A near-surface modeling method and device based on high-precision remote sensing

Abstract

The application discloses a kind of near-surface modeling method and equipment based on high-precision remote sensing, the method comprises: based on remote sensing data and geological outcrop data extraction surface formation lithology interface and formation occurrence information;Based on surface formation lithology interface and formation occurrence information, establish near-surface geological structure model;Speed information filling is carried out to geological structure model, and surface investigation data is carried out interpolation extrapolation, and near-surface velocity model is established.The application can effectively improve the precision of double complex exploration area shallow surface velocity model, provide an effective solution for solving complex surface exploration area time domain static correction and depth domain imaging velocity modeling problem in seismic data processing.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a near-surface modeling method and equipment based on high-precision remote sensing. Background Technology

[0002] High-precision near-surface velocity modeling is crucial for improving the accuracy of seismic imaging in complex regions. Currently, commonly used near-surface velocity modeling methods in seismic data processing include refraction modeling and tomographic inversion modeling based on first-arrival travel time (FAT), as well as modeling methods based on field surface survey data such as micrologging, small refraction, and shallow reflection methods. FAT tomography, influenced by factors such as the spatial sampling interval of the near-arrival travel time data, the number of coverage iterations, and the model grid size, results in low accuracy for inverting near-surface very shallow velocity, yielding only a vague background velocity. Seismic field surface survey data can provide high-precision surface velocity and stratification information at measurement points, but its spatial distribution is sparse. To build a surface velocity model using this data, interpolation of discrete data is necessary. The accuracy of the interpolation model depends on the number of surface survey points and the interpolation algorithm. While applicable in regions with relatively simple surface structures, in complex regions with drastic changes in surface lithology, using only discrete data for interpolation to build a surface model results in significant errors, exhibiting "bull's-eye" velocity anomalies, severely impacting the accuracy of near-surface modeling. Therefore, establishing a high-precision shallow surface velocity model using surface survey data is of great significance for improving the accuracy of seismic imaging in complex dual-zone areas. Summary of the Invention

[0003] The purpose of this invention is to provide a near-surface modeling method and equipment based on high-precision remote sensing. It uses high-precision remote sensing data to establish a near-surface geological structure model, and performs interpolation and extrapolation on surface survey data under the constraints of the geological model to establish a surface model.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a near-surface modeling method based on high-precision remote sensing, the method comprising the following steps:

[0005] Information on lithological interfaces and stratigraphic attitudes of surface strata was extracted based on remote sensing data and geological outcrop data.

[0006] A near-surface geological structure model is established based on the lithological interface and stratigraphic attitude information of the surface strata.

[0007] Velocity information is filled into the geological structure model, and surface survey data is interpolated and extrapolated to establish a near-surface velocity model.

[0008] Preferably, the extraction of surface lithological interfaces and stratigraphic attitude information includes the following steps:

[0009] The existing surface survey data, near-surface geological maps, digital outcrops and high-precision remote sensing image data are overlaid and displayed to establish the correspondence between field measured geological data, structural geological features, stratigraphic lithology and remote sensing image data, and to establish the calibration relationship between remote sensing and geological interpretation for the whole area.

[0010] More detailed stratigraphic and lithological boundaries can be drawn from remote sensing data on a three-dimensional overlay image.

[0011] The attitude of all strata was determined by combining existing stratigraphic attitudes with remote sensing data.

[0012] Preferably, the establishment of the near-surface geological structure model includes the following steps:

[0013] Based on the lithological interfaces and stratigraphic attitude information of the surface strata, a near-surface geological modeling layer is established, and a near-surface geological structure model is established by sequentially creating a layer model, editing the layer model, and dividing the skeleton mesh.

[0014] Preferably, the establishment of the layer model includes the following steps:

[0015] By using the obtained surface lithological boundary lines and stratigraphic attitude information as stratigraphic lithological boundary lines and spatial discrete points, stratigraphic attitude data are grouped according to stratigraphic lithology, and appropriate spatial resampling is performed on the stratigraphic boundaries to generate a spatial surface stratigraphic bedding model.

