Geological envelope body automatic generation method

Abstract

The invention provides a geological envelope body automatic generation method. The method comprises the following steps: defining a target work area; performing sparse horizon picking of a top interface and a bottom interface on the geological envelope in the defined target work area to determine a top interface horizon, a bottom interface horizon and corresponding top interface and bottom interface data; based on the grid positions corresponding to all the pickup point positions of the top interface horizon and the bottom interface horizon, according to the range of the top interface data and the range of the bottom interface data, boundary lines of the geological envelope body at the top interface and the bottom interface in the target work area are determined; determining horizon depth values in all grid points of the top interface and the bottom interface; constructing a top interface boundary constraint and a bottom interface boundary constraint according to the horizon depth value and based on boundary lines of the geological envelope body at the top interface and the bottom interface to determine an intersection point between the top interface and the bottom interface of the envelope body so as to optimize the boundary lines of the geological envelope body at the top interface and the bottom interface; and generating a three-dimensional visualization model of the geological envelope based on the optimized boundary line.

Description

Technical Field

[0001] This invention relates to the fields of mineral resource exploration and data interpretation, and in particular to a method for automatically generating geological envelopes. Background Technology

[0002] In the field of geological exploration, the accurate identification and location of geological envelopes are of great significance for mineral resource exploration and geological hazard assessment. In mineral resource exploration, the enrichment of many metallic minerals is closely related to specific geological envelopes. Accurately determining their boundaries and extent can effectively narrow the exploration scope and improve exploration efficiency. In geological hazard assessment, the distribution and structure of geological envelopes affect the stability of strata. In-depth research on their characteristics helps to accurately assess the risk of disasters such as landslides and debris flows, providing a basis for the formulation of prevention and control measures.

[0003] Currently, the determination of geological envelopes in seismic exploration mainly relies on the manual identification of characteristic seismic waves on seismic profiles and the manual interpretation of the top and bottom interfaces. This process depends on geologists using their experience, combined with the characteristics of seismic wave reflection amplitude, phase, and frequency, to pick up the top and bottom interface layer data of the geological envelope point by point on the seismic profile. For the sparse layer data picked up, simple interpolation or extrapolation methods are usually used to process the data to construct a preliminary geological envelope morphology.

[0004] However, existing technical solutions have significant shortcomings. Due to complex geological structures, large lateral variations in lithology, and low signal-to-noise ratios in seismic data, manually picked top and bottom interface strata often exhibit non-intersection, parallelism, or reverse intersection at the boundaries. To ensure intersection at the boundaries, manual point-by-point modifications are required, which not only consumes substantial manpower and time, resulting in low efficiency, but is also susceptible to subjective influences, leading to inconsistent results among different personnel. This makes it difficult to guarantee the accuracy of the geological envelope morphology, severely restricting the reliability of subsequent geological analysis and decision-making. Summary of the Invention

[0005] The main objective of this invention is to provide an automatic method for generating geological envelopes, so as to overcome or alleviate the above-mentioned problems in the prior art.

[0006] This invention provides a method for automatically generating geological envelopes, comprising: Step 1: Define the target work area; Step 2: Pick the sparse stratigraphic positions of the top and bottom interfaces of the geological envelope within the defined target work area to determine the top interface stratigraphic position, bottom interface stratigraphic position, 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.

[0007] (III) Beneficial Effects 1. Significantly reduces labor costs and time consumption, and substantially improves work efficiency. In existing technologies, due to complex geological structures and other factors, manually selected top and bottom interface strata are prone to problems such as non-intersection, parallelism, or reverse intersection at the boundaries, requiring manual point-by-point modifications, which consumes a lot of manpower and time and is inefficient. This application reduces the initial manual operation by picking sparse strata, and then automates the entire process of determining boundary lines, constructing boundary constraints, optimizing boundary lines, and trimming and closing them, directly replacing the traditional manual point-by-point modification work. It eliminates the need for manual intervention in the boundary correction process, effectively avoiding a large consumption of manpower and time, and significantly improving the efficiency of geological envelope generation.

[0008] 2. Avoid interference from subjective factors and improve the consistency of processing results. Existing technologies rely on the experience of geological engineers to manually select stratigraphic horizons and modify boundaries, which is susceptible to subjective judgment and results in inconsistent outcomes among different personnel. In this application's technical solution, from defining the target work area based on seismic data, to selecting stratigraphic horizons and determining boundary lines, and then to constructing constraints, optimizing boundaries, and generating the model, all processes are completed through unified algorithmic logic and preset constraints, free from interference from personal experience and subjective judgment. Even if different personnel use this method to process the same seismic data, they can obtain convergent results, effectively solving the problem of poor consistency in existing technologies and ensuring the objectivity of the processing.

[0009] 3. Address boundary layer anomalies to improve the accuracy of geological envelope morphology and the reliability of subsequent decision-making. In existing technologies, manually selected top and bottom interface strata often exhibit anomalies at the boundaries, such as non-intersection, parallelism, or reverse intersection. Simple interpolation / extrapolation is insufficient to correct these anomalies, leading to insufficient accuracy in the geological envelope morphology and hindering the reliability of subsequent geological analysis and decision-making. This application addresses this problem through a multi-step collaborative approach: Step 5 establishes boundary lines at the top and bottom interfaces, clarifying the initial boundary range to reduce anomalies caused by disordered extrapolation; Step 6 constructs boundary constraints, standardizing data processing logic inside and outside the boundaries to avoid stratum deviation; Step 7 optimizes the boundary lines by determining the intersection points of the top and bottom interfaces, directly correcting issues such as non-intersection, parallelism, or reverse intersection; and finally, Step 7, based on the optimized boundary lines, trims and closes the boundary to ensure accurate intersection of the top and bottom interfaces at the boundaries, forming 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 foundation for narrowing the scope of subsequent mineral resource exploration and accurately identifying risks in geological hazard assessment, thereby improving the reliability of subsequent geological analysis and decision-making. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating an automatic geological envelope generation method according to an embodiment of this application.

[0011] Figure 2 This is a schematic diagram illustrating the definition of the target work area in an embodiment of this application.

[0012] Figure 3 This is a schematic diagram of the vertical angles of the top and bottom interface layers obtained after step 2 in a scene.

[0013] Figure 4 This is a schematic diagram of the horizontal angles of the top and bottom interface layers obtained after step 2 in a scene.

[0014] Figure 5 This is a schematic diagram of the boundary line obtained after step 3 in a specific scenario.

