A method for automatically generating a geological envelope

Abstract

The present application provides a kind of geological envelope automatic generation method, comprising: defining target work area;The top interface and the bottom interface of the geological envelope in the defined target work area are sparse horizon picking to determine the horizon of top interface, bottom interface and corresponding top interface and bottom interface data;Based on the grid position corresponding to all picking points of top interface horizon and bottom interface horizon, according to the range of top interface data and bottom interface data, the boundary line of geological envelope at top interface and bottom interface in target work area is determined;Determine the horizon depth value in all grid points of top interface and bottom interface;According to horizon depth value, based on the boundary line of geological envelope at top interface and bottom interface, top interface boundary constraint and bottom interface boundary constraint are constructed to determine the intersection between top interface and bottom interface of envelope, to optimize the boundary line of geological envelope at top interface and bottom interface;Based on the optimized boundary line, generate geological envelope three-dimensional visualization model.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of mineral resource exploration and the field of data interpretation, and particularly relates to a method for automatically generating a geological envelope. BACKGROUND

[0002] In the field of geological exploration, accurate identification and positioning of a geological envelope is of great significance to mineral resource exploration, geological disaster assessment and the like. In mineral resource exploration, the enrichment of many metal minerals is closely related to specific geological envelopes, and accurate understanding of the boundaries and ranges thereof can effectively reduce the exploration range and improve the exploration efficiency. In terms of geological disaster assessment, the distribution and structure of a geological envelope can affect the stability of a stratum, and in-depth study of the characteristics thereof can help accurately assess the risk of disasters such as landslides and debris flows, and provide a basis for the development of prevention measures.

[0003] At present, the determination of a geological envelope in seismic exploration is mainly achieved by manually identifying characteristic seismic waves on a seismic profile and manually completing the horizon interpretation of the top and bottom interfaces. This process relies on geologists to pick up the top and bottom interface horizon data of the geological envelope on the seismic profile point by point according to experience, in combination with the reflection amplitude, phase and frequency of the seismic waves. For the sparse horizon data picked up, a simple interpolation or extrapolation method is usually used for processing to construct the preliminary shape of the geological envelope.

[0004] However, the prior art has obvious deficiencies. Due to factors such as complex geological structure, large lateral variation of lithology and low signal-to-noise ratio of seismic data, the manually picked top and bottom interface horizons often do not intersect, are parallel or intersect in reverse at the boundaries. In order to make the boundaries show an intersecting trend, manual point-by-point modification is required, which not only consumes a large amount of manpower and time, has low work efficiency, and is affected by subjective factors, but also has poor consistency of the results of different personnel, and it is difficult to ensure the accuracy of the shape of the geological envelope, which seriously restricts the reliability of subsequent geological analysis and decision-making. SUMMARY

[0005] The main purpose of the present application is to provide a method for automatically generating a geological envelope to overcome or alleviate the above problems in the prior art.

[0006] The present application provides a method for automatically generating a geological envelope, comprising:

[0007] Step 1, defining a target work area;

[0008] Step 2, picking up sparse horizons of the top and bottom interfaces of the geological envelope in the defined target work area to determine the top interface horizon, the bottom interface horizon and the corresponding top interface and bottom interface data;

[0009] Step 3, based on the grid positions corresponding to all the pickup points of the top interface horizon and the bottom interface horizon, the boundary lines of the geological envelope at the top interface and the bottom interface in the target work area are determined according to the range of the top interface and the bottom interface data;

[0010] Step 4, the top interface and the bottom interface data are continuous, so as to determine the horizon depth value of all the grid points in the top interface and the bottom interface;

[0011] Step 5, according to the horizon depth value, based on the boundary lines of the geological envelope at the top interface and the bottom interface, the top interface boundary constraint and the bottom interface boundary constraint are constructed;

[0012] Step 6, based on the constructed top interface boundary constraint and bottom interface boundary constraint, the intersection between the top interface and the bottom interface of the envelope is determined to optimize the boundary lines of the geological envelope at the top interface and the bottom interface;

[0013] Step 7, based on the optimized boundary lines, the target work area is cropped and closed to generate a three-dimensional visualization model of the geological envelope.

[0014] (Three) beneficial effects

[0015] 1. greatly reduce the labor cost and time consumption, and significantly improve the work efficiency

[0016] In the prior art, due to the complexity of geological structure and other factors, the top and bottom interface horizons picked up manually are prone to problems such as non-intersection, parallel or reverse intersection at the boundary, which need to be modified point by point by manual intervention, consuming a lot of manpower and time, and the work efficiency is low. The present application reduces the initial amount of manual operation through sparse horizon picking, and then automatically determines the boundary line, constructs the boundary constraint, optimizes the boundary line, and crops and closes the whole process, directly replacing the traditional manual point-by-point modification work, without manual intervention in the boundary correction process, effectively avoiding the consumption of a large amount of manpower and time, and greatly improving the work efficiency of the generation of the geological envelope.

[0017] 2. Avoid subjective factors interference, improve the consistency of the processing results

[0018] The prior art relies on the experience of geologists to manually pick up horizons and modify boundaries, which is affected by subjective judgment, and the consistency of the processing results of different personnel is poor. In the technical scheme of the present application, from defining the target work area based on seismic data, to picking up horizons, determining boundary lines, to subsequent construction of constraints, optimization of boundaries and generation of models, all are completed through unified algorithm logic and preset constraint conditions, and are not disturbed by personal experience and subjective judgment throughout the process. Even if different personnel use this method to process the same seismic data, they can also get convergent results, effectively solving the problem of poor consistency of the results in the prior art, and ensuring the objectivity of the processing process.

[0019] 3. Solve the problem of boundary layer position anomaly, and improve the accuracy of geological envelope shape and the reliability of subsequent decision-making

[0020] In the prior art, the top and bottom interface positions picked up manually often have abnormalities such as non-intersection, parallel or reverse intersection at the boundary, and simple interpolation / extrapolation cannot correct them, resulting in insufficient accuracy of the shape of the geological envelope and restricting the reliability of subsequent geological analysis and decision-making. The present application solves this problem through multiple steps in a coordinated and targeted manner: determining the boundary line at the top and bottom interface, and clearly defining the initial boundary range to reduce abnormalities caused by unordered extrapolation; step 5 constructs a boundary constraint to standardize the data processing logic inside and outside the boundary to avoid layer position deviation; step 6 optimizes the boundary line by determining the intersection point of the top and bottom interfaces to directly correct non-intersection, parallel or reverse intersection; and the final step 7 clips and encloses based on the optimized boundary line to ensure that the top and bottom interfaces accurately intersect at the boundary and form a complete closed shape. This series of operations enables the generated geological envelope to more realistically reflect the actual geological structure, providing a reliable data basis for subsequent mineral resource exploration to narrow the scope and accurate risk identification for geological disaster assessment, thereby improving the reliability of subsequent geological analysis and decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A flowchart of a geological envelope automatic generation method according to an embodiment of the present application.

[0022] Figure 2 A schematic diagram of defining a target work area according to an embodiment of the present application.

[0023] Figure 3 A longitudinal angle diagram of the top interface and the bottom interface obtained after step 2 in a scene.

[0024] Figure 4 A horizontal angle diagram of the top interface and the bottom interface obtained after step 2 in a scene.

[0025] Figure 5 A boundary line plane diagram obtained after step 3 in a specific scene.

[0026] Figure 6 A plane diagram of valid data and invalid data of the top interface and the bottom interface in an application scenario.

[0027] Figure 7 A three-dimensional diagram of valid data and invalid data of the top interface and the bottom interface in an application scenario.

[0028] Figure 8 A three-dimensional diagram of the top interface and the bottom interface in an application scenario.

[0029] Figure 9A schematic diagram of the boundary line of the address envelope after step 6 in a scene.

[0030] Figure 10 A three-dimensional visualization model of the geological envelope obtained after step 7 in an application scenario. DETAILED DESCRIPTION

[0031] Figure 1 A flowchart of a method for automatically generating a geological envelope according to an embodiment of the present application. As shown in the figure, the method includes the following steps: Figure 1

[0032] Step 1, defining a target work area;

[0033] Step 2, picking up sparse horizons of the top and bottom interfaces of the geological envelope in the defined target work area to determine the horizons of the top and bottom interfaces and the corresponding data of the top and bottom interfaces;

[0034] Step 3, based on the grid positions corresponding to all the picked points of the horizons of the top and bottom interfaces, determining the boundary lines of the geological envelope at the top and bottom interfaces in the target work area according to the range of the data of the top and bottom interfaces;

[0035] Step 4, continuously processing the data of the top and bottom interfaces to determine the horizon depth values of all the grid points in the top and bottom interfaces;

[0036] Step 5, based on the boundary lines of the geological envelope at the top and bottom interfaces, constructing top and bottom interface boundary constraints according to the horizon depth values;

[0037] Step 6, determining the intersection points between the top and bottom interfaces of the envelope based on the constructed top and bottom interface boundary constraints to optimize the boundary lines of the geological envelope at the top and bottom interfaces;

[0038] Step 7, based on the optimized boundary lines, performing clipping and closing processing on the target work area to generate a three-dimensional visualization model of the geological envelope.

[0039] Figure 2 A schematic diagram of defining a target work area according to an embodiment of the present application. In combination with Figure 2 , the exemplary implementation of step 1 is described as follows.

[0040] Step 1, defining a target work area, specifically including:

[0041] Step 11, extracting spatial position information from the seismic data of the target work area to determine the coordinates of four points of the target work area, which are used to describe the geographical spatial range of the target work area;

[0042] ​Step 12, a rectangular region is determined based on the four-point coordinates, and the rectangular region is subjected to plane gridding processing in combination with the defined cell size to cut the rectangular region into a plurality of regular cells to generate a grid plane in which the target work area is located;

[0043] Step 13, based on the position of the cell in the grid plane in which the target work area is located, the coordinate position of the cell is defined and a unique identification number is assigned to the cell to process, to generate cell information with coordinates and identification, the coordinate position of the cell takes the lower left corner of the rectangular region as the starting corner point (x0, y0), and the coordinate of the i-th row and j-th column of the cell is , Δx and Δy correspond to the length and width of the cell size respectively;

[0044] Step 14, the minimum depth is determined according to the horizontal plane of the highest point of the ground elevation of the target work area, and the maximum depth is determined according to the depth range of the exploration target to obtain the depth data of the target work area;

[0045] Step 15, the target work area is defined based on the cell information with coordinates and identification numbers and the depth data of the target work area.