[0016] Preferably, the skeleton mesh subdivision includes the following steps:

[0017] Based on the model boundary and the stratigraphic level model, a hybrid grid of rectangular and triangular meshes is used for mesh generation;

[0018] The meshing process involves first dividing the mesh into rectangular meshes, and then dividing it into Delaunay triangular meshes within the rectangular meshes through which the faults pass, ultimately forming a hybrid mesh of rectangular and Delaunay triangular meshes. This hybrid mesh skeleton model of rectangular and triangular meshes allows the mesh to accurately fit the strata and boundaries, and also enables various optimization measures to optimize the mesh morphology, thereby accelerating the storage and computation of mesh data.

[0019] Preferably, the establishment of the near-surface geological structure model includes:

[0020] Based on the skeleton mesh model, a spatial surface mesh interpolation algorithm is used to calculate the interface between the ground surface and the bottom of the model. Based on the intersecting relationship between ground layers and the intersecting relationship between the boundary model and the bottom of the model, stratigraphic blocks are obtained, and a near-surface geological structure model is established.

[0021] Preferably, establishing the near-surface velocity model includes:

[0022] Based on the established near-surface geological structure model and combined with micrologging and small refraction velocity data from field surface surveys, a final velocity model was established.

[0023] Preferably, establishing the final velocity model includes the following steps:

[0024] Vertical grid subdivision;

[0025] Well trajectory discretization;

[0026] Geostatistical velocity modeling.

[0027] Preferably, the vertical grid subdivision includes, based on the construction model, vertically subdividing the construction blocks into equal proportions to generate a grid model, wherein the vertical accuracy of the subdivided grid model is referenced to the stratified interpretation accuracy of the surface survey data.

[0028] Preferably, the well trajectory discretization includes automatically calculating the spatial relationship between the imported micro-logging vertical layer data and the geological model grid based on computational geometry algorithms, and automatically generating a discrete well trajectory grid model;

[0029] The computational geometry algorithm includes calculating the intersection points of the microgauge well trajectory with all grid surfaces, determining whether the line segment between any two intersection points is inside the grid, and determining the relative relationship between the well trajectory and the grid cells accordingly.

[0030] Preferably, the geostatistical velocity modeling includes obtaining data points on the velocity curve based on the intersection relationship between the well trajectory and the grid under the constraints of the geological model grid, and establishing a near-surface velocity model within each structural block using a geostatistical interpolation algorithm.

[0031] This invention also provides a near-surface modeling device based on high-precision remote sensing, comprising an extraction unit, a model unit one, and a model unit two; wherein,

[0032] The extraction unit is used to extract information on the lithological interface and stratigraphic occurrence of the surface strata based on remote sensing data and geological outcrop data;

[0033] The first model unit is used to establish a near-surface geological structure model based on the lithological interface and stratigraphic attitude information of the surface strata.

[0034] The second model unit is used to fill in velocity information into the geological structure model, interpolate and extrapolate surface survey data, and establish a near-surface velocity model.

[0035] Preferably, the extraction unit for extracting surface lithological interfaces and stratigraphic attitude information includes the following steps:

[0036] The existing surface survey data, near-surface geological maps, digital outcrops and high-precision remote sensing image data are overlaid and displayed to establish the correspondence between field measured geological data, structural geological features, stratigraphic lithology and remote sensing image data, and to establish the calibration relationship between remote sensing and geological interpretation for the whole area.

[0037] More detailed stratigraphic and lithological boundaries can be drawn from remote sensing data on a three-dimensional overlay image.

[0038] The attitude of all strata was determined by combining existing stratigraphic attitudes with remote sensing data.

[0039] Preferably, the establishment of a near-surface geological structure model by model unit one includes the following steps:

[0040] Based on the lithological interfaces and stratigraphic attitude information of the surface strata, a near-surface geological modeling layer is established, and a near-surface geological structure model is established by sequentially creating a layer model, editing the layer model, and dividing the skeleton mesh.

[0041] Preferably, the establishment of the final velocity model by model unit two includes the following steps:

[0042] Vertical grid subdivision;

[0043] Well trajectory discretization;

[0044] Geostatistical velocity modeling.