[0015] Figure 6 This is a planar schematic diagram of valid and invalid data on the top and bottom interfaces in an application scenario.

[0016] Figure 7 This is a 3D schematic diagram of valid and invalid data on the top and bottom interfaces in an application scenario.

[0017] Figure 8 This is a 3D schematic diagram of the connection between the top and bottom interfaces in an application scenario.

[0018] Figure 9 This is a schematic diagram of the address envelope boundary line after step 6 in a scene.

[0019] Figure 10This is a 3D visualization model of the geological envelope obtained after step 7 in an application scenario. Detailed Implementation

[0020] Figure 1 This is a schematic flowchart illustrating an automatic geological envelope generation method according to an embodiment of this application. Figure 1 As shown, it includes: Step 1: Define the target work area; Step 2: Pick the sparse stratigraphic positions of the top and bottom interfaces of the geological envelope within the defined target work area to determine the top interface stratigraphic position, bottom interface stratigraphic position, 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.

[0021] Figure 2 This is a schematic diagram illustrating the definition of the target work area in an embodiment of this application, combined with... Figure 2 An exemplary implementation of step 1 is described below.

[0022] Step 1: Define the target work area, specifically including: 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.

[0023] See also Figure 2 Preferably, in the specific technical implementation of step 11: the seismic data of the target work area is used as the core processing object, and spatial location information including the coordinates of the endpoints of the survey line and coordinate system parameters is extracted from it to obtain the initial spatial location dataset of the target work area; the initial spatial location dataset is subjected to information analysis and boundary fitting processing, and the coordinates of four vertices that can completely cover the geographic spatial range of the target work area are calculated by the fitting algorithm; the coordinates of these four vertices are presented in the form of rectangles to form the basic framework data describing the planar boundary of the target work area, providing a benchmark for the subsequent work area range definition.

[0024] Information parsing and boundary fitting were performed on the initial spatial location dataset. A fitting algorithm was used to calculate the coordinates of four vertices that could completely cover the geographic space of the target work area. Specifically, this involved: performing information parsing on the initial spatial location dataset to extract the planar coordinate values ​​(including x and y coordinates) of all survey line endpoints, obtaining a set of survey line endpoint coordinates; separating all x coordinate values ​​from the set of survey line endpoint coordinates, and using an extreme value calculation algorithm to select the maximum and minimum x coordinate values; simultaneously separating all y coordinate values ​​and selecting the maximum and minimum y coordinate values; combining the maximum x and y coordinate values ​​to form the first vertex coordinates, combining the maximum x and y coordinate values ​​to form the second vertex coordinates, combining the minimum x and y coordinate values ​​to form the third vertex coordinates, and combining the minimum x and y coordinate values ​​to form the fourth vertex coordinates; verifying the coverage of the rectangular area formed by these four vertex coordinates to confirm that the rectangle could completely contain all coordinate points in the set of survey line endpoint coordinates, ultimately obtaining the coordinates of four vertices that could completely cover the geographic space of the target work area.

[0025] See also Figure 2Preferably, in a scenario, step 12 is specifically implemented as follows: using the coordinates of the four vertices determined in step 11 as reference data, a rectangular area data that can completely contain the target work area is delineated through range calculation; the rectangular area data is associated with the surface element size parameters defined by the field observation system (the size in the x-direction is Δx, and the size in the y-direction is Δy, where Δx and Δy are the smallest size units for observing underground objects in geological exploration); for the rectangular area data with surface element size parameters, starting from the coordinates of its lower left corner, a planar gridding operation is performed sequentially along the x-direction at intervals of Δx and along the y-direction at intervals of Δy, cutting the rectangular area into multiple regular surface element data; these regular surface element data are integrated to generate gridded planar data of the target work area, providing a grid carrier for subsequent surface element positioning.

[0026] Preferably, the specific implementation process of step 13 is as follows: Taking each facet element in the grid plane data of the target work area generated in step 12 as a processing unit, first obtain the row and column position parameters of each facet element in the grid plane (denoted as the i-th row and j-th column, where i and j are both positive integers, i represents the arrangement number along the y-direction of the grid plane, and j represents the arrangement number along the x-direction of the grid plane); substitute the row and column position parameters together with the coordinates (x0, y0) of the starting corner point of the lower left corner of the rectangular area of ​​the grid plane into the preset coordinate calculation formula. The calculation is performed to obtain and define the coordinate position data of each facet element. For the coordinate position data of each facet element, a unique identifier is assigned. The identifier adopts the numbering rule of inline in the row direction and crossline in the column direction (the identifier of the facet element corresponding to the starting corner point is set to (1,1), and the identifier of the facet element in the i-th row and j-th column is set to (i,j)). The facet data with coordinate position data and unique identifier are integrated to generate a facet information dataset with coordinates and identifiers. This dataset can support the rapid positioning and identification of specific facet elements in subsequent steps.

[0027] Preferably, in the specific technical implementation of step 14: the surface elevation dataset of the target work area is used as the processing object, and data collection and filtering are performed. The highest elevation point data is extracted by elevation comparison. The horizontal plane corresponding to the highest elevation point data is used as the reference plane data. Combined with the requirements of the initial exploration range below the surface in geological exploration, the minimum depth data of the target work area is determined. The depth range data related to the exploration target (such as deep oil and gas reservoir) in geological exploration is obtained, and the maximum value of the depth range is selected as the maximum depth data of the target work area. The minimum depth data and the maximum depth data are integrated to form a target work area depth dataset containing the vertical exploration range of the target work area, providing a depth reference for subsequent three-dimensional spatial definition.

[0028] Specifically, in one scenario, the above steps include: using the surface elevation dataset of the target work area as the processing object, performing data collection and filtering processing, removing abnormal elevation values ​​(such as unreasonable values ​​caused by measurement errors), and obtaining filtered surface elevation data; performing elevation comparison calculations on the filtered surface elevation data, and extracting the elevation data with the largest value as the highest elevation point data; using the horizontal plane corresponding to the highest elevation point data as the reference plane data, calling the preset initial exploration range parameters below the surface in geological exploration (such as the initial exploration starting 50 meters below the reference plane), and calculating the minimum depth data of the target work area by the difference between the reference plane data and the initial exploration range parameters; obtaining the depth range data related to the exploration target (such as deep oil and gas reservoirs) recorded in geological exploration, performing maximum value extraction processing on the depth range data, and using 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 a target work area depth dataset containing the vertical exploration range of the target work area, which provides a clear depth reference standard for subsequent three-dimensional spatial definition.

