Method and device for establishing near-surface model, storage medium and electronic equipment

Abstract

The invention provides a method and device for establishing a near-surface model, a storage medium and electronic equipment, and the method for establishing the near-surface model comprises the steps: obtaining attribute point data of a plurality of two-dimensional seismic survey lines; acquiring a spatial range of the near-surface model according to the attribute point data; acquiring first point data of each two-dimensional seismic survey line in the plurality of two-dimensional seismic survey lines at the earth surface and second point data of each two-dimensional seismic survey line at the top layer; acquiring a first three-dimensional boundary curved surface according to the first point data and the spatial range of the near-surface model; acquiring a second three-dimensional boundary curved surface according to the second point data and the spatial range of the near-surface model; obtaining effective attribute point data; obtaining a three-dimensional grid model according to preset grid division parameters, the first three-dimensional boundary curved surface and the second three-dimensional boundary curved surface; and establishing a near-surface model according to the effective attribute point data and the three-dimensional grid model. According to the method, the precision and reliability of the near-surface model are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of earthquake data processing technology, specifically relating to a method, apparatus, storage medium, and electronic device for establishing a near-surface model. Background Technology

[0002] In petroleum geophysical exploration, establishing accurate near-surface models is a crucial foundation for seismic exploration in complex areas. Related technologies primarily rely on data acquired through dense 3D seismic surveys. However, in regions with complex surface conditions, full-area 3D exploration is often difficult, resulting in the acquisition of only a limited amount of 2D seismic data. When using traditional interpolation methods to directly construct near-surface models from these sparse 2D seismic lines, the lack of effective constraints on the spatial variations of near-surface structures makes it difficult to accurately depict the complex morphology of surface undulations and high-velocity top interfaces. Consequently, the constructed near-surface models deviate significantly from the actual geological conditions in terms of spatial geometry and physical property distribution, resulting in insufficient accuracy. Summary of the Invention

[0003] In a first aspect, embodiments of the present invention provide a method for establishing a near-surface model, comprising: acquiring attribute point data of multiple two-dimensional seismic survey lines; acquiring the spatial extent of the near-surface model based on the attribute point data; acquiring first point data at the surface and second point data at the top layer of each of the multiple two-dimensional seismic survey lines; acquiring a first three-dimensional boundary surface based on the first point data and the spatial extent of the near-surface model; acquiring a second three-dimensional boundary surface based on the second point data and the spatial extent of the near-surface model; acquiring valid attribute point data located between the first and second three-dimensional boundary surfaces from the attribute point data; acquiring a three-dimensional mesh model based on preset meshing parameters and the first and second three-dimensional boundary surfaces; and establishing a near-surface model based on the valid attribute point data and the three-dimensional mesh model.

[0004] Secondly, embodiments of the present invention provide an apparatus for establishing a near-surface model, comprising: a first acquisition unit for acquiring attribute point data of multiple two-dimensional seismic survey lines; a first processing unit for acquiring the spatial extent of the near-surface model based on the attribute point data; a second acquisition unit for acquiring first point data at the surface and second point data at the top layer of each of the multiple two-dimensional seismic survey lines; a second processing unit for acquiring a first three-dimensional boundary surface based on the first point data and the spatial extent of the near-surface model; a third processing unit for acquiring a second three-dimensional boundary surface based on the second point data and the spatial extent of the near-surface model; a fourth processing unit for acquiring valid attribute point data located between the first and second three-dimensional boundary surfaces from the attribute point data; a fifth processing unit for acquiring a three-dimensional mesh model based on preset mesh division parameters, the first and second three-dimensional boundary surfaces; and a sixth processing unit for establishing a near-surface model based on the valid attribute point data and the three-dimensional mesh model.

[0005] Thirdly, embodiments of the present invention provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for establishing a near-surface model as described in the first aspect.

[0006] Fourthly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for establishing a near-surface model as described in the first aspect.

[0007] The beneficial effects of this invention are as follows: This invention proposes a method for establishing a near-surface model, utilizing sparse two-dimensional seismic survey data to construct a high-precision three-dimensional near-surface model. The core of this invention lies in establishing a near-surface model for the entire work area through spatially constrained interpolation, providing a reliable geological model foundation for subsequent seismic exploration work.

[0008] This invention first acquires attribute point data from multiple two-dimensional seismic survey lines. This attribute point data contains spatial coordinate information and stratigraphic parameters, forming the basic data source for near-surface modeling. Based on the spatial distribution characteristics of these attribute point data, the spatial extent of the near-surface model is determined, and the geometric boundary framework of the modeling area is established.

[0009] After determining the spatial extent, the first point data located on the Earth's surface and the second point data located on the top surface of the high-velocity layer are extracted from the 2D seismic survey lines and used as constraint points controlling the upper and lower boundaries of the model, respectively. The first point data is processed using a spatial interpolation algorithm to generate the first 3D boundary surface, and the second point data is processed to generate the second 3D boundary surface. These two surfaces together constitute the upper and lower constraint boundaries of the near-surface model, ensuring the rationality of the model's spatial structure.

[0010] The original attribute point data is then spatially filtered, retaining only valid attribute point data located between the two boundary surfaces to ensure that subsequent modeling uses only valid data that conforms to geological patterns. Based on preset mesh generation parameters and the two boundary surfaces, an irregular 3D mesh model is generated that is regularly divided in the plane and constrained by the surfaces in the vertical direction.

[0011] Finally, a refined near-surface model is established based on the effective attribute point data and the 3D mesh model. By introducing a double-boundary surface constraint mechanism, this invention effectively solves the problem of insufficient modeling accuracy of traditional methods under sparse 2D survey line data conditions, significantly improving the reliability and accuracy of the near-surface model, and providing an accurate geological model basis for seismic exploration work in complex surface areas. Attached Figure Description

[0012] Figure 1 This illustration shows one of the flowcharts of a method for establishing a near-surface model provided by an embodiment of the present invention; Figure 2 A schematic diagram of near-surface attribute points of the control profile is shown; Figure 3 A schematic diagram of the grid division of near-surface attribute points is shown; Figure 4 A schematic diagram is shown showing near-surface attribute points of a certain layer interpolated under the control of the surface and the top surface of the high-speed highway. Figure 5 The map shows the near-surface attribute point data of five two-dimensional seismic survey lines within the work area. Figure 6 A schematic diagram is shown showing the formation of the Earth's surface using surface scatter interpolation of five two-dimensional seismic lines; Figure 7 A schematic diagram is shown of forming the high-speed top surface using high-speed top scatter interpolation of five two-dimensional seismic survey lines; Figure 8 A schematic diagram is shown, which uses the surface and the top surface of the highway as constraints to interpolate near-surface attribute points of two-dimensional seismic survey lines to form a three-dimensional near-surface attribute model; Figure 9 One of the schematic diagrams of 3D model slicing discretization is shown; Figure 10 This is the second schematic diagram of the discretization of a 3D model by slicing; Figure 11 A structural block diagram of the apparatus for establishing a near-surface model provided in an embodiment of the present invention is shown; Figure 12 A schematic block diagram of the structure of an electronic device provided in an embodiment of the present invention is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0014] The following is combined Figures 1 to 12 The method, apparatus, storage medium, and electronic device for establishing near-surface models provided in this invention will be described in detail through specific embodiments.