[0045] The technical effects and advantages of this invention are as follows:

[0046] This invention proposes a near-surface modeling method and equipment based on high-precision remote sensing and surface geological data. It utilizes high-precision remote sensing data to establish a near-surface geological structure model, and then interpolates and extrapolates surface survey data under the constraints of this geological model to establish a surface model. The proposed high-precision shallow surface velocity modeling method fully leverages the high vertical velocity accuracy of surface survey data, and the accurate characterization of surface lithology, stratigraphic attitude, and other spatial variation features of surface structures by combining high-precision remote sensing data, surface geological survey data, and surface digital outcrops. This organic combination of the two types of data achieves near-surface velocity modeling through multi-information fusion, effectively improving the accuracy of shallow surface velocity models in complex exploration areas. It provides an effective technical solution for addressing the problems of time-domain static correction and depth-domain imaging velocity modeling in complex surface exploration areas during seismic data processing.

[0047] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0048] Figure 1 This is a flowchart of the near-surface modeling method based on high-precision remote sensing of the present invention;

[0049] Figure 2 This is a stratigraphic boundary and attitude formation prism diagram of the present invention;

[0050] Figure 3 This is a diagram illustrating the hybrid mesh optimization process of rectangular mesh and Delaunay triangular mesh according to the present invention.

[0051] Figure 4 This is a diagram illustrating the mesh partitioning effect of the skeleton of the present invention.

[0052] Figure 5 This is a vertical grid subdivision diagram of the present invention;

[0053] Figure 6 This is a schematic diagram of well trajectory discretization according to the present invention;

[0054] Figure 7 This is a flowchart illustrating the near-surface velocity modeling process of the present invention. Detailed Implementation

[0055] To address the shortcomings of existing technologies, this invention discloses a near-surface modeling method based on high-precision remote sensing, such as... Figure 1 As shown, the method includes the following steps:

[0056] Information on lithological interfaces and stratigraphic attitudes of surface strata was extracted based on remote sensing data and geological outcrop data.

[0057] A near-surface geological structure model is established based on the lithological interface and stratigraphic attitude information of the surface strata.

[0058] Velocity information is filled into the geological structure model, and surface survey data is interpolated and extrapolated to establish a near-surface velocity model.

[0059] The following is a more detailed explanation with reference to the accompanying drawings.

[0060] Step 1: Extraction of surface lithological interfaces and stratigraphic attitude information;

[0061] By utilizing high-precision remote sensing data, surface geological survey data, and surface digital outcrops, information on surface lithological boundaries and stratigraphic attitudes was obtained. First, 3D visualization technology was used to overlay existing surface geological outcrop data, geographic information data, elevation information, and other relevant measurement data with high-precision remote sensing imagery to establish a correspondence between the known geological features of stratigraphy, lithology, and structural characteristics from field geological data and remote sensing imagery, thus establishing a calibration relationship between remote sensing and geological interpretation for the entire area. Second, using a human-computer interaction method, more refined stratigraphic and lithological boundaries were drawn from remote sensing data on the 3D overlay graphics. Finally, by combining existing stratigraphic attitudes from near-surface geological maps and seismic field reconnaissance data with remote sensing data, the attitudes of all strata were ultimately determined.

[0062] Step 2: Establishment of near-surface geological model;

[0063] Using the near-surface lithological boundary lines and their spatial discrete occurrence information obtained in step one, a near-surface geological modeling layer is established. Through steps such as grid subdivision and surface depth interpolation, a three-dimensional geological model is generated. The main steps include establishing the layer model, skeleton grid subdivision, and establishing the near-surface geological model.