[0029] Preferably, in one scenario, step 15 is specifically implemented as follows: First, the surface element information dataset with coordinates and identifiers generated in step 13 is called, and it 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, identifiers, and depth range of each surface element; based on this correspondence, by combining the planar coordinate data and depth range data of the surface elements, spatial definition standard data covering the three dimensions of x, y, and z of the target work area is constructed (x and y correspond to planar coordinates, and z corresponds to depth range); the spatial range of the target work area is defined using this three-dimensional spatial definition standard data, and finally the complete definition of the target work area in three-dimensional space is completed. The resulting three-dimensional spatial definition data can directly lay the spatial foundation for the subsequent three-dimensional model construction of the geological envelope.

[0030] Specifically, in one scenario, the implementation of the above steps includes: calling the element information dataset with coordinates and identifiers generated in step 13, which contains the planar coordinates (x, y) and unique identifier of each element; simultaneously retrieving the target work area depth dataset determined in step 14, which contains the minimum and maximum depth values ​​of the target work area; performing association matching processing between the element information dataset and the depth dataset through identifier indexing, binding the planar coordinates (x, y) and identifier of each element to the corresponding depth range (from the minimum depth value to the maximum depth value), establishing a one-to-one correspondence between the coordinates, identifier, and depth range of each element; based on this correspondence, assigning the planar coordinates of each element to the depth range of the target work area. The data (x, y) are numerically combined with the bound depth range data (z) to form spatial data units containing three-dimensional parameters: x, y, and z. These spatial data units are then integrated to construct spatial definition standard data covering the target work area in the three dimensions of x, y, and z (where x and y correspond to the planar coordinate range, and z corresponds to the depth range). Using this three-dimensional spatial definition standard data, the spatial range of the target work area is numerically defined, clarifying the coordinate boundaries of the target work area in the x-axis and y-axis directions and the depth boundary in the z-axis direction. Finally, the complete definition of the target work area in three-dimensional space is completed, and the resulting three-dimensional spatial definition data can directly provide a clear spatial framework foundation for the subsequent construction of the three-dimensional model of the geological envelope.

[0031] Optionally, step 2 involves sparse stratigraphic picking of the top and bottom interfaces of the geological envelope within the defined target work area to determine the top and bottom interface stratigraphic positions and the corresponding top and bottom interface data. This 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 processing of the third layer data in both the longitudinal (inline) and transverse (crossline) directions using equally spaced control lines to determine the top interface layer, bottom interface layer, and the corresponding top and bottom interface data.

[0032] Preferably, in the specific technical implementation of step 21: using the seismic data of the defined target work area as the processing object, the seismic wave reflection characteristic data (including characteristic parameters such as amplitude, phase, and frequency) is extracted from it through a seismic data processing algorithm; these seismic wave reflection characteristic data are associated and matched with the defined 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 stratigraphic identification model, and the stratigraphic range data of the suspected top and bottom interfaces of the geological envelope are initially distinguished; the stratigraphic range data is structured to generate the first stratigraphic picking data of the top and bottom interfaces of the geological envelope.

[0033] Specifically, in one scenario, the implementation of the above steps includes: using the seismic data of the defined target work area as the processing object, calling a seismic data processing algorithm (this algorithm is based on the principle that seismic waves will generate reflection signals at the interfaces of different lithological strata, and through filtering, denoising, and wavefield separation of the seismic data, it highlights the effective reflection signals, 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; and associating and matching these seismic wave reflection characteristic data with the defined data of the target work area (including the grid plane coordinate range and vertical depth interval) through spatial coordinate mapping, so that each reflection characteristic parameter corresponds to the three-dimensional grid of the target work area. The specific location is determined; based on the associated seismic wave reflection characteristic data, the stratigraphic identification model is invoked (this model learns the reflection characteristic templates of the known top and bottom interfaces of geological envelopes, and uses a feature similarity comparison algorithm to match the extracted reflection features with the templates, identifying areas where the reflection features are continuous and conform to the characteristics of the envelope interface), and the reflection interfaces that may belong to the geological envelope are identified from the seismic data of the target work area, and the stratigraphic range data of the suspected top and bottom interfaces of the geological envelope are initially distinguished (including the distribution location of the interface in the grid and the corresponding depth); the stratigraphic range data is structured (such as sorting by grid coordinates and supplementing missing location markers) to generate the first stratigraphic picking data of the top and bottom interfaces of the geological envelope.

[0034] Preferably, in one scenario, step 22 is specifically implemented as follows: using the first stratigraphic picking data generated in step 21 as the input object, a preset geological prior knowledge database (containing data such as regional geological structural characteristics and stratigraphic distribution patterns) is called; based on the geological prior knowledge database, the stratigraphic boundaries in the first stratigraphic picking data are verified, with a focus on verifying the seismic wave amplitude variation characteristic data corresponding to the picking stratigraphic position; abnormal stratigraphic data caused by seismic data noise or identification deviation are corrected to clarify the true boundary characteristic data of the top and bottom interfaces of the geological envelope; the corrected boundary characteristic data are integrated to generate the second stratigraphic picking data of the top and bottom interfaces of the geological envelope.

[0035] Specifically, in one scenario, the implementation of the above steps includes: using the first stratigraphic picking data generated in step 21 (including stratigraphic location data of the top and bottom interfaces of the suspected geological envelope and corresponding seismic wave amplitude variation characteristic data) as input, and simultaneously calling a preset geological prior knowledge database (this database stores geological structural characteristic data of the target work area, such as stratigraphic dip angle, fault strike parameters, and stratigraphic distribution data, such as typical seismic wave amplitude variation intervals corresponding to different lithological interfaces); based on the structural characteristic data such as stratigraphic dip angle and fault strike in the geological prior knowledge database, performing preliminary verification of the stratigraphic boundary positions in the first stratigraphic picking data, determining whether the extension direction of the stratigraphic boundary is consistent with the regional structural trend, and then, combined with the typical seismic wave amplitude variation intervals of different lithological interfaces in the database, focusing on verifying whether the seismic wave amplitude variation characteristic data corresponding to the picked stratigraphic position is within the range of the stratigraphic boundary. Within the reasonable amplitude range of the lithological interface; for stratigraphic data with abrupt amplitude changes due to seismic data noise, or stratigraphic data inconsistent with the regional tectonic trend due to stratigraphic identification deviation, normal stratigraphic data and seismic wave amplitude data of adjacent effective grid points on the same survey line are used as references. The position parameters of the abnormal stratigraphic data are adjusted by linear trend interpolation algorithm, and the corresponding amplitude change characteristic data are corrected to complete the correction processing of the abnormal stratigraphic data. The true boundary characteristic data of the top and bottom interfaces of the geological envelope are clarified (including the corrected stratigraphic position and the seismic wave amplitude change characteristics that conform to the regional geological laws). The corrected true boundary characteristic data of the top interface and the true boundary characteristic data of the bottom interface are associated and integrated according to the grid coordinates of the target work area to ensure that the position identifier of each layer of data corresponds one-to-one with the grid coordinates, and finally the second stratigraphic picking data of the top and bottom interfaces of the geological envelope are generated.