[0015] like Figure 1 As shown, in some embodiments of the present invention, a method for establishing a near-surface model is proposed, comprising: S102: Obtain attribute point data for multiple two-dimensional seismic survey lines; S104: Obtain the spatial extent of the near-surface model based on attribute point data; S106: Obtain the first point data at the ground surface and the second point data at the top layer for each of the multiple two-dimensional seismic survey lines; S108: Obtain the first three-dimensional boundary surface based on the first point data and the spatial range of the near-surface model; S110: Obtain the second three-dimensional boundary surface based on the second point data and the spatial range of the near-surface model; S112: Obtain valid attribute point data that lies between the first 3D boundary surface and the second 3D boundary surface from the attribute point data; S114: Obtain a three-dimensional mesh model based on the preset meshing parameters, the first three-dimensional boundary surface, and the second three-dimensional boundary surface; S116: Establish a near-surface model based on valid attribute point data and a three-dimensional mesh model.

[0016] In this embodiment, the present invention proposes a method for establishing a near-surface model, which effectively solves the modeling problem caused by the inability to carry out full-area 3D exploration in complex surface areas.

[0017] Specifically, a near-surface model is a three-dimensional geological model, which is divided into near-surface and intermediate-deep layers according to depth. The near-surface layer consists of multiple low-velocity and slowing-velocity layers, with depths ranging from a few meters to about 2000 meters and velocities ranging from several hundred to 2000 m / s. The intermediate-deep layer consists of multiple underground structural layers, with depths ranging from several hundred to tens of thousands of meters and velocities ranging from 1000 to 8000 m / s.

[0018] In this invention, data preparation is first performed by acquiring attribute point data from multiple two-dimensional seismic survey lines. Specifically, as follows: Figure 2 As shown, a two-dimensional seismic survey line refers to an observation line that collects seismic data along a straight line on the Earth's surface. The results of this survey characterize the vertical geological profile information beneath the survey line. Attribute point data is a set of data containing spatial coordinates and at least one stratigraphic property parameter, such as seismic wave propagation velocity v. Each data point is stored in the form of (x, y, z, v).

[0019] These attribute point data belong to near-surface attribute point data. They are datasets collected in near-surface areas using seismic exploration methods, recorded as discrete points, and containing spatial three-dimensional coordinates and stratigraphic physical properties. Specifically, the near-surface attribute point data is obtained through seismic tomography inversion, reflecting the distribution of physical properties within the near-surface medium. The stratigraphic attribute parameter 'v' in the aforementioned attribute point data can characterize the P-wave propagation velocity (Vp) of the strata, and can also be extended to shear wave velocity (Vs), density (ρ), or other parameters according to actual exploration needs.

[0020] After obtaining attribute point data from multiple two-dimensional seismic survey lines, the spatial range of the near-surface model is determined based on the spatial distribution of the attribute point data, that is, the extreme values ​​of the coordinates in the X, Y, and Z directions are obtained, and the cubic space for modeling is defined.

[0021] Next, constraint interfaces were established by extracting first-point data located on the surface and second-point data located on the top surface of the high-velocity layer from each 2D seismic survey line. Specifically, the first-point data represents the surface elevation point, and the second-point data represents the vertex of the high-velocity top interface. Based on the first-point data and the spatial extent of the near-surface model, a first 3D boundary surface was obtained using a spatial interpolation algorithm; based on the second-point data and the spatial extent of the near-surface model, a second 3D boundary surface was obtained. These two 3D boundary surfaces together constitute the upper and lower geometric constraint boundaries of the near-surface geological body.

[0022] Next, data preprocessing is performed to obtain valid attribute point data located between the first and second 3D boundary surfaces. Specifically, spatial filtering is performed on the original attribute point data based on the two constraint surfaces to remove invalid data points located above the ground surface or below the high-speed top, thereby obtaining a valid attribute point dataset and ensuring that all data points used for modeling are located within the geological space enclosed by the two boundary surfaces.

[0023] Subsequently, a three-dimensional mesh model is generated based on the preset meshing parameters and two boundary surfaces. Specifically, a regular mesh framework is first created in the XY plane according to the meshing parameters. Then, for each mesh column, the intersection with the two boundary surfaces is calculated according to its planar position to obtain the top and bottom depths of the column, thereby forming an irregular three-dimensional mesh that conforms to the actual geological structure in the vertical direction.

[0024] Specifically, the preset mesh generation parameters in this invention are modeling parameters set according to subsequent application requirements, specifically referring to the planar mesh spacing dx and dy. Their values ​​directly determine the lateral resolution and computational efficiency of the three-dimensional mesh model. When regional analysis is required, a larger mesh spacing (e.g., dx=40 meters, dy=30 meters) can be used to improve computational efficiency by reducing mesh density. When analyzing fine target volumes, a smaller mesh spacing (e.g., dx=20 meters, dy=20 meters) is used to ensure accurate characterization of complex near-surface structures by increasing mesh density. The parameter settings must simultaneously meet accuracy requirements and constraints of actual computational resources.

[0025] Based on the valid attribute point data and the three-dimensional mesh model, a near-surface model is established, and the valid point data is assigned to the three-dimensional mesh model through an interpolation algorithm, thus completing the establishment of the near-surface model.