[0046] Referring to Figure 2 , preferably, in the specific technical implementation of step 11: taking the seismic data of the target work area as the core processing object, the spatial position information including the coordinates of the end points of the survey lines and the coordinate system parameters is extracted to obtain an initial spatial position data set of the target work area; the initial spatial position data set is subjected to information analysis and boundary fitting processing, and four vertex coordinates capable of completely covering the geographical space range of the target work area are calculated through a fitting algorithm; the four vertex coordinates are presented in the form of a rectangle to form basic framework data describing the plane boundary of the target work area, which provides a reference for subsequent work area range definition.

[0047] The initial spatial position data set is subjected to information analysis and boundary fitting processing, and four vertex coordinates capable of completely covering the geographic spatial range of the target work area are calculated through a fitting algorithm. Specifically, information analysis processing is performed on the initial spatial position data set to extract the plane coordinate values (including x coordinate values and y coordinate values) of all survey line endpoints, thereby obtaining a survey line endpoint coordinate set; all x coordinate values are separated from the survey line endpoint coordinate set, and the maximum x coordinate value and the minimum x coordinate value are selected through an extreme value calculation algorithm, and all y coordinate values are separated, and the maximum y coordinate value and the minimum y coordinate value are selected; the maximum x coordinate value and the maximum y coordinate value are combined to form a first vertex coordinate, the maximum x coordinate value and the minimum y coordinate value are combined to form a second vertex coordinate, the minimum x coordinate value and the minimum y coordinate value are combined to form a third vertex coordinate, and the minimum x coordinate value and the maximum y coordinate value are combined to form a fourth vertex coordinate; the rectangular region formed by the four vertex coordinates is subjected to coverage range verification to confirm that the rectangle can completely contain all coordinate points in the survey line endpoint coordinate set, and finally four vertex coordinates capable of completely covering the geographic spatial range of the target work area are obtained.

[0048] Referring again to Figure 2 , preferably, in a scenario, when step 12 is specifically implemented: taking the four vertex coordinates determined in step 11 as reference data, a rectangular region data capable of completely containing the target work area is delimited through range calculation; the rectangular region data is associated with the bin size parameters defined by the field observation system (the x-direction size is Δx and the y-direction size is Δy, and Δx and Δy are both the smallest size units for observing underground objects in geological exploration); starting from the lower left corner coordinate of the rectangular region data with bin size parameters, the plane gridding division operation is sequentially performed along the x-direction with a Δx interval and along the y-direction with a Δy interval, and the rectangular region is cut into a plurality of regular bin data; the regular bin data is integrated to generate the grid plane data of the target work area, thereby providing a grid carrier for subsequent bin positioning.

[0049] Preferably, the specific implementation process of step 13 is as follows: taking each bin data in the grid plane data of the target work area generated in step 12 as a processing unit, first obtaining the row and column position parameters (denoted as the i-th row and the j-th column, i and j are both positive integers, i represents the arrangement serial number along the y-direction of the grid plane, and j represents the arrangement serial number along the x-direction of the grid plane) of each bin in the grid plane; the row and column position parameters and the starting corner coordinate (x0, y0) of the lower left corner of the rectangular region of the grid plane are substituted into a preset coordinate calculation formula The operation is performed to obtain and define the coordinate position data of each bin. For the coordinate position data of each bin, a unique identification number is assigned to it, and the identification number adopts the digital numbering rule of inline in the row direction and crossline in the column direction (the identification number of the bin corresponding to the starting corner point is set as (1, 1), and the identification number of the bin in the i-th row and j-th column is set as (i, j)). The bin data with the coordinate position data and the unique identification number are integrated to generate the bin information data set with the coordinates and the identification, which can support the fast positioning and identification operation of a specific bin in the subsequent steps.

[0050] Preferably, in the specific technical implementation of step 14: taking the ground elevation data set of the target work area as the processing object, performing data collection and screening processing, and extracting the highest elevation point data through elevation comparison; taking the horizontal plane corresponding to the highest elevation point data as the reference surface data, determining the minimum depth data of the target work area in combination with the requirement for the initial exploration range below the ground surface in geological exploration; obtaining the depth range data related to the exploration target (such as deep oil and gas reservoir) in geological exploration, and selecting the maximum value of the depth range as the maximum depth data of the target work area; integrating the minimum depth data and the maximum depth data to form the target work area depth data set containing the vertical exploration range of the target work area, providing depth reference for subsequent three-dimensional space definition.

[0051] Specifically, in a scenario, the implementation of the above steps includes: taking the ground elevation data set of the target work area as the processing object, performing data collection and screening processing, and eliminating abnormal elevation values (such as unreasonable values caused by measurement errors) to obtain the screened ground elevation data; performing elevation comparison operation on the screened ground elevation data to extract the elevation data with the maximum value as the highest elevation point data; taking the horizontal plane corresponding to the highest elevation point data as the reference surface data, calling the preset initial exploration range parameter below the ground surface in geological exploration (such as initial exploration starting from 50 meters below the reference surface), determining the minimum depth data of the target work area through the difference calculation between the reference surface data and the initial exploration range parameter; obtaining the depth range data related to the exploration target (such as deep oil and gas reservoir) recorded in geological exploration, performing maximum value extraction processing on the depth range data, and taking the extracted maximum value as the maximum depth data of the target work area; associating and integrating the minimum depth data and the maximum depth data of the target work area to form the target work area depth data set containing the vertical exploration range of the target work area, which provides an explicit depth reference standard for subsequent three-dimensional space definition.

[0052] Preferably, in a scenario, the step 15 is specifically implemented as follows: first, the face element information dataset generated in step 13 is called, which contains the coordinates and identification numbers of the face elements, and is associated and matched with the target work area depth dataset determined in step 14 to establish a one-to-one correspondence between the coordinates, identification numbers and depth ranges of each face element; based on the correspondence, the spatial definition standard data covering the x, y and z dimensions of the target work area is constructed by combining the plane coordinate data of the face element with the depth range data; the spatial range of the target work area is defined by using the three-dimensional spatial definition standard data, and the complete definition of the target work area in the three-dimensional space is finally completed, and the three-dimensional spatial definition data formed can directly lay a spatial foundation for the subsequent three-dimensional model construction of the geological envelope.

[0053] Specifically, in a scenario, the implementation of the above steps includes: calling the face element information dataset generated in step 13, which contains the plane coordinates (x, y) and unique identification numbers of each face element; at the same time, calling the target work area depth dataset determined in step 14, which contains the minimum and maximum depth values of the target work area; through the identification number index, the face element information dataset is associated and matched with the depth dataset, and the corresponding depth range (minimum depth value to maximum depth value) is bound for the plane coordinates (x, y) and identification number of each face element, to establish a one-to-one correspondence between the coordinates, identification numbers and depth ranges of each face element; based on the correspondence, the plane coordinate data (x, y) of each face element is combined with the bound depth range data (z) to form spatial data units containing x, y and z three-dimensional parameters, and these spatial data units are integrated to construct the spatial definition standard data covering the x, y and z dimensions of the target work area (wherein x and y correspond to the plane coordinate range, and z corresponds to the depth range); the spatial range of the target work area is numerically defined by using the three-dimensional spatial definition standard data, the coordinate boundaries in the x and y directions and the depth boundary in the z direction of the target work area are determined, and the complete definition of the target work area in the three-dimensional space is finally completed, and the three-dimensional spatial definition data formed can directly provide a clear spatial framework foundation for the subsequent three-dimensional model construction of the geological envelope.

[0054] Optionally, step 2, the top and bottom interfaces of the geological envelope in the defined target work area are picked up to determine the top and bottom interface layer positions and the corresponding top and bottom interface data, specifically including:

[0055] Step 21, based on the defined target work area, the layer position identification processing of the geological envelope is performed in combination with the seismic wave reflection characteristics in the seismic data, to preliminarily distinguish the layer position range of the top and bottom interfaces of the suspected geological envelope, to generate the first layer position picking data of the top and bottom interfaces of the geological envelope;

[0056] Step 22, pick up the first layer position data, and verify and correct it in combination with the geological prior knowledge to determine the boundary characteristics of the top and bottom interface layer positions of the geological envelope, and to generate the second layer position data of the top and bottom interfaces of the geological envelope;

[0057] Step 23, iteratively optimize the second layer position data according to the structural form and spatial distribution characteristics of the geological envelope to generate the third layer position data of the top and bottom interfaces of the geological envelope;

[0058] Step 24, perform equal-interval control line sparse picking processing on the third layer position data in the inline and crossline directions to determine the top and bottom interface layer positions and the corresponding top and bottom interface data.

[0059] Preferably, in the specific technical implementation of step 21: the seismic data of the defined target work area is taken as the processing object, the reflection characteristic data (including amplitude, phase, frequency, etc. Characteristic parameters) of the seismic wave are extracted through the seismic data processing algorithm; these seismic wave reflection characteristic data are associated and matched with the definition data (grid range, depth interval) of the target work area; based on the associated characteristic data, the reflection interface that may belong to the geological envelope is identified through the layer position recognition model, and the layer position range data of the suspected top and bottom interfaces of the geological envelope are preliminarily distinguished; the layer position range data is structured to generate the first layer position data of the top and bottom interfaces of the geological envelope.