[0036] Preferably, the specific implementation process of step 23 is as follows: Based on the second-layer picking data obtained in step 22, the structural morphology parameters (such as lenticular, layered, and other morphological feature data) and spatial distribution feature parameters (such as lateral extension length, longitudinal thickness change rate, and other data) of the geological envelope are called; the second-layer picking data is iteratively optimized based on these parameters, and after each round of optimization, the current layer data is compared with the layer data of adjacent survey lines to ensure consistency, and the layer data with large deviations are adjusted; after multiple rounds of iterative optimization, the fit between the picking results and the actual morphology of the geological envelope is improved, and finally, the third-layer picking data of the top and bottom interfaces of the geological envelope are generated.

[0037] Specifically, in one scenario, the implementation of the above steps includes: using the second-layer picking data obtained in step 22 (including the layer position coordinates and corresponding depth values ​​of the top and bottom interfaces of the geological envelope) as the basic data, and simultaneously calling the structural morphological parameters of the geological envelope (such as the curvature range corresponding to lenticular shapes, the horizontal extension angle corresponding to layered shapes, and other morphological feature data) and spatial distribution feature parameters (such as the maximum threshold of the lateral extension length, the reasonable range of the longitudinal thickness change rate, etc.); based on these structural morphological parameters and spatial distribution feature parameters, performing iterative optimization processing on the second-layer picking data, in the first round of optimization, comparing the layer data with the lenticular or layered morphological features through a morphological matching algorithm, and marking the layer positions that deviate from the morphological parameters; after each round of optimization... The process involves extracting the stratigraphic coordinates along the inline of the current stratigraphic data and comparing them with those of adjacent inline stratigraphic lines. The spatial distance deviation between the two is calculated. When the deviation exceeds a preset threshold, the stratigraphic coordinates and corresponding depth values ​​of the significantly deviated stratigraphic lines are adjusted based on the lateral extension length threshold and longitudinal thickness variation rate range in the spatial distribution characteristic parameters. After multiple rounds of iterative optimization (e.g., 3-5 rounds), the morphology of the stratigraphic data gradually conforms to the structural morphology parameters, and the stratigraphic data deviation between adjacent stratigraphic lines remains at a low level, improving the fit between the picked results and the actual morphology of the geological envelope. Finally, a third stratigraphic picking data is generated, integrating the optimized top and bottom interface stratigraphic positions and depth values ​​of the geological envelope.

[0038] Preferably, in the specific technical implementation of step 24: taking the third-layer picking data generated in step 23 as the operation object, control line data are set in the longitudinal (Inline) direction and the transverse (crossline) direction according to preset interval parameters (e.g., when corresponding to a 15m × 30m grid, the Inline direction is spaced at 30 survey lines and the crossline direction at 15 survey lines); on each control line data, the top and bottom positions of the geological envelope are located by the seismic wave amplitude feature recognition algorithm, the top interface layer data is picked at the top position and the bottom interface layer 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 are summarized and organized, and the top interface layer, bottom interface layer, and corresponding top interface data and bottom interface data of the geological envelope are finally determined through sparse picking processing.

[0039] Specifically, in one scenario, the implementation of step 24 includes: using the third-layer picking data generated in step 23 (including the optimized layer position coordinates and corresponding depth values ​​of the top and bottom interfaces of the geological envelope) as the operation object, setting a preset interval parameter according to the grid size parameters of the target work area (such as a 15m × 30m cell size)—with 30 survey lines as the interval in the longitudinal (inline) direction and 15 survey lines as the interval in the transverse (crossline) direction, filtering out survey lines that meet the interval requirements through the survey line number index, and generating control line data covering the target work area (each control line contains continuous grid points along the survey line direction and covers the depth detection range of the target work area); for each control line data, calling the seismic wave amplitude feature recognition algorithm (this algorithm is based on the principle that the lithological differences between the top and bottom interfaces of the geological envelope and the surrounding strata will cause significant abrupt changes in the seismic wave reflection amplitude, first extracting the ground plane values ​​of all grid points along the control line). The seismic wave amplitude is calculated and an amplitude variation curve is generated. Then, the abrupt change location in the curve is detected by using a preset amplitude abrupt change threshold (such as an amplitude difference exceeding 30% of the average of adjacent grid points). This abrupt change location is the top or bottom position of the geological envelope. At the located top abrupt change location, the stratigraphic information (including plane coordinates and depth values) of the corresponding grid points is extracted as the top interface stratigraphic data. At the bottom abrupt change location, the stratigraphic information of the corresponding grid points is extracted as the bottom interface stratigraphic data. The coordinate information (x-coordinate, y-coordinate) and depth value information of each pick point on each control line are recorded simultaneously to form a single control line pick point dataset. The pick point datasets of all control lines are summarized and sparsely picked (only the pick point data at the control lines are retained, and redundant stratigraphic data in non-control line areas are removed) to finally determine the top and bottom interface stratigraphics of the geological envelope containing complete pick point coordinates and depth information, as well as the corresponding top and bottom interface data.

[0040] In a specific scenario, Figure 3 This is a schematic diagram showing the vertical angles of the top and bottom interface layers obtained after step 2 in a scene. Figure 4 This is a schematic diagram of the lateral angles of the top and bottom interface layers obtained after step 2 in a scene, as shown below. Figure 3 , 4 As shown, the top interface layer is denoted by T, and the bottom interface layer is denoted by B. Figure 4 In this context, the color bars represent depth values.

[0041] Optionally, step 3, based on the grid positions corresponding to all picked points at the top and bottom interfaces, determines 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, specifically including: 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 approximate location 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.