[0026] Specifically, the model established in this application is a three-dimensional near-surface velocity model. The modeling depth range is defined as between the surface and the top interface of the high-velocity zone, with a typical depth of 0-2000 meters and a velocity range of 300-2500 meters per second. It mainly includes near-surface stratigraphic units such as the surface weathering layer, low-velocity zone, and deceleration layer. This model differs from the intermediate-deep tectonic model, which typically extends from below the top interface of the high-velocity zone to several kilometers in depth, with a velocity range of 2000-8000 meters per second and includes multiple deep tectonic layers. This invention limits the modeling target to the near-surface area that is significantly affected by surface undulations and weathering layers. By establishing a three-dimensional velocity model within this specific depth range, it provides accurate geological constraints for subsequent seismic data processing.

[0027] The method proposed in this invention effectively controls the spatial morphology of the near-surface model by introducing double-boundary surface constraints, avoiding geological inconsistencies that arise in complex and undulating areas using traditional methods. The established model more closely resembles real geological conditions in both spatial geometry and physical property distribution, significantly improving the accuracy and reliability of the near-surface model. This provides an accurate geological model foundation for seismic exploration in complex areas and overcomes the modeling bottleneck caused by limitations in exploration costs and technical conditions that prevent the implementation of 3D exploration.

[0028] In some embodiments of the present invention, optionally, a first three-dimensional boundary surface is obtained based on the first point data and the spatial range of the near-surface model, specifically including: obtaining the first three-dimensional boundary surface by a spatial interpolation algorithm based on the first point data and the spatial range of the near-surface model.

[0029] In this embodiment, the present invention is based on surface point data extracted from two-dimensional seismic survey lines, combined with the determined spatial range of a three-dimensional model, and uses a spatial interpolation algorithm to calculate and finally generate a surface model that can accurately reflect the undulating shape of the surface, namely the first three-dimensional boundary surface.

[0030] Specifically, the first data point refers to the surface elevation measurement points obtained from two-dimensional seismic survey lines. This first data point is a dataset containing only spatial coordinates (x, y, z), where the z-value represents the surface elevation information. These scattered points collectively define the top surface morphology of the near-surface model and are obtained by interpreting surface reflection waves or direct measurement data using seismic data interpretation software.

[0031] Specifically, the first three-dimensional boundary surface refers to a continuous surface generated by an interpolation algorithm, used to characterize the surface morphology of the work area.

[0032] In this embodiment, based on the first point data and the spatial range of the near-surface model, a first three-dimensional boundary surface is obtained through a spatial interpolation algorithm. Specifically, based on scattered point data of the surface from several two-dimensional control lines, a surface triangulation network is formed within the three-dimensional model range through interpolation. The scattered point data refers to the set of surface elevation points collected along the two-dimensional seismic lines. This invention ensures that even in areas where no control lines pass through, a reasonable surface morphology can be obtained through interpolation.

[0033] In some embodiments of the present invention, optionally, a second three-dimensional boundary surface is obtained based on the second point data and the spatial range of the near-surface model, specifically including: obtaining the second three-dimensional boundary surface by means of a spatial interpolation algorithm based on the second point data and the spatial range of the near-surface model.

[0034] In this embodiment, the present invention establishes the lower boundary of the near-surface model and generates a three-dimensional surface that can accurately reflect the morphology of the high-speed top interface by processing the high-speed top point data.

[0035] Specifically, the second data point refers to the location points of the top surface of the high-velocity layer identified and extracted from the two-dimensional seismic survey lines. This data is also a set containing only spatial coordinates (x, y, z), where the z-value represents the burial depth information of the top interface of the high-velocity layer. These scattered points determine the bottom boundary of the near-surface model and are obtained by tracing and interpreting the reflection phase axes of the top interface of the high-velocity layer in the seismic profile using seismic interpretation software.

[0036] Specifically, the second three-dimensional boundary surface refers to the three-dimensional surface that characterizes the spatial distribution features of the high-speed top interface. This surface serves as the lower boundary of the model and, together with the first three-dimensional boundary surface, defines the vertical range of the near-surface model.

[0037] In this embodiment, the present invention establishes a lower boundary of the model with clear geological significance, so that the modeling range has reasonable geological constraints in the vertical direction. The spatial variation law of the high-speed top interface can be clearly shown through surface modeling, providing an accurate spatial framework for subsequent attribute interpolation and ensuring that the modeling results conform to the actual geological conditions.

[0038] In this embodiment, based on the second point data and the spatial range of the near-surface model, a second three-dimensional boundary surface is obtained through a spatial interpolation algorithm. Specifically, this refers to interpolating the scattered point data of the top layer of the high-speed highway within the three-dimensional model range to form a triangular network of the top layer surface of the high-speed highway. Specifically, the second point data refers to the scattered point data of the top layer of the high-speed highway.

[0039] In some embodiments of the present invention, optionally, obtaining valid attribute point data located between the first three-dimensional boundary surface and the second three-dimensional boundary surface from the attribute point data specifically includes: obtaining the spatial positional relationship between the attribute point data and the first three-dimensional boundary surface and the second three-dimensional boundary surface based on the spatial coordinates of the attribute point data; and obtaining attribute point data with all depth values ​​located between the first three-dimensional boundary surface and the second three-dimensional boundary surface based on the spatial positional relationship, as valid attribute point data.

[0040] In this embodiment, valid attribute point data is filtered. Specifically, firstly, based on the spatial coordinates of the attribute point data, the spatial positional relationship between the attribute point data and the first and second three-dimensional boundary surfaces is obtained. Specifically, the spatial positional relationship refers to the relative positional relationship between the point and the surface determined by spatial geometric calculations, including states such as the point being above, below, or between the two surfaces.

[0041] like Figure 4 As shown, this invention acquires attribute point data with depth values ​​less than or equal to the first three-dimensional boundary surface and greater than or equal to the second three-dimensional boundary surface based on spatial location relationships, and uses these as valid attribute point data. Valid attribute point data refers to the set of attribute points whose spatial location is below the surface surface and above the high-velocity top interface. These data points represent the attribute information within the near-surface strata that truly needs to be modeled. Specifically, Figure 4 The yellow dots in the table represent attribute points of a certain layer in the near-surface table obtained by proportional interpolation.

[0042] In this embodiment, the present invention analyzes the spatial relationship of all attribute points to accurately identify valid data points located between two boundary surfaces, ultimately forming a dataset of valid attribute points that conforms to geological laws. This invention effectively eliminates abnormal data points caused by measurement errors, providing a reliable data foundation for subsequent interpolation calculations and ensuring modeling quality from the outset.