[0060] Specifically, in a scenario, the implementation of the above steps includes: taking seismic data of a defined target work area as a processing object, calling a seismic data processing algorithm (which is based on the principle that seismic waves will produce reflection signals at different lithology stratum interfaces, and highlights effective reflection signals by filtering, denoising and wave field separation of seismic data, and then extracts quantitative characteristic parameters such as amplitude, phase change and frequency of the reflection signals through time-frequency analysis), to obtain seismic wave reflection characteristic data containing amplitude, phase and frequency; correlating and matching these seismic wave reflection characteristic data with the definition data of the target work area (including grid plane coordinate range and vertical depth interval) through spatial coordinate mapping, so that each reflection characteristic parameter corresponds to a specific location in the three-dimensional grid of the target work area; based on the correlated seismic wave reflection characteristic data, calling a horizon recognition model (which learns the reflection characteristic templates of the top and bottom interfaces of the known geological envelope body, uses a feature similarity comparison algorithm to match the extracted reflection characteristics with the templates, and identifies the area where the reflection characteristics are continuous and meet the envelope body interface characteristics), identifies the reflection interfaces that may belong to the geological envelope body from the target work area seismic data, and preliminarily distinguishes the horizon range data of the suspected top and bottom interfaces of the geological envelope body (including the distribution position and corresponding depth of the interfaces in the grid); structuring the horizon range data (such as sorting by grid coordinates, supplementing missing position identifiers), to generate the first horizon picking data of the top and bottom interfaces of the geological envelope body.

[0061] Preferably, in a scenario, when step 22 is specifically implemented: taking the first horizon picking data generated in step 21 as an input object, calling a preset geological prior knowledge database (containing regional geological structure characteristics, stratum distribution rules, etc.); based on the geological prior knowledge database, verifying the horizon boundaries in the first horizon picking data, focusing on verifying the seismic wave amplitude change characteristic data corresponding to the picked horizon position; correcting abnormal horizon data caused by seismic data noise or recognition deviation, to clearly determine the real boundary characteristic data of the top and bottom interfaces of the geological envelope body; integrating the corrected boundary characteristic data to generate the second horizon picking data of the top and bottom interfaces of the geological envelope body.

[0062] Specifically, in a scenario, the implementation of the above steps includes: taking the first horizon picking data (containing horizon position data of the top and bottom boundaries of the suspected geologic envelope and seismic wave amplitude variation characteristic data of the corresponding positions) generated in step 21 as an input object, and calling a preset geologic prior knowledge database (which stores geologic structure characteristic data of the region to which the target work area belongs, such as stratum dip angle, fault strike parameter, and stratum distribution rule data such as typical seismic wave amplitude variation intervals corresponding to different lithologic interfaces); based on the stratum dip angle, fault strike and other structure characteristic data in the geologic prior knowledge database, the horizon boundary position in the first horizon picking data is preliminarily checked to determine whether the extension direction of the horizon boundary is consistent with the regional structure trend, and then combined with the typical seismic wave amplitude variation intervals of different lithologic interfaces, the seismic wave amplitude variation characteristic data corresponding to the picked horizon position is mainly checked to determine whether it is within the reasonable amplitude interval of the lithologic interface; for the amplitude sudden change horizon data identified in the checking due to seismic data noise, or the horizon data inconsistent with the regional structure trend due to horizon recognition deviation, the normal horizon data and seismic wave amplitude data of the adjacent effective grid points of the same line are called as references to adjust the position parameters of the abnormal horizon by a linear trend interpolation algorithm, and the corresponding amplitude variation characteristic data is corrected, so as to complete the correction processing of the abnormal horizon data and clearly determine the real boundary characteristic data (containing the corrected horizon position and the seismic wave amplitude variation characteristic conforming to the regional geologic rule) of the top and bottom boundaries of the geologic envelope; the corrected real boundary characteristic data of the top boundary and the real boundary characteristic data of the bottom boundary are associated and integrated according to the grid coordinates of the target work area to ensure that the position identification of each layer of data corresponds to the grid coordinates one by one, and finally the second horizon picking data of the top and bottom boundaries of the geologic envelope is generated.

[0063] Preferably, the specific implementation process of step 23 is as follows: taking the second horizon picking data obtained in step 22 as the basic data, calling the structure form parameters (such as morphological characteristic data such as lenticular and stratiform) and spatial distribution characteristic parameters (such as lateral extension length, vertical thickness variation rate and other data) of the geologic envelope; based on these parameters, the second horizon picking data is iteratively optimized, and after each round of optimization, the current horizon data is compared with the horizon data of the adjacent lines for consistency, and the horizon data with large deviation is adjusted; after multiple rounds of iterative optimization, the fitting degree of the picking result to the actual form of the geologic envelope is improved, and finally the third horizon picking data of the top and bottom boundaries of the geologic envelope is generated.

[0064] Specifically, in a scenario, the implementation of the above steps includes: taking the second layer position picking data (containing the layer position coordinates and corresponding depth values of the top and bottom boundaries of the geological envelope) obtained in step 22 as the basis data, while calling the structural form parameters (such as the curvature range corresponding to the lenticular shape, the horizontal extension angle corresponding to the stratiform shape, and other form feature data) and the spatial distribution characteristic parameters (such as the maximum threshold of the lateral extension length, the reasonable interval of the vertical thickness variation rate, and other data) of the geological envelope; based on these structural form parameters and spatial distribution characteristic parameters, iterative optimization processing is performed on the second layer position picking data, in the first round of optimization, the layer position data is compared with the lenticular or stratiform shape features through a form matching algorithm, and the layer position deviating from the form parameters is marked; after each round of optimization, the layer position coordinates in the inline direction of the current layer position data are extracted, and a consistency comparison is made with the layer position coordinates of the adjacent inlines, the spatial distance deviation value of the two is calculated, and when the deviation value exceeds the preset threshold, the layer position coordinates and the corresponding depth values with larger deviation are adjusted according to the lateral extension length threshold and the vertical thickness variation rate interval in the spatial distribution characteristic parameters; after multiple rounds of iterative optimization (such as 3-5 rounds), the form of the layer position data gradually fits the structural form parameters, and the layer position data deviation of adjacent lines is kept at a low level, improving the fitting degree of the picking result to the actual form of the geological envelope; finally, the third layer position picking data of the top and bottom boundaries of the geological envelope, which integrates the optimized top and bottom boundary layer position and depth values, is generated.

[0065] Preferably, in the specific technical implementation of step 24: taking the third layer position picking data generated in step 23 as the operation object, control line data is set in the inline direction and the crossline direction according to a preset interval parameter (such as 15m x 30m grid, 30 lines in the inline direction and 15 lines in the crossline direction as the interval); on each control line data, the top and bottom positions of the geological envelope are located through a seismic wave amplitude feature recognition algorithm, the top boundary layer position data is picked at the top position, and the bottom boundary layer position data is picked at the bottom position, and the coordinate information (x coordinate, y coordinate) and depth value information of the picking points on each control line are recorded; these picking point information is summarized and arranged, and the top boundary layer and the bottom boundary layer of the geological envelope and the corresponding top boundary data and bottom boundary data are finally determined through sparse picking processing.

[0066] Specifically, in a scenario, the implementation of the above step 24 includes: taking the third layer picking data (containing the optimized layer position coordinates and corresponding depth values of the top interface and bottom interface of the geological envelope) generated in step 23 as the operation object, setting a preset interval parameter according to the grid size parameter (such as a bin size of 15m x 30m) of the target work area, taking 30 lines as the interval in the inline direction and 15 lines as the interval in the crossline direction, filtering out the lines meeting the interval requirement through line number indexing, and generating control line data (each control line contains grid points continuous along the line direction and covers the depth detection range of the target work area) covering the target work area; for each control line data, calling a seismic wave amplitude feature recognition algorithm (which is based on the principle that the lithology difference between the top interface and bottom interface of the geological envelope and the surrounding strata will cause a significant mutation of the seismic wave reflection amplitude, first extracting the seismic wave amplitude values of all grid points along the line of the control line and generating an amplitude variation curve, and then detecting the mutation position in the curve through a preset amplitude mutation threshold (such as an amplitude difference exceeding 30% of the average value of adjacent grid points), which is the top or bottom position of the geological envelope); at the located top mutation position, extracting the layer information (including plane coordinates and depth value) of the corresponding grid point as the top interface layer data, and at the bottom mutation position, extracting the layer information of the corresponding grid point as the bottom interface layer data, and synchronously recording the coordinate information (x coordinate, y coordinate) and depth value information of each picking point on each control line to form a picking point data set of a single control line; all control line picking point data sets are summarized, and through sparse picking processing (only keeping the picking point data at the control line, and eliminating the redundant layer data in the non-control line area), the top interface layer and bottom interface layer of the geological envelope containing complete picking point coordinates and depth information, as well as the corresponding top interface data and bottom interface data, are finally determined.

[0067] In a specific scenario, Figure 3 is a longitudinal angle diagram of the top interface layer and the bottom interface layer obtained after step 2 in a scenario, Figure 4 is a transverse angle diagram of the top interface layer and the bottom interface layer obtained after step 2 in a scenario, as shown in Figure 3 , 4 , wherein the top interface layer is represented by T and the bottom interface layer is represented by B. Figure 4 In the above, the color bar represents the depth value.

[0068] Optionally, in step 3, based on the grid positions corresponding to all picking point positions of the top interface layer and the bottom interface layer, the boundary lines of the geological envelope at the top interface and the bottom interface in the target work area are determined according to the ranges of the top interface and bottom interface data, specifically including:

[0069] Step 31, based on the range of the top interface and the bottom interface data, traverse all the grid positions corresponding to the pickup points of the top interface and the bottom interface horizon, combine the pre-constructed geological structure knowledge and spatial analysis algorithm, take the grid position as the spatial anchor point, calculate the distribution density and continuity of the pickup points in the three-dimensional space, and perform three-dimensional spatial distribution range calculation processing to generate preliminary three-dimensional spatial distribution data of the geological envelope, which is used to describe the approximate position and form of the geological envelope in the three-dimensional space;

[0070] Step 32, based on the preliminary three-dimensional spatial distribution data, associate the mapping relationship between the grid positions corresponding to all the pickup points of the top interface and the bottom interface horizon and the three-dimensional space coordinates, call the geological structure modeling tool, combine the geological structure map and regional geological data of the target work area, mark the spatial position of all the pickup point grids to determine the spatial relationship between the geological envelope and the surrounding geological environment;

[0071] Step 33, based on the spatial relationship between the geological envelope and the surrounding geological environment, filter out the outermost pickup point grid in the vertical and horizontal directions to generate an initial boundary line, and the initial boundary line represents the three-dimensional boundary of the geological envelope in the form of a polygon or a curve;

[0072] Step 34, check whether the initial boundary line completely covers the distribution range of all the pickup point grids, if there are edge grid points not included, adjust the boundary line to include the edge grid points, perform boundary condition optimization processing to generate the boundary line of the geological envelope at the top interface and the bottom interface, and the edge grid point is the grid position corresponding to all the pickup points of the top interface and the bottom interface horizon, which is located at the outermost of the spatial distribution.