[0042] Preferably, the specific implementation process of step 31 is as follows: Taking the range of the top and bottom interface data as the initial processing object, the grid positions corresponding to all pick points in the top and bottom interface layers are traversed through the grid traversal program to obtain the grid position dataset corresponding to all pick points in the top and bottom interface layers; this grid position dataset is used as the spatial anchor point data for three-dimensional spatial analysis, and a pre-built geological structure knowledge database (containing data such as fault distribution patterns and stratigraphic continuity characteristics) and a preset spatial analysis algorithm (such as a grid cell continuity determination algorithm) are called to perform calculations on the spatial anchor point data to calculate the distribution density data of the pick points in three-dimensional space and the connectivity data of adjacent grid cells; the distribution density data and connectivity data are input into the three-dimensional spatial range calculation module to perform three-dimensional spatial distribution range calculation processing, generating preliminary three-dimensional spatial distribution data to describe the approximate location and shape of the geological envelope in three-dimensional space. This processing method differs from the traditional method that relies solely on manual estimation of the distribution range. By combining geological structure knowledge, it reduces the interference of faults and lithological changes on the distribution range judgment, making the preliminary distribution data more consistent with the actual geological conditions.

[0043] Specifically, in one scenario, the implementation of step 31 above includes: The initial processing object is the range of the top and bottom interface data, which includes the grid coordinate intervals of all picked layers in the inline and crossline directions. The grid traversal program is called to traverse the grid positions corresponding to all picked points in the top and bottom interface layers one by one, following the grid index order of "inline direction first, then crossline direction". 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 picked point are recorded. All recorded three-dimensional grid coordinates are integrated into a structured dataset to obtain the grid position dataset corresponding to all picked points in the top and bottom interface layers. This dataset ensures that it covers all picked layer grid points without duplication.

[0044] The dataset of grid locations corresponding to all pick points at the top and bottom interfaces is used as spatial anchor point data for 3D spatial analysis. A pre-built geological structure knowledge database is invoked—this database stores fault distribution data (such as fault strike and fault drop threshold) and stratigraphic continuity characteristic data (such as the upper limit of the thickness variation rate of the same stratum) for the target work area. The spatial anchor point data is associated with the fault locations and stratigraphic parameters in the database to mark valid spatial anchor points located outside the fault influence zone that meet the stratigraphic continuity requirements. At the same time, a preset grid cell continuity determination algorithm is invoked. The technical essence of this algorithm is to calculate the difference in depth between adjacent valid spatial anchor points. If the difference is less than the upper limit of the stratigraphic thickness variation rate, it is determined to be connected; otherwise, it is determined to be disconnected. Based on the valid spatial anchor points and the algorithm, the spatial anchor point data is processed to count the number of valid spatial anchor points within a unit 3D grid volume to obtain the distribution density data of the pick points in 3D space. The connectivity status of adjacent valid spatial anchor points is recorded to obtain the connectivity data of adjacent grid cells (using Boolean values ​​"1" to indicate connectivity and "0" to indicate disconnection).

[0045] The distribution density data of the picked points in 3D space and the connectivity data of adjacent grid cells are used as input to the 3D spatial range calculation module. The technical principle of this module is to define the range by combining density threshold and connectivity rules: First, a distribution density threshold is set (determined based on the average anchor point density of the strata in the target work area; areas below the threshold are judged as non-envelope areas), and high-density anchor point clusters with a density higher than the threshold are selected. Then, based on the connectivity data, continuous areas with a connectivity state of "1" in the high-density anchor point clusters are retained, and isolated low-density anchor points are removed. By calculating the coordinate extreme values ​​(maximum and minimum values) of the continuous area in the Inline direction, Crossline direction, and Depth direction, the 3D coordinate boundary of the area is determined. The 3D spatial distribution range calculation process is performed to integrate the 3D coordinate boundary with the approximate outline of the continuous area, generating preliminary 3D spatial distribution data to describe the approximate location and shape of the geological envelope in 3D space. This data includes information such as the 3D coordinate range of the envelope and the anchor point distribution density of the core area.

[0046] This processing method differs from the traditional approach that relies solely on manual estimation of the distribution range. Traditional methods are prone to overlooking the impact of faults and lithological variations on anchor point distribution due to differences in human experience, leading to significant deviations in range estimation. In contrast, this method, by linking to a geological structure knowledge database during the calculation process, pre-marks effective spatial anchor points outside the fault-affected area, reducing the interference of faults and lithological variations on the determination of the distribution range. At the same time, by quantifying density and connectivity through algorithms, it avoids the bias of subjective human judgment, making the generated preliminary three-dimensional spatial distribution data more closely match the actual geological conditions of the target work area, providing more reliable basic data for subsequent boundary line determination.

[0047] Preferably, in a scenario, step 32 is specifically implemented as follows: Based on the preliminary three-dimensional spatial distribution data generated in step 31, input data is used. A coordinate mapping algorithm is used to establish the mapping relationship between the grid positions corresponding to all pick points of the top and bottom interfaces and the three-dimensional spatial coordinates (x-coordinate, y-coordinate, depth value), resulting in a grid position-three-dimensional coordinate mapping relationship table. A geological structure modeling tool (such as professional seismic interpretation software) is called to import the geological structure map data of the target work area (including stratigraphic interface strike and fault location markings) and regional geological data (such as stratigraphic lithology and tectonic movement period data) into the modeling tool. Based on the grid position-three-dimensional coordinate mapping relationship table, the spatial position of each pick point grid is marked one by one in the three-dimensional coordinate system of the modeling tool, forming a spatial position marking dataset. The spatial position marking dataset is integrated and analyzed. By comparing the relative positions of the grid positions with the surrounding strata and faults, the spatial relationship data between the geological envelope and the surrounding geological environment is determined.

[0048] Preferably, in the specific technical implementation of step 33: using the spatial relationship data between the geological envelope and the surrounding geological environment determined in step 32 as the judgment basis, an outer perimeter filtering operation is performed on the grid cells of the top interface layer and the bottom interface layer respectively; firstly, each control line in the longitudinal (Inline) direction is traversed, and the outermost picking point grid in the longitudinal direction is selected according to the spatial distance and connectivity between the grid cell and the adjacent strata, to obtain the outer perimeter grid dataset in the longitudinal direction; then, each control line in the crossline direction is traversed, and the outermost picking point grid in the crossline direction is selected using the same filtering logic, to obtain the outer perimeter grid dataset in the crossline direction; the grid points of the outer perimeter grid dataset in the longitudinal direction and the outer perimeter grid dataset in the crossline direction are connected in a three-dimensional spatial distribution order (such as clockwise or counterclockwise) through curve fitting or polygon connection algorithm to form an outline, generating an initial boundary line representing the three-dimensional boundary of the geological envelope in the form of a polygon or curve.