[0064] Layer model establishment: Utilizing the near-surface geological information obtained in step one as stratigraphic lithology boundaries and spatial discrete point stratigraphic attitude data, the stratigraphic layers are grouped according to lithology. Using the strike-dip method, given the transverse prism distances representing the surface and the number of longitudinal points of the prisms, the stratigraphic boundaries are appropriately resampled spatially to generate spatial prisms. This generates spatial prism faces representing the spatial stratigraphic interface. Specifically, as follows... Figure 2 As shown, the stratigraphic boundary is discretized according to a certain spatial sampling interval, and the stratigraphic attitude information of each discrete point is calculated. In this way, a stratigraphic prism with the same stratigraphic attitude can be generated for each spatial discrete point. Spatial stratigraphic prism interpolation is used to establish the spatial stratigraphic plane.

[0065] Skeleton mesh generation: Based on the model boundaries and stratigraphic levels, a hybrid mesh of rectangular and triangular meshes is used for mesh generation. Specifically, the mesh is first generated into rectangular meshes, and then Delaunay triangular meshes are generated within the rectangular meshes traversed by faults, ultimately forming a hybrid mesh of rectangular and Delaunay triangular meshes.

[0066] In this embodiment, reference is made to... Figure 3 As shown, the line segments in the figure represent strata or fault planes. The rectangular grids through which strata or fault planes pass are re-subdivided using triangular meshes and optimized to ensure that the triangular meshes align with the actual strata strike. A hybrid mesh skeleton model using rectangular and triangular meshes allows the mesh to accurately fit strata and boundaries while also employing various optimization techniques to refine the mesh morphology, thus accelerating the storage and computation of mesh data.

[0067] In this embodiment, as Figure 4 The image shows the result of the skeleton mesh subdivision. The mesh in the image is subdivided into Delaunay triangular meshes.

[0068] Establishing a near-surface geological model: Based on the skeleton grid model, a spatial surface grid interpolation algorithm is used to calculate the interface between the ground surface and the bottom of the model. Based on the intersection relationship between ground layers and the intersection relationship between the boundary model and the bottom of the model, stratigraphic blocks are obtained, and a near-surface geological model is established.

[0069] Step 3: Fill the geological structure model with velocity information, interpolate and extrapolate the surface survey data, and establish a near-surface velocity model.

[0070] Based on the near-surface geological model established in step two, and combined with velocity data from micrologging and small refraction measurements from the field surface investigation, the final velocity model can be established. This model mainly includes vertical grid subdivision, well trajectory discretization, and geostatistical velocity modeling.

[0071] I. Vertical Grid Subdivision: Based on the constructed model, the constructed blocks are subdivided vertically in equal proportions to generate a grid model. The vertical accuracy of the subdivided grid model is referenced to the vertical accuracy of the well's attribute data. Specifically, as follows... Figure 5 As shown, the geological model has a blocky structure in the vertical direction. Before establishing the velocity model, the blocky model needs to be subdivided vertically to form a fine vertical grid model, which provides a foundation for subsequent velocity modeling.

[0072] II. Well Trajectory Discretization: Based on computational geometry algorithms, the spatial relationship between imported microlog longitudinal stratification data and the geological model grid is automatically calculated, and a discrete well trajectory grid model is automatically generated. Surface survey microlog or small refraction interpretation results are vertically sparse points, such as... Figure 6 As shown in the figure, the well trajectory discretization is represented by line segments of various colors. This involves re-discretizing the surface survey interpretation data vertically according to the geological model's vertical grid, ensuring that each surface survey point has a velocity value in each vertical grid cell. The computational geometry algorithm primarily includes: first, calculating the intersection points of the microgauge well trajectory with all grid surfaces; then, determining whether the line segment between any two intersection points lies within the grid, and based on this, determining the relative relationship between the well trajectory and the grid cells. The main steps are as follows:

[0073] (1) The well trajectory is abstracted as a spatial polyline, called the well trajectory polyline L. The points on the polyline are marked as Li, and each point includes Cartesian space coordinates (X, Y, Z).

[0074] (2) Calculate the three-dimensional bounding box BOXij of any line segment (Li, Lj) on the well trajectory polyline.

[0075] (3) Calculate the three-dimensional bounding box BOXci of each grid cell (Ci) based on the geometric model of the grid.