[0073] Preferably, the specific implementation process of step 31 is as follows: taking the range of top and bottom interface data as the initial processing object, traversing the grid positions corresponding to all the pickup points of the top and bottom interface horizons through a grid traversal program to obtain a grid position dataset corresponding to all the pickup points of the top and bottom interface horizons; taking the grid position dataset as the spatial anchor point data for three-dimensional space analysis, calling the pre-constructed geological structure knowledge database (containing fault distribution rules, stratigraphic continuity characteristics and other data) and the pre-set spatial analysis algorithm (such as the grid cell continuity determination algorithm), and performing operations on the spatial anchor point data to calculate the distribution density data of the pickup points in the three-dimensional space and the connectivity data of adjacent grid cells; inputting the distribution density data and the connectivity data into a three-dimensional space range calculation module to perform three-dimensional space distribution range calculation processing and generate preliminary three-dimensional space distribution data for describing the approximate position and form of the geological envelope in the three-dimensional space. This processing method is different from the traditional method of relying only on manual estimation of the distribution range. By combining geological structure knowledge, the interference of faults and lithological changes on the distribution range judgment is reduced, and the preliminary distribution data is more consistent with the actual geological conditions.

[0074] Specifically, in one scenario, the implementation of step 31 above includes:

[0075] Taking the range of top and bottom interface data as the initial processing object, the range of the top and bottom interface data includes the grid coordinate interval of all picked horizons in the inline and crossline directions; calling a grid traversal program, traversing the grid positions corresponding to all the pickup points of the top and bottom interface horizons in the order of "first inline direction and then crossline direction", recording the three-dimensional grid coordinates (i, j, k, where i is the inline direction index, j is the crossline direction index, and k is the depth direction index) of each pickup point; integrating all recorded three-dimensional grid coordinates into a structured data set to obtain a grid position dataset corresponding to all the pickup points of the top and bottom interface horizons, ensuring that the dataset covers all picked horizon grid points and has no repetition.

[0076] The grid position data set corresponding to all pickup points of the top interface and the bottom interface horizon is taken as the spatial anchor point data for three-dimensional space analysis, a pre-constructed geological structure knowledge database is called, the database stores fault distribution data (such as fault strike and fault throw threshold) and stratum continuity feature data (such as the upper limit of the thickness variation rate of the same stratum) of the target work area, the spatial anchor point data is associated with the fault position and stratum parameters in the database, and the effective spatial anchor points located outside the fault influence area and meeting the stratum continuity requirement are marked; meanwhile, a preset grid unit continuity determination algorithm is called, the technical essence of the algorithm is to calculate the difference of adjacent effective spatial anchor points in the depth direction, if the difference is less than the upper limit of the stratum thickness variation rate, it is determined that the adjacent grid units are connected, otherwise, it is determined that the adjacent grid units are not connected; based on the effective spatial anchor points and the algorithm, the spatial anchor point data is operated, the number of effective spatial anchor points in a unit three-dimensional grid volume is counted to obtain the distribution density data of the pickup points in the three-dimensional space, and the connectivity data of the adjacent grid units (connected is represented by Boolean value "1" and not connected is represented by Boolean value "0") are recorded.

[0077] The distribution density data of the pickup points in the three-dimensional space and the connectivity data of the adjacent grid units are taken as inputs and transmitted to a three-dimensional space range calculation module; the technical essence of the module is to define the range in combination with the density threshold and the connectivity rule: first, a distribution density threshold is set (determined based on the average anchor point density of the stratum of the target work area, and the area below the threshold is determined as a non-envelope body area), and a high-density anchor point cluster with a density higher than the threshold is screened out; then, based on the connectivity data, the continuous area with a connectivity state of "1" in the high-density anchor point cluster is retained, and isolated low-density anchor points are removed; the coordinate extreme values (maximum value and minimum value) of the continuous area in the Inline direction, the crossline direction and the depth direction are calculated to determine the three-dimensional coordinate boundary of the area; three-dimensional space distribution range calculation processing is performed, the three-dimensional coordinate boundary and the approximate outline of the continuous area are integrated, and preliminary three-dimensional space distribution data for describing the approximate position and morphology of the geological envelope in the three-dimensional space are generated, the data includes the three-dimensional coordinate range of the envelope and the anchor point distribution density of the core area.

[0078] The processing mode is different from the traditional mode which only relies on manual estimation of the distribution range, the traditional mode is easy to ignore the influence of faults and lithological changes on the distribution of anchor points due to differences in manual experience, resulting in large range estimation deviation; and the present mode marks the effective spatial anchor points outside the fault influence area by associating the geological structure knowledge database in the operation process, reduces the interference of faults and lithological changes on the judgment of the distribution range, quantifies the density and connectivity through the algorithm, avoids the deviation of manual subjective judgment, and makes the generated preliminary three-dimensional space distribution data more consistent with the actual geological conditions of the target work area, thereby providing more reliable basic data for subsequent boundary line determination.

[0079] Preferably, in a scenario, step 32 is specifically implemented as follows: the preliminary three-dimensional spatial distribution data generated in step 31 is used as the input data, a mapping relationship between the grid positions corresponding to all the picked points on the top and bottom interface horizons and the three-dimensional spatial coordinates (x coordinate, y coordinate, and depth value) is established by a coordinate mapping algorithm to obtain a grid position-three-dimensional coordinate mapping relationship table; a geological structure modeling tool (such as a professional seismic interpretation software) is called to import the geological structure map data (including the stratum interface trend and fault position annotation) and regional geological data (such as stratum lithology and tectonic movement period data) of the target work area into the modeling tool; based on the grid position-three-dimensional coordinate mapping relationship table, the spatial position of each picked point grid is marked one by one in the three-dimensional coordinate system of the modeling tool to form a spatial position marking data set; the spatial position marking data set is integrated and analyzed, and the spatial relationship data of the geological envelope and the surrounding geological environment is determined by comparing the relative positions of the grid positions and the surrounding strata and faults.

[0080] Preferably, in the specific technical implementation of step 33: the spatial relationship data of the geological envelope and the surrounding geological environment determined in step 32 is used as the basis for judgment, and the peripheral screening operation is performed for the grid elements of the top and bottom interface horizons respectively; first, each control line in the inline direction is traversed, and the outermost picked point grid in the inline direction is screened out according to the spatial distance and connectivity of the grid elements and the adjacent strata to obtain an inline direction peripheral grid data set; then, each control line in the crossline direction is traversed, and the same screening logic is used to screen out the outermost picked point grid in the crossline direction to obtain a crossline direction peripheral grid data set; the grid points of the inline direction peripheral grid data set and the crossline direction peripheral grid data set are connected in three-dimensional spatial distribution order (such as clockwise or counterclockwise) by a curve fitting or polygon connection algorithm to form a contour, and an initial boundary line representing the three-dimensional boundary of the geological envelope in the form of a polygon or a curve is generated.

[0081] Preferably, the specific implementation process of step 34 is as follows: taking the initial boundary line generated in step 33 as a verification object, calling a boundary coverage verification algorithm, comparing the spatial range of the initial boundary line with the grid position data corresponding to all the pickup points of the top and bottom interfaces, and obtaining the boundary coverage verification result; if the verification result shows that there is an edge grid point (i.e. the grid point at the outermost periphery of the grid position corresponding to all the pickup points of the top and bottom interfaces) not contained by the initial boundary line, then calling a boundary adjustment algorithm, calculating the adjustment parameters of the boundary line according to the three-dimensional coordinates (x, y, depth) of the edge grid point, adjusting the orientation of the initial boundary line to contain the edge grid point, and obtaining the adjusted boundary line; superimposing the adjusted boundary lines of the top and bottom interfaces, picking up the outermost grid points of the superimposed spatial range for contour integration, performing boundary condition optimization processing to eliminate local irregular deviations of the boundary line, and generating the optimized boundary line. This optimization process automatically adjusts by algorithm instead of traditional manual correction, not only reducing labor consumption, but also keeping the fit degree of the boundary line and the pickup points highly consistent.

[0082] Figure 5 A specific boundary line plane diagram obtained after step 3 is shown in FIG. 3. As shown in FIG. 3, the processing of step 3 for the top and bottom interfaces of the target work area obtains clear boundary lines. Figure 5 Figures 3-4

[0083] Optionally, step 4, the top and bottom interface data are continuous, to determine the depth value of all grid points in the top and bottom interfaces, specifically including:

[0084] Step 41, the top and bottom interface data are interpolated to generate preliminary gridding data;

[0085] Step 42, the preliminary gridding data are extrapolated to generate extended gridding data extending to the entire target work area;

[0086] Step 43, based on the boundary line, the spatial range of the extended gridding data is determined, the coordinate points within the boundary line are marked as valid data, and the coordinate points outside the boundary line are marked as invalid data, to generate top and bottom interface data with validity identification;

[0087] Step 44, based on the top and bottom interface data with validity identification, the effective depth value of each grid point in the grid plane of the target work area is extracted and continuous integration processing is performed, to generate the depth value of all grid points in the top and bottom interfaces.

[0088] ​​Preferably, the specific implementation process of step 41 is as follows: the top and bottom interface data obtained in step 2 are taken as initial processing objects, the grid points on the equally spaced control lines in the inline and crossline directions of the top and bottom interface data only contain depth values, and the depth data of the grid points in the non-control line area are sparsely distributed; based on the distribution characteristics (such as data density, spatial uniformity) of the top and bottom interface data, a suitable interpolation algorithm (such as distance weighted interpolation method, Kriging interpolation method) is automatically matched and called; the depth value filling operation is performed on the sparse non-control line grid points by the interpolation algorithm, and the depth data of the grid points at the non-control line positions are supplemented; and finally, the preliminary gridded data covering part of the grid area of the target work area is generated. This technology is different from the traditional fixed single interpolation algorithm, and can reduce the depth value deviation caused by algorithm mismatch by dynamically selecting the algorithm combined with the data distribution characteristics, so that the accuracy of the preliminary gridded data remains at a high level.