[0043] In some embodiments of the present invention, optionally, the three-dimensional mesh model includes mesh columns, and the mesh columns include multiple layers of vertically arranged mesh units. A near-surface model is established based on valid attribute point data and the three-dimensional mesh model, specifically including: obtaining the maximum number of mesh unit layers in the vertical direction based on the valid attribute point data; obtaining the depth range of each mesh column based on the three-dimensional mesh model; obtaining the attribute value of each mesh unit layer based on the maximum number of mesh unit layers, the depth range of each mesh column, and the valid attribute point data; and establishing a near-surface model based on the three-dimensional mesh model and the attribute values ​​of each mesh unit layer.

[0044] In this embodiment, a near-surface model is established based on the effective attribute point data and the three-dimensional mesh model. First, based on the effective attribute point data, the maximum number of mesh cells in the vertical direction is obtained, that is, the vertical layering structure of the model is determined.

[0045] Next, based on the 3D mesh model, the depth range of each mesh column is obtained, i.e., the spatial range of each mesh column. Based on the maximum number of mesh layers, the depth range of each mesh column, and the effective attribute point data, the attribute values ​​of each mesh layer are obtained. After calculating the attribute values ​​of each mesh layer, all information is integrated, and a near-surface model is established based on the 3D mesh model and the attribute values ​​of each mesh layer.

[0046] Specifically, a grid column refers to a sequence of grid cells arranged vertically in a three-dimensional grid model, with each grid column representing a vertical geological structure at a planar location.

[0047] Specifically, the maximum number of layers refers to the maximum number of vertical layers across all grid columns.

[0048] Specifically, the depth range refers to the vertical distance from the top to the bottom of each grid column, reflecting the total thickness of the near-surface strata at that location.

[0049] In this embodiment, the present invention adopts the maximum number of layers to unify the vertical resolution, ensuring the regularity of the model. Through step-by-step calculation and integration, the complete transformation from sparse two-dimensional data to dense three-dimensional model is realized, providing a reliable near-surface model for subsequent applications.

[0050] Specifically, based on the valid attribute point data, the process of obtaining the maximum number of grid cells in the vertical direction can be achieved by analyzing the maxNz parameter obtained from the number of attribute points in each data column. The depth range can be obtained by calculating the z1 and z2 values ​​of each grid column through ray intersection. A proportional mapping method is used for attribute value calculation.

[0051] In some embodiments of the present invention, optionally, the attribute value of each layer of grid cell is obtained based on the maximum number of layers, the depth range of each grid column, and the effective attribute point data. Specifically, this includes: obtaining the attribute interpolation source data of each layer based on the maximum number of layers and the effective attribute point data; obtaining the depth coordinates of each layer of grid cell in the corresponding grid column based on the depth range of each grid column and the maximum number of layers; and obtaining the attribute value of each layer of grid cell based on the attribute interpolation source data of each layer and the depth coordinates of each layer of grid cell in the corresponding grid column.

[0052] In this embodiment, the present invention completes the assignment of attributes through three levels of operations. Specifically, the present invention first obtains the attribute interpolation source data for each level based on the maximum number of levels and the effective attribute point data.

[0053] Specifically, attribute interpolation source data refers to the set of attribute values ​​prepared for spatial interpolation for each layer, which are extracted from the original attribute points through a scaling mechanism.

[0054] Based on the depth range and maximum number of layers for each grid column, the depth coordinates of each grid cell within its corresponding grid column are obtained. The depth coordinates refer to the vertical position of each grid cell in 3D space, determined through proportional layering calculations. After obtaining the attribute interpolation source data for each layer and the depth coordinates of each grid cell within its corresponding grid column, the attribute values ​​of each grid cell are determined using a spatial interpolation algorithm. Specifically, the spatial interpolation algorithm refers to a mathematical method used to deduce the attribute values ​​of unknown points based on known point attribute values, such as the inverse distance weighted method or hierarchical B-splines.

[0055] In this embodiment, the present invention maintains the vertical variation characteristics of the original data through a layered interpolation mechanism, and the depth coordinate calculation ensures the accurate correspondence between attribute values ​​and spatial locations, thus guaranteeing the spatial continuity and geological rationality of the attribute model and providing reliable attribute parameters for subsequent applications.

[0056] In some embodiments of the present invention, optionally, a three-dimensional mesh model is obtained according to preset mesh division parameters, a first three-dimensional boundary surface, and a second three-dimensional boundary surface. Specifically, this includes: obtaining a three-dimensional mesh frame according to preset mesh division parameters, wherein the three-dimensional mesh frame includes multiple mesh columns; obtaining the top surface depth and bottom surface depth of each mesh column in the three-dimensional mesh frame according to the first three-dimensional boundary surface and the second three-dimensional boundary surface; and obtaining a three-dimensional mesh model according to the top surface depth and bottom surface depth of each mesh column.

[0057] In this embodiment, for the establishment of the three-dimensional mesh model, the present invention first obtains a three-dimensional mesh framework according to preset mesh division parameters, then determines the spatial range of each mesh column based on the boundary surface, and finally forms a mesh model as shown in the figure. Figure 3 The complete 3D mesh model shown is, specifically, Figure 3 The blue dashed lines in the diagram indicate that the model has been meshed.

[0058] Specifically, the top and bottom depths of a grid column refer to the depth values ​​at the intersection points of the central vertical line of each grid column with the surface surface and the high-speed top surface, respectively. These two depth values ​​together define the vertical start and end positions of the grid column. The 3D mesh model is the final grid model with true 3D spatial coordinates, where each grid cell has a defined spatial location and geometric dimensions.

[0059] This invention achieves a balance between standardization and geological adaptability in the grid system by combining regular grids with curved surface constraints. Depth range calculations ensure a high degree of consistency between the grid model and the actual geological interface.

[0060] In some embodiments of the present invention, optionally, attribute point data of multiple two-dimensional seismic survey lines are obtained, specifically including: obtaining sampling points containing spatial coordinates and stratigraphic attributes; and obtaining attribute point data based on the sampling points.

[0061] In this embodiment, for acquiring attribute point data, the present invention first collects the original measurement sampling points, then performs data standardization processing, and finally forms attribute point data that meets the modeling requirements. Through the above-described design for acquiring attribute point data, the present invention ensures the quality and standardization of the basic data for modeling.

[0062] In some embodiments of the present invention, optionally, the spatial range of the near-surface model is obtained based on the attribute point data, specifically including: obtaining coordinate extreme values ​​based on the spatial coordinates in the attribute point data; and obtaining the spatial range of the near-surface model based on the coordinate extreme values.