[0076] (4) If the outer bounding boxes BOXij and BOXci do not intersect, then the line segments Li, Lj and Ci will not intersect, and no further calculation will be performed. If they intersect, continue to calculate the intersection point P1 of the mesh cell Ci and the line segments Li, Lj. Each point is marked as Pi, and each point includes spatial coordinates (X, Y, Z).

[0077] (5) Insert the two endpoints of the well trajectory broken line L at the beginning and end of the above intersection point to form the point sequence L1, P1, P2...Ln.

[0078] (6) Calculate the midpoint (Pi+Pj) / 2.0 of any two adjacent points (Pi, Pj) in the above point sequence, and denote it as Pij.

[0079] (7) Determine whether Pij is in grid cell Ci. If Pij is in Ci, then Ci is a cell that is crossed by the well trajectory polyline L, and the well trajectory line from Pi to Pj (which can contain multiple segments) must be in grid cell Ci.

[0080] (8) Calculate the positional relationship between the well trajectory polyline and all grid cells in sequence to obtain all grids that are crossed by the well trajectory polyline.

[0081] III. Geostatistical Velocity Modeling: Under the constraints of the geological model grid, data points on the velocity curve are obtained based on the intersection relationship between the well trajectory and the grid. Within each structural block, a near-surface velocity model can be established using a geostatistical interpolation algorithm.

[0082] In this implementation, the near-surface velocity modeling process is as follows: Figure 7 As shown, surface lithological boundaries and stratigraphic attitude information were obtained using surface geological maps, high-precision remote sensing data, non-seismic data, and near-surface outcrop data. Based on the surface stratigraphic lithological boundaries and stratigraphic attitude information, near-surface geological modeling layers were established, and a near-surface geological model was built sequentially through layer model creation, layer model editing, and skeleton mesh generation. Velocity information was filled into the near-surface geological model. Based on the structural model, structural blocks were vertically subdivided at equal scales to generate a framework model. The vertical accuracy of the subdivided framework model referenced the vertical accuracy of the well attribute data. Well trajectory discretization was used to re-discrete the surface survey interpretation data vertically according to the geological model's vertical grid, ensuring that each surface survey point had a velocity value in each vertical grid. Geostatistical interpolation algorithms were used to interpolate the stratigraphic structural unit data, performing interpolation and extrapolation of the surface survey data to establish a near-surface velocity model.

[0083] This invention also provides a near-surface modeling device based on high-precision remote sensing, the device comprising an extraction unit, a model unit one, and a model unit two; wherein,

[0084] The extraction unit is used to extract information on the lithological interface and stratigraphic occurrence of the surface strata based on remote sensing data and geological outcrop data;

[0085] The first model unit is used to establish a near-surface geological structure model based on the lithological interface and stratigraphic attitude information of the surface strata.

[0086] The second model unit is used to fill in velocity information into the geological structure model, interpolate and extrapolate surface survey data, and establish a near-surface velocity model.

[0087] Specifically, the surface survey data includes surface geological maps, high-precision remote sensing data, non-seismic data, and near-surface outcrop data;

[0088] Specifically, the extraction unit is used to extract surface lithological interfaces and stratigraphic attitude information, including the following steps:

[0089] The existing surface digital outcrop data, geographic information data, elevation information and related measurement data in the work area are overlaid and displayed with high-precision remote sensing image data to establish the correspondence between the stratigraphic lithology and structural characteristics of the known geological features of the field measured geological data and the remote sensing image data, and to establish the calibration relationship between remote sensing and geological interpretation of the whole area.

[0090] More detailed stratigraphic and lithological boundaries can be drawn from remote sensing data on a three-dimensional overlay image.

[0091] The attitude of all strata was determined by combining existing stratigraphic attitudes with remote sensing data.

[0092] Specifically, the establishment of a near-surface geological structure model by model unit one includes the following steps:

[0093] Based on the lithological interfaces and stratigraphic attitude information of the surface strata, a near-surface geological modeling layer is established, and a near-surface geological structure model is established by sequentially creating a layer model, editing the layer model, and dividing the skeleton mesh.