[0089] Specifically, in a scenario, the implementation of the above step 41 includes: taking the top and bottom interface data obtained in step 2 as the initial processing object, the structure of the top and bottom interface data is that: the grid points on the equally spaced control lines in the inline and crossline directions each contain a corresponding depth value (z value), and the grid points in the non-control line area only retain coordinate information (x, y) because they are not picked up, and the depth data is sparsely distributed; performing distribution feature analysis on the top and bottom interface data, calculating the number of control points with depth values in a unit grid area to obtain data density, and calculating the standard deviation of the difference between adjacent control point depth values to obtain spatial uniformity; based on the analysis results of the data density and the spatial uniformity, automatically matching the interpolation algorithm - when the data density is low and the spatial uniformity is high (the difference standard deviation is small), matching the distance weighted interpolation method (the technical essence of this algorithm is to weight the distance between the point to be interpolated and the surrounding known control points, the closer the distance, the greater the weight, and the depth value of the point to be interpolated is calculated by weighted average), when the data density is high and the spatial uniformity is low (there is an obvious depth change trend), matching the Kriging interpolation method (the technical essence of this algorithm is to construct a variogram based on the spatial correlation of known points, estimate the spatial correlation degree of the point to be interpolated and the known points through the variogram, and then calculate the depth value); calling the matched interpolation algorithm to perform depth value filling operation on the sparse grid points in the non-control line area, taking each non-control line grid point as the center, searching for the depth values of the control line grid points within a preset range around it as known data, and substituting them into the algorithm formula to calculate the depth value of the non-control line grid point; integrate the filled non-control line grid point depth value with the original control line grid point depth value to generate preliminary gridded data covering part of the grid area of the target work area (including control lines and adjacent non-control line areas). This technology is different from the traditional fixed single interpolation algorithm. By dynamically selecting a more suitable algorithm combined with the data distribution characteristics, it can reduce the depth value deviation caused by the mismatch between the algorithm and the data characteristics, and keep the accuracy of the preliminary gridded data at a high level.

[0090] Preferably, in a scenario, when step 42 is specifically implemented: taking the preliminary gridded data generated in step 41 as the input object, calling a preset extrapolation algorithm (such as a trend extrapolation algorithm based on polynomial fitting); the extrapolation algorithm extends the data range to the grid area of the target work area not covered by the preliminary gridded data according to the existing spatial distribution trend of the preliminary gridded data (such as the variation law of the depth of the stratum along the direction of the measuring line); during the extension process, the depth values of the grid points in the uncovered area are calculated by the algorithm to ensure that the supplemented depth values are consistent with the surrounding existing depth values in trend; finally, the extended gridded data is generated, which is extended to the entire target work area, so that each grid point of the target work area corresponds to a depth value, avoiding the omission of subsequent boundary determination caused by incomplete data coverage.

[0091] Specifically, in one scenario, the implementation of the above step 42 includes: taking the preliminary gridding data generated in step 41 as the input object, which only covers part of the grid area of the target work area, and the grid points in the uncovered area are marked as blank due to the lack of depth values; calling a preset trend extrapolation algorithm based on polynomial fitting, the technical essence of which is to construct a trend model of depth value changes with inline (Inline) direction coordinate and crossline (crossline) direction coordinate by polynomial function fitting (such as quadratic or cubic polynomial) on the existing depth values in the preliminary gridding data (the independent variables in the model are Inline coordinate x and crossline coordinate y, and the dependent variable is depth value z); the algorithm analyzes the existing spatial distribution trend of the preliminary gridding data (such as the law of linear increase of depth value with x and parabolic change of y) according to the trend model, and extends the data range to the grid area of the target work area (blank grid points) not covered by the preliminary gridding data; during the extension process, the algorithm substitutes the coordinates (x, y) of each blank grid point in the uncovered area into the trend model to calculate the depth value of the blank grid point, and adjusts the predicted value with large deviation through trend consistency check with the surrounding existing depth values (such as calculating the difference between the predicted depth value and the nearest known depth value to ensure that the difference is within the preset trend fluctuation range); the depth values of the grid points in the uncovered area after the check and adjustment are integrated with the existing depth values in the preliminary gridding data, and finally the extended gridding data extended to the entire target work area is generated, in which each grid point of the target work area corresponds to a depth value, avoiding the omission of subsequent boundary determination due to incomplete data coverage. This technology is different from the traditional simple linear extrapolation method, which captures more complex spatial trends through polynomial fitting, making the supplemented depth values more consistent with the actual variation of the stratum.

[0092] Preferably, in the specific technical implementation of step 43: taking the extended gridding data generated in step 42 as the analysis object, calling the boundary lines at the top and bottom interfaces of the geological envelope determined in step 3 as the spatial range determination basis; performing spatial position check operation on each coordinate point (corresponding to the grid points of the target work area) in the extended gridding data, and judging whether the coordinate point is located inside the boundary line by comparing the spatial position relationship between the coordinate point and the boundary line; marking the depth data corresponding to the coordinate points inside the boundary line as valid data, and marking the depth data corresponding to the coordinate points outside the boundary line as invalid data; through the above marking operation, the top interface horizon data and the bottom interface horizon data with validity identification are generated, providing data screening basis for subsequent extraction of valid depth values.

[0093] Preferably, the specific implementation process of step 44 is as follows: taking the top and bottom interface horizon data with validity identification generated in step 43 as the data source, traversing each grid point in the grid plane where the target work area is located; for each grid point, extracting its corresponding effective depth value (i.e. the depth value marked as valid data), and automatically ignoring the depth value marked as invalid data; for the extracted effective depth value, performing a continuous integration operation to realize smooth transition of the depth value by eliminating the depth value jump between adjacent grid points, and ensuring the continuity of the depth data in spatial distribution; finally generating the horizon depth value in all grid points of the top interface and the horizon depth value in all grid points of the bottom interface, and providing continuous and reliable depth data basis for subsequent construction of the three-dimensional model of the geological envelope.

[0094] Figure 6 It is a plan view of valid data and invalid data of the top interface and the bottom interface in an application scenario. Figure 7 It is a perspective view of valid data and invalid data of the top interface and the bottom interface in an application scenario. Figure 8 It is a perspective view of the top interface and the bottom interface abutting in an application scenario. Figures 6-8 For the scenario of Figures 3-5 , the valid data and the invalid data are distinguished based on the boundary line, the invalid data forms an invalid area, and the valid data forms a valid area. From the perspective, the top interface and the bottom interface are spliced to form a three-dimensional envelope according to the valid area and the invalid area. Figure 6 In the figure, the color bar is the horizon depth value.

[0095] Optionally, step 5, according to the horizon depth value, based on the boundary line of the geological envelope at the top interface and the bottom interface, the top interface boundary constraint and the bottom interface boundary constraint are constructed, specifically including:

[0096] Step 51, according to the horizon depth value, based on the boundary line of the geological envelope at the top interface and the bottom interface, the top interface boundary constraint is constructed in combination with the top interface horizon data with validity identification;

[0097] Step 52, according to the horizon depth value, based on the boundary line of the geological envelope at the top interface and the bottom interface, the bottom interface boundary constraint is constructed in combination with the bottom interface horizon data with validity identification.

[0098] Optionally, step 51, according to the horizon depth value, based on the boundary line of the geological envelope at the top interface and the bottom interface, the top interface boundary constraint is constructed in combination with the top interface horizon data with validity identification, specifically including:

[0099] Step 511, based on the boundary line, traversing and spatial position judgment processing are performed on the corresponding coordinate points of the top interface horizon data with validity identification in the grid plane where the target work area is located, to generate top interface coordinate point data marked with boundary inside / outside attributes, which is used to mark the position information of each coordinate point relative to the boundary line;

[0100] Step 512, according to the top interface coordinate point data marked with boundary inside / outside attributes, the horizon depth value of the corresponding top interface is retained for the coordinate points inside the boundary line; the maximum depth value defined by the work area is given to the coordinate points outside the boundary line, to obtain top interface depth data subjected to boundary constraint assignment;

[0101] Step 513, data format standardization and integrity checking processing are performed on the top interface depth data subjected to boundary constraint assignment, to generate the final top interface boundary constraint.

[0102] Preferably, in the specific technical implementation of step 511: taking the boundary line at the top interface of the geological envelope as the spatial reference datum, the top interface horizon data with validity identification generated in step 43 is called; all coordinate points corresponding to the top interface horizon data in the grid plane where the target work area is located are traversed, and spatial position judgment processing is performed on each coordinate point through a spatial geometric judgment algorithm (which is associated with the polygon or curve parameters of the boundary line) to determine the position of the coordinate point relative to the boundary line (inside or outside the boundary line); according to the judgment result, an attribute label of “boundary inside” or “boundary outside” is added to each coordinate point, to generate top interface coordinate point data marked with boundary inside / outside attributes, which contains the plane coordinates (x, y), corresponding depth value and boundary attribute information of each coordinate point.

[0103] Specifically, in one scenario, the implementation of step 511 includes: taking the boundary line at the top interface of the geological envelope as the spatial reference datum, which contains the geometric shape parameters of the boundary: if it is a polygon boundary, the vertex coordinate set is recorded , if the boundary is a curve, record the curve fitting parameters (such as the coefficients of a quadratic curve); call the top interface horizon data with validity identification generated in step 43, which contains the plane coordinates (x, y) of all top interface coordinate points in the target work area grid plane, the corresponding depth value and the previously marked validity identification; according to the grid index order of the inline and crossline, traverse all coordinate points in the top interface horizon data with validity identification point by point, and for each coordinate point to be judged, call the spatial geometry judgment algorithm (the technical essence of the algorithm is: if the boundary line is a polygon, a "ray method" is used to emit a virtual ray from the coordinate point in the horizontal direction, count the number of intersection points of the ray and the edges of the polygon, and if the number of intersection points is odd, it is determined that the coordinate point is inside the boundary line, and if it is even, it is outside; if the boundary line is a curve, first discretize the curve into a polygon according to the preset accuracy, and then execute the "ray method" to determine), and the algorithm will automatically associate the geometric parameters of the boundary line (polygon vertex coordinates or curve discretized vertex coordinates) for operation; through the spatial geometry judgment algorithm, the spatial position of the coordinate point is determined (inside or outside the boundary line); according to the judgment result, add the "inside the boundary" or "outside the boundary" attribute label to each coordinate point (x, y), bind and integrate the attribute label with the plane coordinates (x, y) of the coordinate point and the corresponding depth value, and generate top interface coordinate point data marked with boundary inside / outside attributes, which contains the plane coordinates (x, y) of a single point, the depth value and the boundary attribute information.