[0063] In this embodiment, in the process of obtaining the spatial range of the near-surface model based on the attribute point data, the extreme values ​​of the coordinates of all data points are first calculated based on the spatial coordinates in the attribute point data, and then a reasonable modeling spatial range is determined based on these extreme values.

[0064] Specifically, the coordinate extreme values ​​refer to the maximum and minimum coordinate values ​​of all attribute point data in the x, y, and z directions. These six values ​​together define the spatial distribution range of the data points. The spatial range of the near-surface model is the cubic modeling area determined by the coordinate extreme values.

[0065] This invention ensures that the model can fully reflect the geological features of the study area by accurately determining the spatial range. The optimized spatial size not only guarantees the integrity of the modeling but also avoids the waste of computing resources, thereby improving the modeling efficiency.

[0066] In some embodiments of the present invention, a near-surface model is used for forward illumination of seismic data, providing model data for forward illumination.

[0067] In this embodiment, the near-surface model established by the present invention provides accurate velocity field input for the forward illumination of seismic data. The near-surface model contains the three-dimensional spatial coordinates of the near-surface region and the corresponding stratum velocity parameters. The forward illumination process constructs the medium parameter field required for wave equation data simulation by reading the velocity data in the near-surface model, thereby simulating the propagation path and energy distribution characteristics of seismic waves behind the complex near-surface structure. This effectively solves the problem of wave field simulation distortion caused by the inaccuracy of the near-surface model in traditional methods, and significantly improves the reliability of seismic illumination analysis.

[0068] In some embodiments of the present invention, a near-surface model is established based on five two-dimensional seismic survey lines.

[0069] In this embodiment, firstly, as Figure 5 As shown, multiple attribute point data were collected along five two-dimensional seismic survey lines. Each data point contains spatial coordinates in (x, y, z) format and formation velocity information (v). The spatial range of the model was determined by traversing the coordinates of all data points.

[0070] After that, as Figure 6 As shown, surface elevation points are extracted from five survey lines as the first point data to generate the first three-dimensional boundary surface. Figure 7 As shown, the high-speed top interface points are extracted as the second point data to generate the second three-dimensional boundary surface.

[0071] Then, spatial filtering is performed on the original attribute points. By comparing the z-coordinate of each point with the elevation values ​​of the two boundary surfaces at the corresponding locations, outlier points above the ground surface and below the top of the highway are removed to obtain valid attribute point data.

[0072] A three-dimensional mesh framework is established based on the meshing parameters. Ray intersections are performed between each mesh column and two boundary surfaces within the framework to calculate the top surface depth z1 and bottom surface depth z2 of each mesh column, forming a three-dimensional mesh model containing multiple mesh columns. Based on this, five survey line data columns are analyzed to determine the maximum number of vertical layers. Attribute interpolation source data is prepared for each layer through proportional mapping. Each mesh column is proportionally divided according to its depth range and the number of layers. The depth coordinates of each mesh cell are calculated. An inverse distance weighted interpolation algorithm is used to assign the attribute interpolation source data of the corresponding layer to each mesh cell. Finally, as shown... Figure 8 As shown, a near-surface model containing multiple grid cells is generated.

[0073] For example, the near-surface modeling method proposed in this invention can be viewed as follows: First, a box is established, defining the model's length, width, and height (x, y, z) range. Then, the top and bottom are capped, i.e., two 3D surfaces covering the entire work area are generated by interpolation using surface points from 2D seismic survey lines and the top point of the high-speed highway. The area outside the box is cleared, i.e., invalid data points not located between the surface and the top of the high-speed highway are deleted. Next, the layers are divided, and all 2D seismic survey lines are analyzed to determine the maximum number of layers requiring modeling. Then, a mesh is created, dividing the entire box space into a regular 3D mesh. Finally, layer-by-layer filling is performed, from top to bottom, interpolating the attribute values ​​from the 2D seismic survey lines proportionally to the corresponding depth of each mesh column, thereby generating a complete near-surface model.

[0074] Specifically, Figure 9 This model does not include near-surface attribute points; the velocity of the adjacent subsurface layer is constant, which is not accurate enough. Figure 10 It's a model that includes near-surface attribute points; the velocity in the near-surface layer varies both vertically and horizontally, making the model more detailed. For example... Figure 10 As shown, the near-surface model established by this invention is built using five two-dimensional seismic survey lines. In the area between the survey lines, hyperboloid constraints and proportional interpolation are used to maintain geological rationality, which significantly improves the accuracy and reliability of the near-surface model in complex surface areas.

[0075] In some embodiments of the present invention, a method for establishing a near-surface model is proposed, comprising: Step 1: Based on the near-surface attribute point data (x, y, z, v) of several two-dimensional control survey lines, establish the x, y, and z ranges of the three-dimensional model; Step 2: Based on the scattered point data of the ground surface from several two-dimensional control survey lines, interpolate within the three-dimensional model to form a surface triangulation network; Step 3: Based on the scattered data of the top surface of the high-speed railway from several two-dimensional control survey lines, interpolate within the three-dimensional model to form a triangular network of the top surface of the high-speed railway. Step 4: Delete attribute point data with z-coordinate above the ground surface or below the top of the highway; Step 5: Divide the near-surface attribute point data of several two-dimensional control lines into multiple columns according to the x and y coordinates. Sort the data in each column from top to bottom according to the z coordinate, and denote it as ColAttr (column attribute data). Intersect the x and y coordinates of each column and the ray segments that exceed the z range of the three-dimensional model with the surface layer and the top surface of the high-speed roadway of the model to obtain the intersection points z1 and z2 of each column. Calculate the maximum number of attribute points in each column to obtain maxNz (maximum number of vertical layers). Step 6: Divide the 3D model space into nx and ny attribute point grids according to the grid size dx and dy, denoted as GridAttr (grid attribute data). Each GridAttr is used to store attribute points of multiple layers. Step 7: On each attribute point grid to be interpolated, use a ray segment that exceeds the z range of the 3D model to intersect with the surface layer and the top high-speed top layer of the model to obtain the intersection points z1 and z2 from top to bottom; Step 8: Collect interpolated data of 2D seismic survey lines for each layer: Collect interpolated data of attribute points for each layer sequentially, with maxNz as the maximum number of layers. For each ColAttr column, calculate the column ratio s = number of attribute points in the column / maxNz. The set of attribute v for the kth layer = the set of s·k attribute v in each column. Step 9: Interpolate the attribute data for each layer: Using maxNz as the maximum layer number, interpolate the near-surface attribute data in each GridAttr grid of each layer in turn. For the grid in the i-th row and j-th column, dz = (z1-z2) / maxNz for this column, x = i·dx and y = j·dy for the k-th point to be interpolated, and z = z1-k·dz for the attribute point in the k-th layer. The attribute v for the k-th layer is obtained by interpolating x and y using the set of attribute v for the k-th layer using the interpolation algorithm.

