be included in the sixth sampling point set. After determining multiple sampling point sets, the processor can also determine the corresponding x-coordinate intervals and y-coordinate intervals for the third, fourth, fifth, and sixth sampling point sets based on the maximum x-coordinate, minimum x-coordinate, maximum y-coordinate, minimum y-coordinate, horizontal median, and vertical median values in the sampling coordinates. For example, the processor can determine the x-coordinate interval of the third sampling point set as [x...]. min x k ), the ordinate interval is [y min y k The x-coordinate interval of the fourth sampling point set is [x min x k ), the ordinate interval is [y k y max The x-coordinate interval of the fifth sampling point is [x k x maxThe vertical coordinate interval is [y]. min y k The x-coordinate interval of the sixth sampling point is [x k x max The vertical coordinate interval is [y]. k y max ].
[0043] In one embodiment, determining the target sampling point set corresponding to each grid node based on the x-coordinate interval and the y-coordinate interval includes: for each grid node, determining the target x-coordinate interval corresponding to the node's x-coordinate and the target y-coordinate interval corresponding to the node's y-coordinate; and for each grid node, determining the target sampling point set corresponding to the grid node based on the target x-coordinate interval and the target y-coordinate interval.
[0044] After obtaining the node coordinates of the grid nodes, the processor can determine the target x-coordinate interval and the target y-coordinate interval corresponding to the x-coordinate of the grid node for each grid node. Based on the target x-coordinate interval and the target y-coordinate interval corresponding to the grid node, the processor can determine the target sampling point set corresponding to the grid node from the third sampling point set, the fourth sampling point set, the fifth sampling point set, and the sixth sampling point set.
[0045] In one embodiment, the seismic data includes seismic amplitude and seismic waves. For each grid node, the seismic data collected by the target sampling point is reconstructed to obtain the target seismic data corresponding to the grid node. This includes: for each grid node, determining the distance between the grid node and the target sampling point and the propagation velocity of the seismic waves collected by the target sampling point in the formation medium of the target area; for each grid node, determining the reconstruction time point corresponding to the sampling time point of the target sampling point at the grid node based on the distance and propagation velocity; obtaining the first seismic amplitude collected by the target sampling point at the sampling time point; obtaining multiple second seismic amplitudes collected by the target sampling point at preset intervals based on the sampling time point; and determining the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and the multiple second seismic amplitudes.
[0046] After determining the target sampling point corresponding to each grid node, the processor can reconstruct the seismic data acquired from the target sampling point for each grid node. For each grid node, the processor can determine the distance between the grid node and the corresponding target sampling point, as well as the propagation velocity of the seismic waves acquired from the target sampling point within the formation medium of the target area. Based on the distance and the wave propagation velocity, the processor can determine the reconstruction time point corresponding to the sampling time point of the target sampling point at the grid node. For example, assuming the sampling time point of the target sampling point is t... sBased on the distance and propagation speed, if the time it takes for the seismic wave to travel from the target sampling point to the corresponding grid node within the geological medium of the target area is determined to be 0.5 s, then the processor can determine the reconstruction time point corresponding to the sampling time point of the target sampling point at that grid node as t. s +0.5s, meaning the target sampling point is at sampling time t. s The corresponding seismic data should be reconstructed into the corresponding grid nodes at t s Earthquake data corresponding to +0.5s.
[0047] For each grid node, the processor can obtain the first seismic amplitude collected at the sampling time point of the target sampling point corresponding to that grid node, and, based on the sampling time point of the target sampling point, obtain multiple second seismic amplitudes collected at the target sampling point at preset intervals. The preset intervals can be set according to user-input data. The processor can determine the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and the multiple second seismic amplitudes.
[0048] In one embodiment, the seismic data includes seismic amplitude. Acquiring multiple second seismic amplitudes of target sampling points at preset intervals, based on the sampling time point, includes: acquiring the second seismic amplitudes of N target sampling points before the sampling time point and the second seismic amplitudes of M target sampling points after the sampling time point, based on the sampling time point and at preset intervals, where N and M are constants.
[0049] In one embodiment, determining the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and multiple second seismic amplitudes includes: determining the polynomial interpolation coefficients of each second seismic amplitude; determining the product of each second seismic amplitude and the corresponding polynomial interpolation coefficients; and determining the sum of the multiple products and the sum of the first seismic amplitude as the node seismic amplitude corresponding to the grid node at the reconstruction time point.