[0104] Preferably, the specific implementation process of step 512 is as follows: taking the top interface coordinate point data marked with boundary inside / outside attributes generated in step 511 as the input object, filtering out the top interface coordinate points marked as "inside the boundary", and directly retaining the top interface horizon depth values corresponding to these coordinate points; for the top interface coordinate points marked as "outside the boundary", calling the maximum depth value of the target work area determined in step 14 to assign the maximum depth value to these coordinate points; through this differential assignment processing for inside and outside the boundary, the top interface depth data subjected to boundary constraint assignment is obtained, which makes the top interface depth present a downward trend matching the depth range of the work area at the boundary line, avoiding the interference of the data outside the boundary to the envelope body shape.

[0105] Preferably, in a scenario, step 513 is specifically implemented as follows: taking the top interface depth data subjected to boundary constraint assignment generated in step 512 as the processing object, performing format standardization processing on the top interface depth data according to a preset data format standard (for example, a three-dimensional array form of "coordinate point (x, y)-depth value (z)-boundary attribute" is uniformly adopted); performing integrity checking on the standardized top interface depth data, checking whether there are problems such as coordinate point omission and abnormal depth value (for example, exceeding the depth range of the work area), and correcting the abnormal data; after the standardization and checking processing, generating the final top interface boundary constraint, which can be directly used for subsequent boundary interaction calculation of the top interface and the bottom interface.

[0106] Optionally, step 52, according to the depth value of the horizon, constructs the bottom interface boundary constraint based on the boundary line of the geological envelope at the top interface and the bottom interface, in combination with the bottom interface horizon data with the validity identifier, specifically includes:

[0107] Step 521, based on the boundary line, iterates and performs spatial position judgment processing on the coordinate points corresponding to the bottom interface horizon data with the validity identifier in the grid plane where the target work area is located, to generate the bottom interface coordinate point data marked with the boundary inside / outside attribute, which marks the position relationship between each coordinate point and the boundary line;

[0108] Step 522, according to the bottom interface coordinate point data marked with the boundary inside / outside attribute, retaining the depth value of the corresponding bottom interface for the coordinate points inside the boundary line, and assigning the minimum depth value defined by the work area to the coordinate points outside the boundary line, to obtain the bottom interface depth data subjected to boundary constraint assignment;

[0109] Step 523, performing data format standardization and integrity checking processing on the bottom interface depth data subjected to boundary constraint assignment, to generate the final bottom interface boundary constraint.

[0110] Preferably, in the specific technical implementation of step 521: taking the boundary line of the bottom interface of the geological envelope as the spatial reference datum, calling the bottom interface horizon data with the validity identifier generated in step 43; iterating all coordinate points corresponding to the bottom interface horizon data in the grid plane where the target work area is located, and performing spatial position judgment processing on each coordinate point through the same spatial geometric judgment algorithm (associated with the polygon or curve parameter of the boundary line) as step 511, to determine the position of the coordinate point relative to the boundary line (inside or outside the boundary line); according to the judgment result, adding the attribute labels of "boundary inside" or "boundary outside" to each coordinate point, to generate the bottom interface coordinate point data marked with the boundary inside / outside attribute, which contains the plane coordinates (x, y), the corresponding depth value and the boundary attribute information of each coordinate point, clearly marking the position relationship between the coordinate point and the boundary line.

[0111] Preferably, the specific implementation process of step 522 is as follows: taking the bottom interface coordinate point data marked with the inside / outside boundary attribute generated in step 521 as the input object, screening out the bottom interface coordinate points marked as "inside the boundary", directly retaining the bottom interface horizon depth values corresponding to these coordinate points; for the bottom interface coordinate points marked as "outside the boundary", calling the target work area minimum depth value determined in step 14 to assign the minimum depth value to these coordinate points; through this differentiated assignment processing for the inside and outside of the boundary, the bottom interface depth data subjected to boundary constraint assignment is obtained, which makes the bottom interface depth present an upward trend matching the work area depth range at the boundary line, avoiding the interference of the data outside the boundary on the bottom shape of the geological envelope.

[0112] Preferably, in a scenario, when step 523 is specifically implemented: taking the bottom interface depth data subjected to boundary constraint assignment generated in step 522 as the processing object, performing format standardization processing on the bottom interface depth data according to the same preset format standard (such as uniformly adopting the three-dimensional array form of "coordinate point (x, y)-depth value (z)-boundary attribute") as the top interface boundary constraint, ensuring the consistency of the top and bottom interface data formats; performing integrity checking on the standardized bottom interface depth data, checking whether there are problems such as coordinate point omission, abnormal depth value (such as lower than the work area minimum depth or higher than the corresponding top interface depth), and modifying the abnormal data; after the standardization and checking processing, the final bottom interface boundary constraint data is generated, which can work together with the top interface boundary constraint to provide complete boundary control basis for the three-dimensional spatial definition of the geological envelope.

[0113] Optionally, step 6, based on the constructed top interface boundary constraint and bottom interface boundary constraint, determining the intersection point between the top interface and the bottom interface to optimize the boundary line of the geological envelope at the top interface and the bottom interface specifically includes:

[0114] Step 61, based on the top interface boundary constraint and the bottom interface boundary constraint, performing point-by-point comparison processing on the depth values of all coordinate points of the top and bottom interface horizons to generate a top-bottom interface intersection point candidate set, which contains coordinate points with equal depth values of the top interface and the bottom interface as possible intersection positions of the top and bottom interfaces at the boundary;

[0115] Step 62, performing effectiveness checking processing on the top-bottom interface intersection point candidate set to eliminate abnormal intersection points caused by data errors, obtaining an effective top-bottom interface intersection point set, which includes the real intersection positions of the top and bottom interfaces at the boundary;

[0116] Step 63, re-fitting the boundary curve in the three-dimensional space for the effective top-bottom interface intersection point set to generate an optimized boundary line.

[0117] Preferably, the specific implementation process of step 61 is as follows: call the top interface boundary constraint and the bottom interface boundary constraint generated in step 5, take the same coordinate point (x, y) in the grid plane where the target work area is located as the matching reference, extract the top interface depth value and the bottom interface depth value corresponding to each coordinate point; perform point-by-point comparison processing on these top interface depth values and bottom interface depth values, calculate the difference between the two; filter out the coordinate points with a difference of zero (i.e., equal depth values), and integrate the three-dimensional coordinates (x, y, z) of these coordinate points to generate a candidate set of top-bottom interface intersection points, which contains the coordinate points as possible intersection positions of the top and bottom interfaces at the boundary.

[0118] Preferably, in the specific technical implementation of step 62: take the candidate set of top-bottom interface intersection points generated in step 61 as the analysis object, call the spatial distribution rule data of the geological envelope (such as the spatial continuity feature near the boundary line, the distance threshold of adjacent intersection points, etc.); based on these rule data, perform validity check processing on each intersection point in the candidate set to determine whether the intersection point conforms to the spatial distribution trend of the geological envelope boundary; remove isolated abnormal points (such as points with a distance from surrounding intersection points exceeding the threshold) or intersection points that do not conform to the spatial trend due to data errors, to obtain an effective set of top-bottom interface intersection points containing the real intersection positions of the top and bottom interfaces at the boundary.

[0119] Preferably, in a scenario, when step 63 is implemented: take the effective set of top-bottom interface intersection points generated in step 62 as the basis data, and the intersection points in this set contain spatial distribution information in the inline direction and the crossline direction; call the three-dimensional curve fitting algorithm, input the three-dimensional coordinates (x, y, z) of the effective intersection points into the algorithm, and the algorithm re-fits the boundary curve according to the distribution characteristics of the intersection points in three-dimensional space (such as the variation trend along the measuring line direction and the continuity in the depth direction); ensure that the curve passes through all effective intersection points and maintains smooth transition between adjacent points during the fitting process, and finally generate an optimized boundary line that better fits the actual intersection boundary of the top and bottom interfaces of the geological envelope.

[0120] Specifically, in a scenario, the implementation of the above step 63 includes: taking the valid top and bottom interface intersection point set generated in step 62 as the basis data, which contains the inline coordinate x, crossline coordinate y, depth coordinate z of each intersection point, and the data density identification of the survey line to which the intersection point belongs (such as the number of intersection points in each 100m grid in the Inline direction, the number of intersection points in each 100m grid in the crossline direction); calling a weighted three-dimensional curve fitting algorithm suitable for the geological survey line scene (the technical essence of the algorithm is: combining the spatial correlation of geological data, assigning different weights to intersection points with different data densities - the weight of intersection points in areas with high data density (such as Inline direction, the number of points in each 100m grid is ≥5) is set to 0.7-0.9, and the weight of intersection points in areas with low data density (such as crossline direction, the number of points in each 100m grid is <3) is set to 0.4-0.6, and the interference of sparse data on the fitting result is reduced through weight adjustment, and a cubic quadratic polynomial fitting function is constructed , where a, b, c, d, e, f are fitting parameters to be solved, x and y correspond to plane coordinates, and z corresponds to depth coordinate); input the three-dimensional coordinates (x, y, z) of the effective intersection points and the corresponding weight values into the fitting algorithm, the algorithm first quantitatively analyzes the distribution characteristics of the intersection points, calculates the average change rate of the depth z when the Inline direction x increases by 100m, and the continuous deviation value of the depth z when the crossline direction y increases by 100m, to verify the adaptability of the order of the fitting function (if the continuous deviation value is <0.3m, keep the quadratic term, if >0.3m, supplement the cubic term to improve the fitting accuracy); based on the distribution characteristic analysis result, the fitting parameters are solved by minimizing the weighted residual sum of squares (the residual is the difference between the actual depth z of the intersection point and the calculated depth z' of the fitting function), and a first derivative constraint condition is applied (the slope change of the fitting curve at adjacent intersection points does not exceed 0.08 rad), to avoid abrupt turning of the curve and ensure smooth transition between adjacent intersection points; through the above fitting operation, a three-dimensional boundary curve is generated through all effective intersection points, which is the optimized boundary line, and its spatial form can match the actual intersection boundary of the top and bottom interfaces of the geological envelope due to changes in lithology and the influence of small-scale faults, and the fitting degree is higher than that of the traditional unweighted fitting method.