[0076] In this embodiment, the first step is the model initialization phase, which determines the spatial boundary of the 3D model by traversing the near-surface attribute point data along all 2D seismic survey lines. Here, the near-surface attribute point data (x, y, z, v) refers to the raw observation data containing spatial coordinates (x, y, z) and the stratigraphic attribute v (usually seismic wave velocity). The x, y, z range of the 3D model refers to the smallest cubic spatial range that can contain all data points, determined by calculating the minimum and maximum values ​​of all data points in the three coordinate directions.

[0077] The second step is to construct the upper boundary surface of the model, which transforms discrete surface measurement points into a continuous surface using spatial interpolation methods. The scattered surface data refers to surface elevation measurement points collected along two-dimensional seismic survey lines, and the surface triangulation network refers to a continuous triangular mesh surface formed by connecting adjacent scattered surface points.

[0078] The third step is to construct the lower boundary surface of the model, using the same technical approach as the second step to build the high-velocity top interface. Specifically, the scattered data of the high-velocity top layer refers to the location points of the high-velocity layer top surface obtained through seismic data interpretation. The high-velocity top surface triangulation is a continuous surface generated by triangulation, representing the top boundary morphology of the high-velocity layer.

[0079] The fourth step is to screen the data for validity, ensuring the geological rationality of the modeling data. By comparing the z-coordinate of each attribute point with the corresponding surface elevation and the elevation of the high-speed top surface, outlier data points located above the surface or below the high-speed top are removed, and valid attribute points located between the two surfaces are retained.

[0080] In the fifth step, vertical grouping and parameter calculation are performed on the original near-surface attribute points. First, the attribute points are grouped into multiple vertical data columns ColAttr according to the planar coordinates, and the data in each column is sorted from top to bottom according to the depth coordinate z. Subsequently, the vertical ray method (without using the deleted invalid points) is used to emit virtual rays for the planar coordinates (x, y) of each column, and intersections are found with the surface triangular mesh and the high-velocity top triangular mesh respectively, obtaining the top surface depth z1 and the bottom surface depth z2 corresponding to this column, thereby determining the actual vertical range of each column. Finally, the maximum value of the number of attribute points in all data columns is statistically calculated, denoted as maxNz, as the global vertical stratification benchmark.

[0081] In the sixth step, a regular three-dimensional grid framework is constructed. Based on the initial three-dimensional model spatial range, that is, the cuboid bounding box containing all two-dimensional survey line attribute points, the model space is divided into nx×ny grids according to the preset planar grid spacings dx and dy, forming the initial three-dimensional grid structure GridAttr. This grid framework only defines the planar positions, and each grid position reserves the capacity to store multi-layer vertical attribute values, providing a carrier for subsequent depth calibration and attribute interpolation under the constrained surface.

[0082] In the seventh step, the depth range of each grid column is calculated. For each grid column in GridAttr constructed in the sixth step, a vertical ray is emitted with its planar center coordinates (x, y), and geometric intersections are made with the surface triangular mesh and the high-velocity top triangular mesh respectively, obtaining the top surface depth z1 and the bottom surface depth z2 of this grid column. This operation combines the regular planar grid with the actual geological interface, ensuring that each grid column conforms to the near-surface true structure vertically, and at the same time providing a spatial benchmark for subsequent calculation of stratification thickness and attribute interpolation.

[0083] In the eighth step, source data for stratified interpolation is prepared through a proportional mapping mechanism. Taking maxNz as the total number of vertical stratifications, the proportional factor s of the number of attribute points in each original data column ColAttr to maxNz is calculated. For the k-th layer (0 ≤ k < maxNz), the attribute values near the s•k-th position are extracted from each ColAttr in depth order, and the attribute values extracted from all data columns are汇集 to form the attribute interpolation source data set for the k-th layer. For example, if the source data has 5 columns, and the number of attribute points in each column is 20, 15, 22, 25, 18 respectively, then maxNz = 25. The ratios of each column are the number divided by 25, which are 0.8, 0.6, 0.88, 1.0, 0.72 in sequence, and the attribute points for each layer from 0 to 24 are taken for interpolation. When k = 10, the attributes {x, y, z, v} of the 8th, 6th, 8.8th, 10th, and 7.2nd points in the source data columns are taken in sequence.

[0084] Step nine completes the construction of the three-dimensional attribute model. For each grid column, the vertical layer thickness dz=(z1-z2) / maxNz is calculated based on its z1, z2, and maxNz, and the vertical depth coordinate z=z1-k•dz of each grid cell is determined. For the k-th layer, the attribute interpolation source data of this layer obtained in step eight is used to calculate the attribute value v at the center point (x=i•dx, y=j•dy) of each grid cell through a spatial interpolation algorithm, finally forming a near-surface model with spatially continuous attribute distribution.

[0085] In this embodiment, the present invention interpolates to form a three-dimensional surface and a top surface of the highway based on near-surface attribute data, surface data, and highway top data from a few two-dimensional control survey lines. Then, under the constraints of the surface and the top of the highway, it interpolates to form near-surface attribute points for the entire work area in a proportional pattern, thus forming a near-surface model.

[0086] like Figure 11 As shown, in some embodiments of the present invention, an apparatus 200 for establishing a near-surface model is proposed, including a first acquisition unit 210, a first processing unit 220, a second acquisition unit 230, a second processing unit 240, a third processing unit 250, a fourth processing unit 260, a fifth processing unit 270 and a sixth processing unit 280. The first acquisition unit 210 is used to acquire attribute point data of multiple two-dimensional seismic survey lines; the first processing unit 220 is used to acquire the spatial range of the near-surface model based on the attribute point data; the second acquisition unit 230 is used to acquire the first point data at the surface and the second point data at the top layer of each two-dimensional seismic survey line; the second processing unit 240 is used to acquire the first three-dimensional boundary surface based on the first point data and the spatial range of the near-surface model; the third processing unit 250 is used to acquire the second three-dimensional boundary surface based on the second point data and the spatial range of the near-surface model; the fourth processing unit 260 is used to acquire the valid attribute point data between the first and second three-dimensional boundary surfaces; the fifth processing unit 270 is used to acquire the three-dimensional mesh model based on preset mesh division parameters, the first and second three-dimensional boundary surfaces; and the sixth processing unit 280 is used to establish the near-surface model based on the valid attribute point data and the three-dimensional mesh model.