[0094] Specifically, the model unit two establishes the near-surface velocity model including:

[0095] Based on the established near-surface geological structure model and combined with micrologging and small refraction velocity data from field surface surveys, a final velocity model was established.

[0096] Specifically, the establishment of the final velocity model by model unit two includes the following steps:

[0097] Vertical grid subdivision;

[0098] Well trajectory discretization;

[0099] Geostatistical velocity modeling.

[0100] The vertical grid subdivision involves subdividing the structural blocks vertically at equal ratios based on the structural model to generate a grid model. The vertical accuracy of the subdivided grid model is referenced to the vertical accuracy of the well's attribute data.

[0101] The well trajectory discretization includes automatically calculating the spatial relationship between the imported micro-logging vertical layer data and the geological model grid based on computational geometry algorithms, and automatically generating a discrete well trajectory grid model.

[0102] The computational geometry algorithm includes calculating the intersection points of the microgauge well trajectory with all grid surfaces, determining whether the line segment between any two intersection points is inside the grid, and determining the relative relationship between the well trajectory and the grid cells accordingly.

[0103] The geostatistical velocity modeling includes obtaining data points on the velocity curve based on the intersection relationship between the well trajectory and the grid under the constraints of the geological model grid, and establishing a near-surface velocity model within each structural block using a geostatistical interpolation algorithm.

[0104] In summary, this invention proposes a near-surface modeling method and device based on high-precision remote sensing. It establishes a near-surface geological structure model using high-precision remote sensing data, and establishes a surface model by interpolating and extrapolating surface survey data under the constraints of the geological model. This achieves near-surface velocity modeling by fusing multiple information sources, which can effectively improve the accuracy of shallow surface velocity models in complex exploration areas.

[0105] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A near-surface modeling method based on high-precision remote sensing, characterized in that, The method includes the following steps: Based on remote sensing data and geological outcrop data, information on the lithological boundaries and attitudes of surface strata is extracted. This includes: using 3D visualization technology to overlay existing surface geological outcrop data, geographic information data, elevation information data, and high-precision remote sensing image data; establishing the correspondence between the known geological features of strata lithology and structural characteristics in field-measured geological data and remote sensing image data; establishing the calibration relationship between remote sensing and geological interpretation for the entire region; using human-computer interaction methods to delineate lithological boundaries from remote sensing data on the 3D overlay graphics; and finally determining the attitude of all strata by combining existing strata attitudes with near-surface geological maps, seismic field reconnaissance data, and remote sensing data. Based on the lithological interfaces and stratigraphic attitude information of the surface strata, a near-surface geological structure model is established. The establishment of the near-surface geological structure model includes the following steps: based on the lithological interfaces and stratigraphic attitude information of the surface strata, a near-surface geological modeling layer is established, and the near-surface geological structure model is established sequentially through the establishment of the layer model, layer model editing, and skeleton mesh generation. The skeleton mesh generation includes the following steps: according to the model boundary and the stratigraphic layer model, a hybrid mesh of rectangular and triangular meshes is used for mesh generation; the mesh generation includes first dividing into rectangular meshes, then dividing into Delaunay triangular meshes within the rectangular meshes traversed by faults, ultimately forming a hybrid mesh of rectangular and Delaunay triangular meshes. Velocity information is filled into the geological structure model. Based on the geological structure model, the structural blocks are subdivided vertically in equal proportions to generate a grid model. The vertical accuracy of the subdivided grid model is referenced to the vertical accuracy of the attribute data of the well. Well trajectory discretization is used to re-discrete the surface survey data vertically according to the vertical grid of the geological structure model, so that each surface survey point has a velocity value in each vertical grid. Geostatistical interpolation algorithm is used to interpolate the stratigraphic structural unit data, and the surface survey data is interpolated and extrapolated to establish a near-surface velocity model.

2. The near-surface modeling method based on high-precision remote sensing according to claim 1, characterized in that, The establishment of the stratigraphic layer model includes the following steps: using the stratigraphic attitude data of the obtained surface lithological boundary line and stratigraphic attitude information as the stratigraphic lithological boundary line and spatial discrete points, grouping according to stratigraphic lithology, performing appropriate spatial resampling on the stratigraphic boundary line, and generating a spatial surface stratigraphic layer model.