[0050] When the processor acquires multiple second seismic amplitudes at the target sampling point at a preset interval, using the sampling time point as a reference, the processor can acquire the second seismic amplitudes of N target sampling points before the sampling time point and M target sampling points after the sampling time point at the preset interval, where N and M are constants. The processor can determine the polynomial interpolation coefficients for each acquired second seismic amplitude, determine the product of each second seismic amplitude and the corresponding polynomial difference coefficients, and determine the sum of each determined product and the sum of the first seismic amplitude as the node seismic amplitude corresponding to the grid node of the target sampling point at the reconstruction time point. For example, assuming the processor sets both N and M to 2, that is, the processor acquires the second seismic amplitudes of the target sampling points corresponding to two time points before the sampling time point at the preset interval, and acquires the second seismic amplitudes of the target sampling points corresponding to two time points after the sampling time point. Assuming the preset interval time set by the processor is 0.2 seconds, and the sampling time point of the target sampling point is t... s Then the processor can collect t samples before the sampling time point. s -0.2S as t s-1 , will t s -0.4S as t s-2 The processor can collect t after the sampling time point. s +0.2S as t s+1 , will t s +0.4S as t s+2 For example, suppose the processor can determine t based on user input data. s-2 The polynomial interpolation coefficients are t s-1 The polynomial interpolation coefficients are t s+1 The polynomial interpolation coefficients are t s+2 The polynomial interpolation coefficients can be Where F is the seismic amplitude value collected by the target sampling point when the target sampling point is at the sampling time point indicated by the lower right corner subscript.
[0051] In one embodiment, if the distance between the grid node and the target sampling point is less than a preset distance, the first seismic amplitude is determined as the node seismic amplitude corresponding to the grid node at the reconstruction time point.
[0052] For each grid node, the processor can determine the target sampling point based on the distance between the grid node and each sampling point in the corresponding target sampling point set. When the processor determines that the distance between the target sampling points corresponding to the grid node is less than the preset distance set by the processor, the processor can directly determine the first seismic amplitude collected by the target sampling point as the seismic amplitude corresponding to the grid node at the reconstruction time point.
[0053] In one embodiment, a processor is provided, configured to perform any of the above-described binary tree-based seismic data reconstruction methods.
[0054] The above technical solution classifies target sampling points using a binary tree structure method, thereby quickly determining the target sampling points corresponding to grid nodes, and then reconstructing the seismic data of the target sampling points to obtain the target seismic data corresponding to the grid nodes.
[0055] In one embodiment, a machine-readable storage medium is provided, on which instructions are stored, which, when executed by a processor, cause the processor to be configured to perform a binary tree-based seismic data reconstruction method according to any one of the foregoing.
[0056] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.
[0057] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor A01, a network interface A02, a memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computational and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores earthquake-related data collected from sampling points. The network interface A02 communicates with external terminals via a network connection. When the processor A01 executes the computer program B02, it implements a seismic data reconstruction method based on a binary tree structure.
[0058] Figure 1This is one embodiment of a seismic data reconstruction method based on a binary tree structure. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are executed, and they can be performed in other orders. Furthermore, Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0059] This application provides an apparatus including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring seismic data to be reconstructed from multiple sampling points, the seismic data including sampling coordinates of the sampling points, wherein each sampling coordinate includes a sampling abscissa and a sampling ordinate; determining the node coordinates corresponding to multiple grid nodes according to preset rules; dividing each sampling point according to its abscissa and ordinate based on a binary tree structure to obtain multiple sampling point sets, and determining the abscissa and ordinate intervals corresponding to each sampling point set; determining the target sampling point set corresponding to each grid node based on the abscissa and ordinate intervals; for each grid node, determining the distance between the grid node and all sampling points in the corresponding target sampling point set; for each grid node, determining the sampling point with the shortest distance in the corresponding target sampling point set as the target sampling point corresponding to the grid node; and reconstructing the seismic data collected from the target sampling points for each grid node to obtain the target seismic data corresponding to the grid node.
[0060] In one embodiment, determining the node coordinates corresponding to multiple grid nodes according to preset rules includes: determining a target region based on the maximum, minimum, maximum, and minimum sampled x-coordinates, where all sampled points are within the target region; determining a minimum region including all sampled points based on the maximum, minimum, maximum, and minimum sampled x-coordinates, and using the minimum region as the target region; dividing the target region into multiple grids according to preset intervals; and determining the node coordinates corresponding to each grid node, where each node coordinate includes a node x-coordinate and a node y-coordinate.