[0049] Preferably, the specific implementation process of step 34 is as follows: Taking the initial boundary line generated in step 33 as the verification object, the boundary coverage verification algorithm is called to compare the spatial range of the initial boundary line with the grid position data corresponding to all picked points in the top and bottom interface layers to obtain the boundary coverage verification result; if the verification result shows that there are edge grid points (i.e., the grid points on the outermost spatial distribution among all picked points in the top and bottom interface layers) not included by the initial boundary line, the boundary adjustment algorithm is called to calculate the adjustment parameters of the boundary line according to the three-dimensional coordinates (x, y, depth) of the edge grid points, and adjust the direction of the initial boundary line to include the edge grid points to obtain the adjusted boundary line; the adjusted boundary lines of the top and bottom interface layers are overlaid and analyzed, the outermost grid points of the overlaid spatial range are picked for contour integration, boundary condition optimization processing is performed to eliminate local irregular deviations of the boundary line, and the optimized boundary line is generated. This optimization process automatically adjusts the boundary line by algorithm instead of traditional manual correction, which not only reduces manpower consumption, but also maintains a high degree of consistency between the boundary line and the picked points.

[0050] Figure 5 This is a schematic diagram of the boundary line obtained after step 3 in a specific scenario. For example... Figure 5 As shown, for Figures 3-4 The top and bottom interfaces are processed in step 3 above to obtain clear boundary lines.

[0051] Optionally, step 4 involves making the data of the top and bottom interfaces continuous to determine the layer depth values ​​within all grid points of the top and bottom interfaces, specifically including: 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.

[0052] Preferably, the specific implementation process of step 41 is as follows: Using the top and bottom interface data obtained in step 2 as the initial processing objects, this data only contains depth values ​​for grid points on equally spaced control lines in the longitudinal and transverse directions; the depth data of grid points in non-control line areas is sparsely distributed. Based on the distribution characteristics of this top and bottom interface data (such as data density and spatial uniformity), a suitable interpolation algorithm (such as distance-weighted interpolation or Kriging interpolation) is automatically matched and invoked. The interpolation algorithm is used to perform depth value filling operations on the sparse non-control line grid points, supplementing the depth data of grid points in non-control line locations. Finally, preliminary gridded data covering part of the target work area is generated. This technology differs from the traditional fixed single interpolation algorithm approach. By dynamically selecting the algorithm based on data distribution characteristics, it can reduce depth value deviations caused by algorithm mismatch, maintaining a high level of accuracy for the preliminary gridded data.

[0053] Specifically, in one scenario, the implementation of step 41 includes: using 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 as follows: grid points on equally spaced control lines in the longitudinal (Inline) and transverse (Crossline) directions all contain corresponding depth values ​​(z values), while grid points in non-control line areas are not picked up and only retain coordinate information (x, y), resulting in a sparse distribution of depth data; performing distribution feature analysis on the top and bottom interface data, calculating the number of control points with depth values ​​within a unit grid area to obtain data density, and statistically analyzing the standard deviation of the difference in depth values ​​between adjacent control points to obtain spatial uniformity; based on the analysis results of data density and spatial uniformity, automatically matching interpolation algorithms—when the data density is low and the spatial uniformity is high (small standard deviation of difference), matching distance-weighted interpolation method (the technical essence of this algorithm is to match the interpolation point with the surrounding area) is used. Weights are assigned based on the distance to known control points, with closer points receiving greater weights. The depth of the interpolation point is calculated using a weighted average. When data density is high and spatial uniformity is low (with a clear trend in depth variation), a matching Kriging interpolation method is used. (The essence of this algorithm is to construct a variogram based on the spatial correlation of known points, estimate the spatial correlation between the interpolation point and known points, and then calculate the depth value). A matching interpolation algorithm is then called to perform depth value filling operations on sparse grid points in non-control line areas. Using each non-control line grid point as the center, the depth values ​​of control line grid points within a preset range are searched as known data and substituted into the algorithm formula to calculate the depth value of that non-control line grid point. The filled non-control line grid point depth values ​​are then integrated with the original control line grid point depth values ​​to generate preliminary gridded data covering a portion of the target work area (including control lines and adjacent non-control line areas). This technique differs from traditional fixed single interpolation algorithms by dynamically selecting a more suitable algorithm based on data distribution characteristics. This reduces depth value deviations caused by mismatches between the algorithm and data characteristics, maintaining a high level of accuracy in the preliminary gridded data.

[0054] Preferably, in one scenario, step 42 is specifically implemented as follows: using the preliminary gridded data generated in step 41 as the input object, a preset extrapolation algorithm (such as a trend extrapolation algorithm based on polynomial fitting) is called; the extrapolation algorithm extends the data range to the target work area grid region not covered by the preliminary gridded data based on the existing spatial distribution trend of the preliminary gridded data (such as the variation law of stratum depth with the direction of the survey line); during the extension process, the algorithm calculates the depth value of the grid points in the uncovered area to ensure that the supplemented depth value is consistent with the existing depth value in the surrounding area in terms of trend; finally, extended gridded data extending to the entire target work area is generated, so that each grid point in the target work area corresponds to a depth value, avoiding omissions in subsequent boundary determination due to incomplete data coverage.

[0055] Specifically, in one scenario, the implementation of step 42 includes: using the preliminary meshing data generated in step 41 as input, which only covers a portion of the grid area of ​​the target work area, with the grid points in the uncovered areas 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 the depth value changing with the coordinates of the inline and crossline directions by fitting a polynomial function (such as a quadratic or cubic polynomial) to the existing depth values ​​in the preliminary meshing data (in the model, the independent variables are the inline coordinate x and the crossline coordinate y, and the dependent variable is the depth value z); the algorithm analyzes the existing spatial distribution trend of the preliminary meshing data based on this trend model (such as the depth value increasing linearly with x, and increasing with...). The algorithm follows a parabolic curve pattern (reflecting the variation of stratum depth along the survey line direction) to extend the data range to the target work area grid region (blank grid points) not covered by the initial gridded data. During this extension, 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 that blank grid point. It then performs trend consistency verification with existing depth values ​​in the surrounding area (e.g., calculating the difference between the predicted depth value and the most recently known depth value to ensure the difference is within a preset trend fluctuation range), adjusting predicted values ​​with large deviations. The verified and adjusted depth values ​​of the uncovered area grid points are then integrated with the existing depth values ​​in the initial gridded data to finally generate extended gridded data covering the entire target work area. In this extended gridded data, each grid point in the target work area corresponds to a depth value, avoiding omissions in subsequent boundary determination due to incomplete data coverage. This technique differs from traditional simple linear extrapolation methods by capturing more complex spatial trends through polynomial fitting, resulting in a higher degree of alignment between the supplemented depth values ​​and the actual strata variation patterns.