[0121] Figure 9 is a schematic diagram of the address envelope boundary line after step 6 in a scenario. As Figure 9 shows, after the above step 6 processing, the top and bottom interface boundary lines (the outermost) are completely intersected. Figures 3-4

[0122] ​Optionally, step 7, based on the optimized boundary line, the target work area is cropped and closed to generate a geological envelope three-dimensional visualization model, specifically including:

[0123] Step 71, based on the optimized boundary line, the top interface boundary constraint and the bottom interface boundary constraint are screened and processed, and the grid points outside the intersection points are deleted to generate the cropped top and bottom interface effective data;

[0124] Step 72, the cropped top and bottom interface effective data is executed for data closure processing to generate effective geological envelope data;

[0125] Step 73, based on the effective geological envelope data, three-dimensional model rendering processing is performed to generate a geological envelope three-dimensional visualization model.

[0126] Preferably, the specific implementation process of step 71 is as follows: the optimized boundary line generated in step 6 is called as the cropping standard, and the top interface boundary constraint and the bottom interface boundary constraint constructed in step 5 are called; through a spatial geometric judgment algorithm, the spatial position of each grid point in the top interface boundary constraint and the bottom interface boundary constraint is judged to determine whether the grid point is located in the area surrounded by the optimized boundary line (i.e. whether it is a grid point inside the intersection point and inside); the grid point data outside the intersection point is deleted, and the grid point data inside the intersection point is reserved; the reserved grid point data is integrated to generate the cropped top and bottom interface effective data, which only contains the depth information of the actual range of the geological envelope, avoiding the interference of redundant data on subsequent processing.

[0127] Specifically, in a scene, the implementation of the above step 71 includes: calling the optimized boundary line generated in step 6 as the cropping standard, which contains the geometric shape parameters of the intersection area of the top and bottom interfaces of the geological envelope - if it is a polygon boundary, record the vertex coordinate set after smoothing , if the curve boundary is recorded high-precision fitting curve equation coefficients (such as cubic polynomial coefficients); while calling step 5 to build based on the top interface boundary constraints and the bottom interface boundary constraints, these two kinds of data respectively contain all the grid points of the target work area plane coordinates (x, y), corresponding depth value (z) and the boundary attribute mark ( "boundary" or "boundary") marked in the early stage; call the improved ray method space geometry judgment algorithm suitable for the geological grid scene (the technical essence of this algorithm is: for the grid characteristics of equal interval distribution of geological survey lines, the virtual ray direction is fixed as the direction parallel to the inline, avoiding the misjudgment caused by the traditional ray method because the ray direction is random and coincides with the boundary line vertex, edge line, at the same time, by precomputing the slope range of each side of the boundary line, the ray and edge line combination without intersection is quickly excluded, the judgment efficiency is improved); the algorithm executes spatial position judgment processing on each grid point in the top interface boundary constraint and the bottom interface boundary constraint, emits a virtual ray parallel to the inline direction from the grid point, counts the number of intersection points of the ray and each side of the optimized boundary line, if the number of intersection points is odd, the grid point is determined to be located in the area surrounded by the optimized boundary line (i.e. the grid points inside the intersection point of the top and bottom interfaces), if it is even, it is determined to be outside the area; according to the judgment result, delete the grid point data outside the intersection point (including the coordinates, depth value and attribute mark of the grid point), keep the grid point data inside the intersection point; classify and integrate the retained top interface grid point data and bottom interface grid point data according to the survey line index (inline number, crossline number), generate the clipped top and bottom interface effective data, which only contains the depth information and corresponding coordinates of the actual range of the geological envelope, which can avoid the interference of redundant external grid point data on subsequent data closure, three-dimensional modeling and other processing links, and reduce the operation load.

[0128] Preferably, in the specific technical implementation of step 72: taking the clipped top and bottom interface effective data generated in step 71 as the processing object, calling the data closure algorithm based on grid point connectivity; the algorithm identifies and fills the gap between the top and bottom interfaces caused by data sparseness or clipping (such as the area where the depth value of adjacent grid points jumps too much, the transition depth value is supplemented according to the trend) by analyzing the spatial position relationship and depth value continuity of the top and bottom interface grid points; ensure that the top and bottom interfaces are completely connected and closed at the optimized boundary line, forming a three-dimensional spatial data form without data breakage and complete form; after the closure processing, the effective geological envelope data with complete structure and continuous data is generated, providing a coherent data source for subsequent three-dimensional modeling. This technology is different from the traditional simple filling of fixed value, which fills the gap by supplementing the depth value according to the trend, so that the envelope form is more consistent with the actual geological structure.

[0129] Specifically, in a scenario, the implementation of the above step 72 includes: taking the cropped top and bottom interface effective data generated in step 71 as the processing object, which contains the plane coordinates (x, y) of the top interface effective grid points, the depth value (ztop), and the plane coordinates (x, y) of the bottom interface effective grid points, the depth value (zbottom), and the gaps caused by data sparseness or cropping due to the absence of grid points in some areas; calling a data closure algorithm based on grid point connectivity suitable for the geological scene, the technical essence of which is: combining the characteristics of the continuous change of the geological stratum depth with the direction of the measuring line, first calculating the coordinate distance (set ≤ 1.2 times the grid spacing as effective adjacent) and the depth difference (reference the target work area stratum thickness change rate to set a threshold, such as ≤ 5m for continuity) of the adjacent grid points (Inline direction and crossline direction adjacent) of the top interface and the bottom interface through "neighbor connectivity analysis", identifying the gap area (such as the area where the depth difference between Inline direction i and i+1 grid points in the top interface is 8m) with a depth difference exceeding the threshold or missing grid points; for these gap areas, the algorithm extracts the depth values of 3-5 effective grid points around the gap, fits the depth change trend through local weighted linear regression (the weight decreases with the increase of the distance from the gap point), and calculates and supplements the transition depth value of the grid point at the gap (such as the depth value of the top interface gap point is assigned according to the linear trend of the depth values of the effective points around it); at the same time, the algorithm verifies the connection of the top interface and the bottom interface grid points at the optimized boundary line to ensure that the depth value (ztop) of the top interface is always less than the depth value (zbottom) of the corresponding position of the bottom interface at the boundary line, and if there is a deviation, the depth value of the bottom interface is adjusted based on the normal thickness range of the stratum to ensure that the top interface and the bottom interface are completely connected and closed at the boundary line. After the above gap filling and connection verification, a three-dimensional spatial data form without data breaks and complete form is formed; finally, the effective geological envelope data with complete structure and continuous data is generated, which contains the coordinates and depth values of all effective grid points of the top interface and the bottom interface, providing a coherent data source for subsequent three-dimensional modeling. This technology is different from the traditional simple filling of fixed values, and the gap is filled by the trend of the depth value, making the envelope form more consistent with the continuous change rule of the stratum depth in the actual geological structure.

[0130] Preferably, in a scenario, when step 73 is specifically implemented: taking the effective geological envelope data generated in step 72 as the data source, calling the three-dimensional modeling tool commonly used in the field of geology (such as Paradiam, Surfer); input the effective geological envelope data to the modeling tool, and configure the rendering parameters (such as assigning different materials to different depth intervals, setting the transparency in line with the needs of geological observation, so as to clearly observe the internal structure); the modeling tool executes three-dimensional model rendering processing according to the parameters, and converts the discrete depth data into an intuitive three-dimensional shape; finally, a geological envelope three-dimensional visualization model is generated, which can be used in scenarios such as mineral resource exploration and geological disaster assessment, and the model supports export to common data formats such as SEGY and LAS in the field of geology, has high compatibility, and is convenient for subsequent linkage with other geological analysis tools.

[0131] Specifically, in a scenario, the implementation of the above step 73 includes: taking the effective geological envelope data generated in step 72 as the data source, which contains the three-dimensional coordinates (x, y, z) of all effective grid points of the top and bottom interfaces and the lithology-related identifier (such as the classification code of sandstone and mudstone) of the corresponding stratum, first performing format preprocessing on the effective geological envelope data, and converting it into a structured grid format (such as GRD format, where the row index corresponds to the inline number, the column index corresponds to the crossline number, and the cell value corresponds to the depth z) that can be recognized by a three-dimensional modeling tool (such as Paradiam, Surfer), ensuring that the data fields match the coordinates, depth, and attribute fields of the tool interface one by one; by calling the standardized API interface of the three-dimensional modeling tool to establish a communication connection, the preprocessed effective geological envelope data is transmitted to the modeling tool; at the same time, the rendering parameters suitable for the observation requirements of the geological scene are configured, specifically: dividing the levels according to the preset stratum depth intervals (such as 0-300m, 300-600m, 600-1000m) of the target work area, assigning differentiated material properties to each depth interval (light gray translucent material for loose sediment layer in the 0-300m interval, medium gray material for transition rock layer in the 300-600m interval, and dark gray material for dense bedrock in the 600-1000m interval), and setting the overall transparency parameter to 35%-45% (this interval can clearly present the outline of the envelope body external space, and can also observe the depth distribution and lithology boundary of the internal grid points through the surface layer); after the modeling tool receives the data and parameters, it starts the special surface rendering algorithm adapted to geological data. This algorithm, which is different from the traditional general surface rendering algorithm, adjusts the grid surface smoothness in combination with the lithology-related identifier in the effective geological envelope data - continuous areas with consistent lithology codes (such as pure sandstone section) are optimized by Bezier curve to make the surface shape smoother, and areas with sudden lithology code changes (such as sand-mudstone interface) retain slight edges to reflect geological interface features, and then accurately map the material and transparency properties in the rendering parameters to the corresponding grid surface; through this three-dimensional model rendering process, the modeling tool converts the discrete grid point depth data into an intuitive three-dimensional shape that fits the actual geological structure; finally, the modeling tool generates a geological envelope three-dimensional visualization model that can be used in scenarios such as mineral resource exploration (such as positioning the overlapping area of the envelope body and the ore body), geological hazard assessment (such as analyzing the contact relationship between the envelope body and the fault), etc. Further calling the format export module of the tool makes the model support export to common data formats (such as SEGY, LAS) in the geological field (these formats are compatible with the input formats of mainstream geological analysis tools such as GeoFrame and Landmark), which has high compatibility and facilitates subsequent direct import of the three-dimensional visualization model into other tools for reserve calculation, stability simulation, and other analyses, reducing redundant operations of data format conversion.