[0061] In one embodiment, each sampling point is divided according to the horizontal and vertical coordinates based on a binary tree structure to obtain multiple sampling point sets. Determining the horizontal and vertical coordinate intervals corresponding to each sampling point set includes: determining the horizontal median of all sampled horizontal coordinates and the vertical median of all sampled vertical coordinates; identifying sampling points whose horizontal coordinates are less than the horizontal median as the first sampling point set, and identifying sampling points whose horizontal coordinates are greater than or equal to the horizontal median as the second sampling point set; identifying sampling points in the first sampling point set whose vertical coordinates are less than the vertical median as the third sampling point set, and identifying sampling points whose vertical coordinates are greater than or equal to the vertical median as the fourth sampling point set; identifying sampling points in the second sampling point set whose vertical coordinates are less than the vertical median as the fifth sampling point set, and identifying sampling points whose vertical coordinates are greater than or equal to the vertical median as the sixth sampling point set; and determining the horizontal and vertical coordinate intervals corresponding to the third, fourth, fifth, and sixth sampling point sets, respectively.
[0062] In one embodiment, determining the target sampling point set corresponding to each grid node based on the x-coordinate interval and the y-coordinate interval includes: for each grid node, determining the target x-coordinate interval corresponding to the node's x-coordinate and the target y-coordinate interval corresponding to the node's y-coordinate; and for each grid node, determining the target sampling point set corresponding to the grid node based on the target x-coordinate interval and the target y-coordinate interval.
[0063] In one embodiment, the seismic data includes seismic amplitude and seismic waves. For each grid node, the seismic data collected by the target sampling point is reconstructed to obtain the target seismic data corresponding to the grid node. This includes: for each grid node, determining the distance between the grid node and the target sampling point and the propagation velocity of the seismic waves collected by the target sampling point in the formation medium of the target area; for each grid node, determining the reconstruction time point corresponding to the sampling time point of the target sampling point at the grid node based on the distance and propagation velocity; obtaining the first seismic amplitude collected by the target sampling point at the sampling time point; obtaining multiple second seismic amplitudes collected by the target sampling point at preset intervals based on the sampling time point; and determining the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and the multiple second seismic amplitudes.
[0064] In one embodiment, the seismic data includes seismic amplitude. Acquiring multiple second seismic amplitudes of target sampling points at preset intervals, based on the sampling time point, includes: acquiring the second seismic amplitudes of N target sampling points before the sampling time point and the second seismic amplitudes of M target sampling points after the sampling time point, based on the sampling time point and at preset intervals, where N and M are constants.
[0065] In one embodiment, determining the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and multiple second seismic amplitudes includes: determining the polynomial interpolation coefficients of each second seismic amplitude; determining the product of each second seismic amplitude and the corresponding polynomial interpolation coefficients; and determining the sum of the multiple products and the sum of the first seismic amplitude as the node seismic amplitude corresponding to the grid node at the reconstruction time point.
[0066] In one embodiment, the method further includes: when the distance between the grid node and the target sampling point is less than a preset distance, determining the first seismic amplitude as the node seismic amplitude corresponding to the grid node at the reconstruction time point.
[0067] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0068] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function 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.
[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable 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.
[0071] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0072] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0073] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0074] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0075] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A seismic data reconstruction method based on a binary tree structure, characterized in that, The method includes: Acquire seismic data to be reconstructed from multiple sampling points, wherein the seismic data to be reconstructed includes the sampling coordinates of the sampling points, wherein each sampling coordinate includes a sampling x-coordinate and a sampling y-coordinate; The node coordinates corresponding to multiple grid nodes are determined according to preset rules; Based on the binary tree structure, each sampling point is divided according to the horizontal and vertical coordinates to obtain multiple sampling point sets, and the horizontal and vertical coordinate intervals corresponding to each sampling point set are determined. The target sampling point set corresponding to each grid node is determined based on the horizontal coordinate interval and the vertical coordinate interval; For each grid node, determine the distance between the grid node and all sampling points in the corresponding target sampling point set; For each grid node, the sampling point with the shortest distance from the corresponding target sampling point set is determined as the target sampling point corresponding to the grid node; For each grid node, the seismic data collected at the target sampling point is reconstructed to obtain the target seismic data corresponding to the grid node. The seismic data includes seismic amplitude and seismic waves. Specifically, the process of reconstructing the seismic data collected at the target sampling point for each grid node to obtain the target seismic data corresponding to that grid node includes: For each grid node, determine the distance between the grid node and the target sampling point and the propagation speed of the seismic wave collected by the target sampling point in the geological medium of the target area; For each grid node, the reconstruction time point corresponding to the sampling time point of the target sampling point at the grid node is determined based on the distance and the propagation speed; Obtain the first seismic amplitude collected at the target sampling point at the sampling time point; Based on the sampling time point, multiple second seismic amplitudes collected at the target sampling point are obtained at preset intervals. The node seismic amplitude corresponding to the grid node at the reconstruction time point is determined based on the first seismic amplitude and the plurality of second seismic amplitudes.