3. The near-surface modeling method based on high-precision remote sensing according to claim 1, characterized in that, The establishment of the near-surface geological structure model includes: based on the skeleton grid model, using a spatial surface grid interpolation algorithm to calculate the interface between the ground surface and the bottom of the model, and obtaining stratigraphic blocks based on the intersection relationship between ground layers and the intersection relationship between the boundary model and the bottom of the model, thus establishing the near-surface geological structure model.

4. A near-surface modeling method based on high-precision remote sensing according to claim 1 or 3, characterized in that, The establishment of the near-surface velocity model includes: Based on the established near-surface geological structure model and combined with micrologging and small refraction velocity data from field surface surveys, a final velocity model was established.

5. The near-surface modeling method based on high-precision remote sensing according to claim 1, characterized in that, The well trajectory discretization includes automatically calculating the spatial relationship between the imported microlobe vertical layer data and the geological model grid based on a computational geometry algorithm, and automatically generating a discrete well trajectory grid model; wherein, the computational geometry algorithm includes calculating the intersection points of the microlobe well trajectory with all grid surfaces, determining whether the line segment between any two intersection points is inside the grid, and determining the relative relationship between the well trajectory and the grid cells accordingly.

6. The near-surface modeling method based on high-precision remote sensing according to claim 1, characterized in that, The geostatistical velocity modeling includes obtaining data points on the velocity curve based on the intersection relationship between the well trajectory and the grid under the constraints of the geological model grid, and establishing a near-surface velocity model within each structural block using a geostatistical interpolation algorithm.

7. A near-surface modeling device based on high-precision remote sensing, characterized in that, It includes an extraction unit, model unit one, and model unit two; among which, The extraction unit is used to extract surface lithological boundaries and stratigraphic attitude information based on remote sensing data and geological outcrop data. This extraction includes: using 3D visualization technology to overlay existing surface geological outcrop data, geographic information data, elevation information data, and high-precision remote sensing image data; establishing a correspondence between known geological features of stratigraphy and lithology from field geological data and remote sensing image data; establishing a calibration relationship between remote sensing and geological interpretation for the entire region; using a human-computer interaction method to delineate stratigraphic lithological boundaries from remote sensing data on a 3D overlay graphic; and finally determining the attitude of all strata by combining existing stratigraphic attitudes from near-surface geological maps and seismic field reconnaissance data with remote sensing data. The first model unit is used to establish a near-surface geological structure model based on the lithological interface and stratigraphic attitude information of the surface strata. The model unit one is specifically used for: Based on the lithological interfaces and stratigraphic attitude information of the surface strata, a near-surface geological modeling layer is established, and a near-surface geological structural model is established sequentially through the creation of the layer model, layer model editing, and skeleton mesh generation. The skeleton mesh generation includes the following steps: according to the model boundary and the stratigraphic layer model, a hybrid mesh of rectangular and triangular meshes is used for mesh generation; the mesh generation includes first dividing into rectangular meshes, and then dividing into Delaunay triangular meshes within the rectangular meshes traversed by faults, ultimately forming a hybrid mesh of rectangular and Delaunay triangular meshes. Model unit two is used to fill the geological structure model with velocity information. Based on the geological structure model, the structural blocks are subdivided vertically in equal proportions to generate a grid model. The vertical accuracy of the subdivided grid model is referenced to the vertical accuracy of the attribute data of the well. The surface survey interpretation data is re-discrete vertically according to the vertical grid of the geological structure model using well trajectory discretization, so that each surface survey point has a velocity value in each vertical grid. Geostatistical interpolation algorithm is used to interpolate the stratigraphic structural unit data, and the surface survey data is interpolated and extrapolated to establish a near-surface velocity model.

8. A near-surface modeling device based on high-precision remote sensing according to claim 7, characterized in that, The model unit two establishes a near-surface velocity model including: Based on the established near-surface geological structure model and combined with micrologging and small refraction velocity data from field surface surveys, a final velocity model was established.

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