[0087] This invention provides a near-surface model building device 200 for constructing a high-precision three-dimensional near-surface model using sparse two-dimensional seismic survey data. This invention establishes a three-dimensional near-surface model for the entire work area through spatially constrained interpolation, providing a reliable geological model foundation for subsequent seismic exploration work.

[0088] The first acquisition unit 210 first acquires attribute point data from multiple two-dimensional seismic survey lines. This data includes spatial coordinate information and stratigraphic parameters, forming the basic data source for modeling. Based on the spatial distribution characteristics of these attribute point data, the first processing unit 220 determines the spatial extent of the near-surface model and establishes the geometric boundary framework of the modeling area.

[0089] After determining the spatial extent, the second acquisition unit 230 extracts first point data located on the Earth's surface and second point data located on the top surface of the high-velocity layer from the two-dimensional seismic survey lines, which serve as constraint points controlling the upper and lower boundaries of the model, respectively. The second processing unit 240 processes the first point data using a spatial interpolation algorithm to generate a first three-dimensional boundary surface, and the third processing unit 250 processes the second point data to generate a second three-dimensional boundary surface. These two surfaces together constitute the upper and lower constraint boundaries of the near-surface model, ensuring the rationality of the model's spatial structure.

[0090] The fourth processing unit 260 then performs spatial filtering on the original attribute point data, retaining valid attribute point data located between the two boundary surfaces to ensure that subsequent modeling uses only valid data that conforms to geological laws. The fifth processing unit 270 generates an irregular three-dimensional mesh model that is regularly divided on the plane and constrained by the surfaces in the vertical direction, based on preset mesh division parameters and the two boundary surfaces.

[0091] The sixth processing unit 280 establishes a refined three-dimensional near-surface model based on effective attribute point data and a three-dimensional mesh model. By introducing a double-boundary surface constraint mechanism, this invention effectively solves the problem of insufficient modeling accuracy under sparse two-dimensional survey line data conditions in traditional methods, significantly improving the reliability of the near-surface model and providing an accurate geological model basis for seismic exploration work in complex surface areas. In some embodiments of this invention, optionally, in the process of obtaining the first three-dimensional boundary surface based on the first point data and the spatial range of the near-surface model, the second processing unit 240 is specifically used to: obtain the first three-dimensional boundary surface based on the first point data and the spatial range of the near-surface model through a spatial interpolation algorithm.

[0092] In some embodiments of the present invention, optionally, during the process of obtaining the second three-dimensional boundary surface based on the second point data and the spatial range of the near-surface model, the third processing unit 250 is specifically used to obtain the second three-dimensional boundary surface based on the second point data and the spatial range of the near-surface model through a spatial interpolation algorithm.

[0093] In some embodiments of the present invention, optionally, during the process of acquiring valid attribute point data located between the first three-dimensional boundary surface and the second three-dimensional boundary surface in the attribute point data, the fourth processing unit 260 is specifically used to: acquire the spatial positional relationship between the attribute point data and the first three-dimensional boundary surface and the second three-dimensional boundary surface according to the spatial coordinates of the attribute point data; and acquire all attribute point data with depth values ​​less than or equal to the first three-dimensional boundary surface and greater than or equal to the second three-dimensional boundary surface as valid attribute point data according to the spatial positional relationship.

[0094] In some embodiments of the present invention, optionally, the three-dimensional mesh model includes mesh columns, and the mesh columns include multiple layers of vertically arranged mesh units. Based on the effective attribute point data and the three-dimensional mesh model, a near-surface model is established. The sixth processing unit 280 is specifically used to: obtain the maximum number of mesh unit layers in the vertical direction based on the effective attribute point data; obtain the depth range of each mesh column based on the three-dimensional mesh model; obtain the attribute value of each mesh unit layer based on the maximum number of mesh unit layers, the depth range of each mesh column, and the effective attribute point data; and establish a near-surface model based on the three-dimensional mesh model and the attribute values ​​of each mesh unit layer.

[0095] In some embodiments of the present invention, optionally, the attribute value of each layer of grid cell is obtained based on the maximum number of layers, the depth range of each grid column, and the effective attribute point data. Specifically, this includes: obtaining the attribute interpolation source data of each layer based on the maximum number of layers and the effective attribute point data; obtaining the depth coordinates of each layer of grid cell in the corresponding grid column based on the depth range of each grid column and the maximum number of layers; and obtaining the attribute value of each layer of grid cell based on the attribute interpolation source data of each layer and the depth coordinates of each layer of grid cell in the corresponding grid column.

[0096] In some embodiments of the present invention, optionally, during the process of obtaining a three-dimensional mesh model according to preset mesh division parameters, a first three-dimensional boundary surface, and a second three-dimensional boundary surface, the fifth processing unit 270 is specifically used to: obtain a three-dimensional mesh frame according to preset mesh division parameters, wherein the three-dimensional mesh frame includes multiple mesh columns; obtain the top surface depth and bottom surface depth of each mesh column in the three-dimensional mesh frame according to the first three-dimensional boundary surface and the second three-dimensional boundary surface; and obtain a three-dimensional mesh model according to the top surface depth and bottom surface depth of each mesh column.

[0097] In some embodiments of the present invention, optionally, in the process of obtaining the number of attribute points of multiple two-dimensional seismic survey lines, the first acquisition unit 210 is specifically used to: acquire sampling points containing spatial coordinates and stratigraphic attributes; and acquire attribute point data based on the sampling points.

[0098] In some embodiments of the present invention, optionally, in the process of obtaining the spatial range of the near-surface model based on the attribute point data, the first processing unit 220 is specifically used to obtain the coordinate extreme values ​​based on the spatial coordinates in the attribute point data; and to obtain the spatial range of the near-surface model based on the coordinate extreme values.

[0099] In an embodiment of the present invention, a storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the steps of the method for establishing a near-surface model as described in any of the above embodiments.