2. The seismic data reconstruction method based on a binary tree structure according to claim 1, characterized in that, The step of determining the node coordinates corresponding to multiple grid nodes according to preset rules includes: The target region is determined based on the maximum and minimum abscissas, maximum and minimum ordinates of all sampled coordinates, wherein all sampled points are within the target region. The minimum region encompassing all sampling points is determined based on the maximum and minimum sampling x-coordinates, the maximum and minimum sampling y-coordinates among all sampling coordinates, and this minimum region is taken as the target region. The target area is divided into multiple grids according to a preset spacing; Determine the node coordinates corresponding to each grid node, where each node coordinate includes the node's x-coordinate and y-coordinate.
3. The seismic data reconstruction method based on a binary tree structure according to claim 1, characterized in that, The binary tree structure is used to divide each sampling point according to the x-coordinate and y-coordinate to obtain multiple sampling point sets, and the x-coordinate interval and y-coordinate interval corresponding to each sampling point set are determined as follows: Determine the horizontal median value of all sampled x-axis coordinates and the vertical median value of all sampled y-axis coordinates; The sampling points whose horizontal coordinates are less than the horizontal median value are determined as the first set of sampling points, and the sampling points whose horizontal coordinates are greater than or equal to the horizontal median value are determined as the second set of sampling points. The sampling points whose ordinates are less than the median value in the first sampling point set are determined as the third sampling point set, and the sampling points whose ordinates are greater than or equal to the median value in the vertical direction are determined as the fourth sampling point set. The sampling points whose ordinates are less than the median value in the second sampling point set are determined as the fifth sampling point set, and the sampling points whose ordinates are greater than or equal to the median value in the vertical direction are determined as the sixth sampling point set. Determine the x-axis and y-axis intervals corresponding to the third, fourth, fifth, and sixth sampling point sets, respectively.
4. The seismic data reconstruction method based on a binary tree structure according to claim 1, characterized in that, The step of determining the target sampling point set corresponding to each grid node based on the horizontal coordinate interval and the vertical coordinate interval includes: For each grid node, determine the target x-coordinate interval corresponding to the node's x-coordinate and the target y-coordinate interval corresponding to the node's y-coordinate; For each grid node, the target sampling point set corresponding to the grid node is determined based on the target horizontal coordinate interval and the target vertical coordinate interval.
5. The seismic data reconstruction method based on a binary tree structure according to claim 1, characterized in that, The seismic data includes seismic amplitudes, and the acquisition of multiple second seismic amplitudes collected at the target sampling point at preset intervals, based on the sampling time point, includes: Based on the sampling time point, the second seismic amplitudes of the N target sampling points before the sampling time point and the second seismic amplitudes of the M target sampling points after the sampling time point are obtained at preset intervals, where N and M are constants.
6. The seismic data reconstruction method based on a binary tree structure according to claim 5, characterized in that, The step of determining the node seismic amplitude corresponding to the grid node at the reconstruction time point based on the first seismic amplitude and the plurality of second seismic amplitudes includes: Determine the polynomial interpolation coefficients for each second earthquake amplitude; The product of each second seismic amplitude and its corresponding polynomial interpolation coefficient is determined, and the sum of the multiple products and the sum of the first seismic amplitude are determined as the node seismic amplitude of the grid node at the reconstruction time point.
7. The seismic data reconstruction method based on a binary tree structure according to claim 1, characterized in that, The method further includes: If the distance between the grid node and the target sampling point is less than a preset distance, the first seismic amplitude is determined as the node seismic amplitude of the grid node at the reconstruction time point.
8. A processor, characterized in that, It is configured to perform the seismic data reconstruction method based on a binary tree structure as described in any one of claims 1 to 7.
9. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the seismic data reconstruction method based on a binary tree structure according to any one of claims 1 to 7.