[0132] Figure 10 is a three-dimensional visualized model of the geological envelope obtained by the step 7 in an application scenario. As shown in Figure 10 , the geological envelope is intuitively presented in a three-dimensional visualized form for the application scenario shown in Figures 3-4 , which is convenient for the geologist to analyze and study.

[0133] Here, it should be noted that the above Figures 2-10 is only an exemplary description of the method shown in Figure 1 in combination with a specific application scenario, and the content embodied in the figures does not constitute a limitation on the present application.

[0134] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for automatically generating geological envelopes, characterized in that, include: Step 1: Define the target work area; Step 2 specifically includes: Step 21: Combining the seismic wave reflection characteristics in the seismic data, perform geological envelope layer identification processing based on the defined target work area to initially distinguish the layer range of the top and bottom interfaces of the suspected geological envelope, so as to generate the first layer picking data of the top and bottom interfaces of the geological envelope. Step 22: The first-layer data is verified and corrected by combining prior geological knowledge to determine the boundary characteristics of the top and bottom interfaces of the geological envelope, so as to generate the second-layer data of the top and bottom interfaces of the geological envelope. Step 23: Iteratively optimize the second-layer picking data based on the structural morphology and spatial distribution characteristics of the geological envelope to generate the third-layer picking data of the top and bottom interfaces of the geological envelope. Step 24: Perform sparse picking of the third layer data in both the longitudinal and transverse survey lines with equal intervals to determine the top interface layer, the bottom interface layer, and the corresponding top and bottom interface data. Step 3: Based on the grid positions corresponding to all pick points of the top and bottom interface layers, determine the boundary lines of the geological envelope in the target work area at the top and bottom interfaces according to the range of the top and bottom interface data. Step 4: Continuate the data of the top and bottom interfaces to determine the layer depth values ​​within all grid points of the top and bottom interfaces; Step 5: Based on the stratigraphic depth value and the boundary lines of the geological envelope at the top and bottom interfaces, construct the top interface boundary constraints and the bottom interface boundary constraints. Step 6: Based on the constructed top and bottom interface boundary constraints, determine the intersection points between the top and bottom interfaces of the envelope to optimize the boundary lines of the geological envelope at the top and bottom interfaces. Step 7: Based on the optimized boundary line, the target work area is trimmed and closed to generate a three-dimensional visualization model of the geological envelope.

2. The method according to claim 1, characterized in that, Step 1 specifically includes: Step 11: Extract spatial location information from the seismic data of the target work area to determine the coordinates of four points in the target work area. The coordinates of the four points are used to describe the geographic spatial range of the target work area. Step 12: Determine a rectangular area based on the four-point coordinates, and perform planar meshing on the rectangular area in combination with the defined surface element size to cut the rectangular area into multiple regular surface elements, so as to generate the meshed target work area grid plane. Step 13: Based on the position of the facet element in the grid plane of the target work area, define the coordinate position of the facet element and assign a unique identifier to it to generate facet element information with coordinates and identifier. The coordinate position of the facet element starts from the lower left corner of the rectangular area (x0, y0), and the coordinates of the facet element in the i-th row and j-th column are... Δx and Δy correspond to the length and width of the element, respectively; Step 14: Determine the minimum depth based on the horizontal plane of the highest point of the target work area, and determine the maximum depth based on the depth range of the exploration target, so as to obtain the depth data of the target work area. Step 15: Define the target work area based on the surface element information with coordinates and identification numbers and the target work area depth data.

3. The method according to claim 1, characterized in that, Step 3 specifically includes: Step 31: Based on the range of the top and bottom interface data, traverse the grid positions corresponding to all pick points in the top and bottom interface layers. Combine the pre-constructed geological structure knowledge and spatial analysis algorithm, use the grid positions as spatial anchor points, calculate the distribution density and continuity of the pick points in three-dimensional space, and perform three-dimensional spatial distribution range calculation processing to generate preliminary three-dimensional spatial distribution data of the geological envelope. This preliminary three-dimensional spatial distribution data is used to describe the position and shape of the geological envelope in three-dimensional space. Step 32: Based on the preliminary three-dimensional spatial distribution data, associate the mapping relationship between the grid positions and three-dimensional spatial coordinates of all pick points at the top and bottom interfaces, call the geological structure modeling tool, and combine the geological structure map and regional geological data of the target work area to mark the spatial position of all pick point grids in order to determine the spatial relationship between the geological envelope and the surrounding geological environment. Step 33: Based on the spatial relationship between the geological envelope and the surrounding geological environment, select the outermost pick point grid in the longitudinal and transverse survey directions to generate the initial boundary line. The initial boundary line represents the three-dimensional boundary of the geological envelope in the form of a polygon or a curve. Step 34: Verify whether the initial boundary line completely covers the distribution range of all picked point grids. If there are edge grid points that are not included, adjust the boundary line to include the edge grid points and perform boundary condition optimization to generate the boundary line of the geological envelope at the top and bottom interfaces. The edge grid points are the grid points that are spatially outermost among the grid positions corresponding to all picked points at the top and bottom interfaces.

4. The method according to claim 1, characterized in that, Step 4 specifically includes: Step 41: Perform interpolation on the top and bottom interface data to generate preliminary gridded data; Step 42: Extrapolate the initial gridded data to generate extended gridded data that extends to the entire target work area; Step 43: For the extended gridded data, determine the spatial range based on the boundary line, mark the coordinate points inside the boundary line as valid data, and mark those outside the boundary line as invalid data, so as to generate top and bottom interface layer data with validity labels. Step 44: Based on the top and bottom interface layer data with validity labels, extract the valid depth value of each grid point in the grid plane where the target work area is located and perform continuous integration processing to generate the layer depth value of all grid points in the top and bottom interfaces.

5. The method according to claim 4, characterized in that, Step 5 specifically includes: Step 51: Based on the stratigraphic depth value, and using the boundary lines of the geological envelope at the top and bottom interfaces, combined with the top interface stratigraphic data with validity labels, construct the top interface boundary constraints. Step 52: Based on the stratigraphic depth values, and using the boundary lines of the geological envelope at the top and bottom interfaces, combined with the bottom interface stratigraphic data with validity indicators, construct the bottom interface boundary constraints.

6. The method according to claim 5, characterized in that, Step 51 specifically includes: Step 511: Based on the boundary line, traverse and spatially determine the coordinate points of the top interface layer data with validity indicators in the grid plane of the target work area to generate top interface coordinate point data marked with boundary inside / outside attributes. This top interface coordinate point data is used to mark the position information of each coordinate point relative to the boundary line. Step 512: Based on the top interface coordinate point data marked with boundary inside / outside attributes, retain the corresponding top interface layer depth value for coordinate points inside the boundary line; assign the maximum depth value defined by the work area to coordinate points outside the boundary line to obtain the top interface depth data after boundary constraint assignment. Step 513: Perform data format standardization and integrity verification on the top interface depth data after boundary constraint assignment to generate the final top interface boundary constraints.

7. The method according to claim 5, characterized in that, Step 52 specifically includes: Step 521: Based on the boundary line, traverse and spatially determine the coordinate points of the bottom interface layer data with validity indicators in the grid plane of the target work area to generate bottom interface coordinate point data marked with boundary inside / outside attributes. The bottom interface coordinate point data marks the positional relationship between each coordinate point and the boundary line. Step 522: Based on the bottom interface coordinate point data marked with boundary inside / outside attributes, retain the corresponding bottom interface layer depth value for coordinate points inside the boundary line, and assign the minimum depth value defined by the work area to coordinate points outside the boundary line, so as to obtain the bottom interface depth data after boundary constraint assignment. Step 523: Perform data format standardization and integrity verification on the bottom interface depth data after boundary constraint assignment to generate the final bottom interface boundary constraints.

8. The method according to claim 1, characterized in that, Step 6 specifically includes: Step 61: Based on the boundary constraints of the top interface and the bottom interface, perform point-by-point comparison of the depth values ​​of all coordinate points of the top and bottom interface layers to generate a candidate set of intersection points of the top and bottom interfaces. This candidate set of intersection points of the top and bottom interfaces includes coordinate points with equal depth values ​​of the top and bottom interfaces as possible intersection positions of the top and bottom interfaces at the boundary. Step 62: Perform validity verification on the candidate set of intersection points of the top and bottom interfaces, remove abnormal intersection points caused by data errors, and obtain a valid set of intersection points of the top and bottom interfaces. This set of intersection points of the top and bottom interfaces includes the actual intersection positions of the top and bottom interfaces at the boundary. Step 63: For the effective set of intersection points of the top and bottom interfaces, refit the boundary curve in three-dimensional space to generate the optimized boundary line.

9. The method according to claim 1, characterized in that, Step 7 specifically includes: Step 71: Based on the optimized boundary lines, filter the top interface boundary constraints and bottom interface boundary constraints, and delete grid points other than the intersection points to generate the clipped effective data of the top and bottom interfaces. Step 72: Perform data closure processing on the effective data of the trimmed top and bottom interfaces to generate effective geological envelope data; Step 73: Perform 3D model rendering processing based on effective geological envelope data to generate a 3D visualization model of the geological envelope.

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