[0100] In this embodiment, the storage medium proposed by the present invention implements the steps of the method for establishing a near-surface model as described in any of the above embodiments when the computer program is executed by the processor. Therefore, it has all the beneficial effects of the method for establishing a near-surface model as described in any of the above embodiments, which will not be repeated here.

[0101] like Figure 12 As shown, in an embodiment of the present invention, an electronic device 300 is proposed, including a memory 310, a processor 320, and a computer program stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program, it implements the steps of the method for establishing a near-surface model as described in any of the above embodiments.

[0102] In this embodiment, the electronic device 300 and processor 320 of the present invention implement the steps of the method for establishing a near-surface model as described in any of the above embodiments when executing a computer program. Therefore, they have all the beneficial effects of the method for establishing a near-surface model as described in any of the above embodiments, which will not be repeated here.

[0103] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for establishing a near-surface model, characterized in that, include: Acquire attribute point data for multiple two-dimensional seismic survey lines; Based on the attribute point data, the spatial extent of the near-surface model is obtained; Acquire the first point data at the ground surface and the second point data at the top layer for each of the multiple two-dimensional seismic survey lines; Based on the first point data and the spatial range of the near-surface model, obtain the first three-dimensional boundary surface; Based on the second point data and the spatial range of the near-surface model, obtain the second three-dimensional boundary surface; Obtain valid attribute point data that lies between the first three-dimensional boundary surface and the second three-dimensional boundary surface from the attribute point data; A three-dimensional mesh model is obtained based on preset mesh division parameters, the first three-dimensional boundary surface, and the second three-dimensional boundary surface; Based on the effective attribute point data and the three-dimensional mesh model, the near-surface model is established.

2. The method for establishing a near-surface model according to claim 1, characterized in that, The step of obtaining the first three-dimensional boundary surface based on the first point data and the spatial range of the near-surface model specifically includes: Based on the first point data and the spatial range of the near-surface model, the first three-dimensional boundary surface is obtained through a spatial interpolation algorithm.

3. The method for establishing a near-surface model according to claim 1, characterized in that, The step of obtaining the second three-dimensional boundary surface based on the second point data and the spatial range of the near-surface model specifically includes: Based on the second point data and the spatial range of the near-surface model, the second three-dimensional boundary surface is obtained through a spatial interpolation algorithm.

4. The method for establishing a near-surface model according to claim 1, characterized in that, The acquisition of valid attribute point data located between the first three-dimensional boundary surface and the second three-dimensional boundary surface specifically includes: Based on the spatial coordinates of the attribute point data, the spatial positional relationship between the attribute point data and the first three-dimensional boundary surface and the second three-dimensional boundary surface is obtained; Based on the spatial relationship, all attribute point data with depth values ​​located between the first three-dimensional boundary surface and the second three-dimensional boundary surface are obtained as the valid attribute point data.

5. The method for establishing a near-surface model according to claim 1, characterized in that, The three-dimensional mesh model includes mesh columns, and each mesh column includes multiple layers of vertically arranged mesh cells. The process of establishing the near-surface model based on the effective attribute point data and the three-dimensional mesh model specifically includes: Based on the valid attribute point data, obtain the maximum number of grid cell layers in the vertical direction; Based on the three-dimensional mesh model, obtain the depth range of each mesh column; Based on the maximum number of layers in the grid cell, the depth range of each grid column, and the effective attribute point data, obtain the attribute value of each layer of the grid cell; The near-surface model is established based on the three-dimensional mesh model and the attribute values ​​of each mesh cell in each layer.

6. The method for establishing a near-surface model according to claim 5, characterized in that, Based on the maximum number of layers, the depth range of each of the grid columns, and the effective attribute point data, the attribute values ​​of each layer of grid cells are obtained, specifically including: Based on the maximum number of layers and the effective attribute point data, obtain the attribute interpolation source data for each layer; Based on the depth range of each grid column and the maximum number of layers, obtain the depth coordinates of each grid cell in the corresponding grid column; The attribute values ​​of each layer's grid cells are obtained based on the attribute interpolation source data for each layer and the depth coordinates of each layer's grid cells in the corresponding grid column.

7. The method for establishing a near-surface model according to claim 1, characterized in that, The step of obtaining a three-dimensional mesh model based on preset mesh division parameters, the first three-dimensional boundary surface, and the second three-dimensional boundary surface specifically includes: A three-dimensional mesh framework is obtained based on the preset mesh division parameters, wherein the three-dimensional mesh framework includes multiple mesh columns; Based on the first three-dimensional boundary surface and the second three-dimensional boundary surface, obtain the top surface depth and bottom surface depth of each mesh column in the three-dimensional mesh frame; The three-dimensional mesh model is obtained based on the top and bottom surface depths of each of the mesh columns.

8. The method for establishing a near-surface model according to claim 1, characterized in that, The acquisition of attribute point data for multiple two-dimensional seismic survey lines specifically includes: Obtain sampling points containing spatial coordinates and stratigraphic attributes; Based on the sampling points, obtain the attribute point data.

9. The method for establishing a near-surface model according to claim 1, characterized in that, The step of obtaining the spatial extent of the near-surface model based on the attribute point data specifically includes: Based on the spatial coordinates in the attribute point data, obtain the coordinate extreme values; The spatial extent of the near-surface model is obtained based on the extreme coordinate values.

10. An apparatus for establishing a near-surface model, characterized in that, include: The first acquisition unit is used to acquire attribute point data of multiple two-dimensional seismic survey lines; The first processing unit is used to obtain the spatial range of the near-surface model based on the attribute point data. The second acquisition unit is used to acquire the first point data at the ground surface and the second point data at the top layer of each of the multiple two-dimensional seismic survey lines. The second processing unit is used to obtain the first three-dimensional boundary surface based on the first point data and the spatial range of the near-surface model; The third processing unit is used to obtain the second three-dimensional boundary surface based on the second point data and the spatial range of the near-surface model; The fourth processing unit is used to acquire valid attribute point data located between the first three-dimensional boundary surface and the second three-dimensional boundary surface from the attribute point data; The fifth processing unit is used to obtain a three-dimensional mesh model based on preset mesh division parameters, the first three-dimensional boundary surface, and the second three-dimensional boundary surface; The sixth processing unit is used to establish the near-surface model based on the effective attribute point data and the three-dimensional mesh model.

11. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for establishing a near-surface model as described in any one of claims 1 to 9.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for establishing a near-surface model as described in any one of claims 1 to 9.

Images