[0056] Preferably, in the specific technical implementation of step 43: using the extended gridded data generated in step 42 as the analysis object, the boundary lines at the top and bottom interfaces of the geological envelope determined in step 3 are called and used as the basis for spatial range determination; for each coordinate point in the extended gridded data (corresponding to the grid point of the target work area), a spatial position verification operation is performed, and by comparing the spatial position relationship between the coordinate point and the boundary line, it is determined whether the coordinate point is located within the boundary line; the depth data corresponding to the coordinate points determined to be located within the boundary line are marked as valid data, and the depth data corresponding to the coordinate points outside the boundary line are marked as invalid data; through the above marking operation, top interface stratigraphic data and bottom interface stratigraphic data with validity labels are generated, providing a data filtering basis for subsequent extraction of valid depth values.

[0057] Preferably, the specific implementation process of step 44 is as follows: using the top interface stratigraphic data and bottom interface stratigraphic data with validity labels generated in step 43 as the data source, traverse each grid point in the grid plane where the target work area is located; for each grid point, extract its corresponding valid depth value (i.e., the depth value marked as valid data), and automatically ignore the depth values ​​marked as invalid data; for the extracted valid depth values, perform continuous integration operation, and ensure the continuity of depth data in spatial distribution by eliminating the depth value jump between adjacent grid points and realizing the smooth transition of depth values; finally, generate the stratigraphic depth values ​​in all grid points of the top interface and the stratigraphic depth values ​​in all grid points of the bottom interface, providing a continuous and reliable depth data foundation for the subsequent construction of the three-dimensional model of the geological envelope.

[0058] Figure 6 This is a planar schematic diagram of valid and invalid data on the top and bottom interfaces in an application scenario. Figure 7 This is a 3D schematic diagram of valid and invalid data on the top and bottom interfaces in an application scenario. Figure 8 This is a 3D diagram illustrating the connection between the top and bottom interfaces in an application scenario. (Example) Figure 6-8 ,against Figures 3-5 In this scenario, valid and invalid data are distinguished based on boundary lines. Invalid data forms invalid regions, and valid data forms valid regions. From a three-dimensional perspective, the top and bottom interfaces are pieced together according to the valid and invalid regions to form a three-dimensional envelope. Figure 6 In the text, the color code represents the layer depth value.

[0059] Optionally, step 5 involves constructing top and bottom boundary constraints based on the stratigraphic depth values ​​and the boundary lines of the geological envelope at the top and bottom interfaces. This 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.

[0060] Optionally, step 51 involves constructing top interface boundary constraints based on the stratigraphic depth values, the boundary lines of the geological envelope at the top and bottom interfaces, and the top interface stratigraphic data with validity indicators. This 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.

[0061] Preferably, in the specific technical implementation of step 511: using the boundary line at the top interface of the geological envelope as a spatial reference benchmark, the top interface stratigraphic data with validity markers generated in step 43 is called; all coordinate points corresponding to the top interface stratigraphic data in the grid plane of the target work area 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 (located inside or outside the boundary line); according to the judgment result, an attribute label of "inside the boundary" or "outside the boundary" is added to each coordinate point to generate top interface coordinate point data marked with the inside / outside boundary attributes. Each coordinate point in this data contains its planar coordinates (x, y), corresponding depth value, and boundary attribute information.

[0062] Specifically, in one scenario, the implementation of step 511 above includes: using the boundary line at the top interface of the geological envelope as a spatial reference, the boundary line containing the geometric morphological parameters of the boundary; if it is a polygonal boundary, then recording the set of vertex coordinates. If the boundary is a curve, record the curve fitting parameters (such as the coefficients of a quadratic curve); call the top interface layer data with validity indicators generated in step 43. This data contains the planar coordinates (x, y), corresponding depth values, and validity indicators of all top interface coordinate points in the target work area grid plane; according to the grid index order of the longitudinal survey line (Inline) and the transverse survey line (Crossline), traverse all coordinate points in the top interface layer data with validity indicators point by point. For each coordinate point to be judged, call the spatial geometry judgment algorithm (the technical essence of this algorithm is: if the boundary line is a polygon, then use the "ray method" - emit a virtual ray from the coordinate point to the horizontal direction, count the number of intersections between the ray and each side of the polygon, and if the number of intersections is odd, then the coordinate point is determined to be inside the boundary line). Even numbers are excluded; if the boundary line is a curve, the curve is first discretized into polygons with a preset precision, and then the "ray casting" method is executed for judgment. The algorithm automatically associates the geometric shape parameters of the boundary line (polygon vertex coordinates or curve discretized vertex coordinates) for calculation. The spatial geometric judgment algorithm performs spatial position judgment processing on the coordinate point to determine the position of the coordinate point relative to the boundary line (whether it is inside or outside the boundary line). According to the judgment result, an attribute label of "inside the boundary" or "outside the boundary" is added to each coordinate point (x, y). The attribute label is bound and integrated with the plane coordinates (x, y) and corresponding depth value of the coordinate point to generate top interface coordinate point data marked with the inside / outside boundary attribute. Each entry in this data completely contains the plane coordinates (x, y), depth value and boundary attribute information of a single point.

[0063] 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, the top interface coordinate points marked as "within the boundary" are filtered out, and the corresponding top interface layer depth values ​​of these coordinate points are directly retained; for the top interface coordinate points marked as "outside the boundary", the maximum depth value of the target work area determined in step 14 is called, and the maximum depth value is assigned to these coordinate points; through this differentiated assignment processing for inside and outside the boundary, the top interface depth data after boundary constraint assignment is obtained. This data makes the top interface depth at the boundary line show a downward extension trend that matches the depth range of the work area, avoiding interference of the outer boundary data on the envelope shape.

[0064] Preferably, in one scenario, step 513 is specifically implemented as follows: taking the top interface depth data generated in step 512 after boundary constraint assignment as the processing object, the top interface depth data is standardized according to a preset data format standard (such as uniformly adopting a three-dimensional array form of "coordinate point (x,y)-depth value (z)-boundary attribute"); the standardized top interface depth data is verified for completeness to check for problems such as missing coordinate points or abnormal depth values ​​(such as exceeding the depth range of the work area), and abnormal data is corrected; after standardization and verification, the final top interface boundary constraint is generated, which can be directly used for subsequent boundary interaction calculation between the top interface and the bottom interface.

[0065] Optionally, step 52 involves constructing bottom interface boundary constraints based on the stratigraphic depth values, the boundary lines of the geological envelope at the top and bottom interfaces, and the bottom interface stratigraphic data with validity markers. This 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.

[0066] Preferably, in the specific technical implementation of step 521: using the boundary line at the bottom interface of the geological envelope as a spatial reference benchmark, the bottom interface stratigraphic data with validity markers generated in step 43 is called; all coordinate points corresponding to the bottom interface stratigraphic data in the grid plane of the target work area are traversed, and the spatial position judgment processing of each coordinate point is performed by the same spatial geometric judgment algorithm as in step 511 (associating the polygon or curve parameters of the boundary line) to determine the position of the coordinate point relative to the boundary line (located inside or outside the boundary line); according to the judgment result, an attribute label of "inside the boundary" or "outside the boundary" is added to each coordinate point to generate bottom interface coordinate point data marked with the inside / outside boundary attributes. Each coordinate point in this data contains its planar coordinates (x, y), corresponding depth value and boundary attribute information, clearly marking the positional relationship between the coordinate point and the boundary line.

[0067] Preferably, the specific implementation process of step 522 is as follows: taking the bottom interface coordinate point data marked with boundary inside / outside attributes generated in step 521 as the input object, the bottom interface coordinate points marked as "within the boundary" are filtered out, and the bottom interface layer depth values ​​corresponding to these coordinate points are directly retained; for the bottom interface coordinate points marked as "outside the boundary", the minimum depth value of the target work area determined in step 14 is called, and the minimum depth value is assigned to these coordinate points; through this differentiated assignment processing for inside and outside the boundary, bottom interface depth data with boundary constraint assignment is obtained. This data makes the bottom interface depth at the boundary line show an upward trend matching the depth range of the work area, avoiding interference of the data outside the boundary on the bottom morphology of the geological envelope.

[0068] Preferably, in one scenario, step 523 is specifically implemented as follows: taking the bottom interface depth data generated in step 522 after boundary constraint assignment as the processing object, the bottom interface depth data is standardized according to the same preset format standard as the top interface boundary constraint (such as uniformly adopting the three-dimensional array form of "coordinate point (x,y)-depth value (z)-boundary attribute") to ensure the consistency of the top and bottom interface data formats; the standardized bottom interface depth data is subjected to integrity verification to check for problems such as missing coordinate points or abnormal depth values ​​(such as being lower than the minimum depth of the work area or higher than the corresponding top interface depth), and abnormal data is corrected; after standardization and verification, the final bottom interface boundary constraint data is generated, which can work together with the top interface boundary constraint to provide a complete boundary control basis for the three-dimensional spatial definition of the geological envelope.

[0069] Optionally, step 6, based on the constructed top and bottom interface boundary constraints, determines 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. This 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.

[0070] Preferably, the specific implementation process of step 61 is as follows: calling the top interface boundary constraint and bottom interface boundary constraint generated in step 5, taking the same coordinate point (x,y) in the grid plane where the target work area is located as the matching reference, extracting the top interface depth value and bottom interface depth value corresponding to each coordinate point; performing point-by-point comparison processing on these top interface depth values ​​and bottom interface depth values, calculating the difference between the two; filtering out coordinate points with a difference of zero (i.e., equal depth values), summarizing and integrating the three-dimensional coordinates (x,y,z) of these coordinate points to generate a candidate set of top and bottom interface intersection points, and the coordinate points contained in this candidate set are the possible intersection positions of the top and bottom interfaces at the boundary.

[0071] Preferably, in the specific technical implementation of step 62: taking the candidate set of intersection points of the top and bottom interfaces generated in step 61 as the analysis object, calling the spatial distribution law data of the geological envelope (such as the spatial continuity characteristics near the boundary line, the distance threshold of adjacent intersection points, etc.); based on these law data, performing validity verification 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; removing isolated outliers caused by data errors (such as points whose distance from surrounding intersection points exceeds the threshold) or intersection points that do not conform to the spatial trend, to obtain a valid set of intersection points of the top and bottom interfaces containing the actual intersection positions of the top and bottom interfaces at the boundary.

[0072] Preferably, in one scenario, step 63 is specifically implemented as follows: using the set of valid intersection points of the top and bottom interfaces generated in step 62 as the basic data, the intersection points in this set contain spatial distribution information of the inline and crossline directions; calling a three-dimensional curve fitting algorithm, inputting the three-dimensional coordinates (x, y, z) of the valid intersection points into the algorithm, and refitting the boundary curve according to the distribution characteristics of the intersection points in three-dimensional space (such as the changing trend along the survey line direction and the continuity in the depth direction); during the fitting process, ensuring that the curve passes through all valid intersection points and maintains a smooth transition between adjacent points, and finally generating an optimized boundary line that better fits the actual intersection boundary of the top and bottom interfaces of the geological envelope.

[0073] Specifically, in one scenario, the implementation of step 63 includes: using the effective set of intersection points of the top and bottom interfaces generated in step 62 as the basic data, which includes the inline coordinate (x), crossline coordinate (y), and depth coordinate (z) of each intersection point, as well as the data density identifier of the survey line to which the intersection point belongs (such as the number of intersection points per 100m grid in the inline direction and the number of intersection points per 100m grid in the crossline direction); calling a weighted three-dimensional curve fitting algorithm adapted to the geological survey line scenario (the technical essence of this algorithm is: combining the spatial correlation of geological data, assigning differentiated weights to intersection points with different data densities—the weight of intersection points in areas with higher data density (such as ≥5 points per 100m grid in the inline direction) is set to 0.7-0.9, and the weight of intersection points in areas with lower data density (such as <3 points per 100m grid in the crossline direction) is set to 0.4-0.6, thereby reducing the interference of sparse data on the fitting results through weight adjustment, and simultaneously constructing a ternary quadratic polynomial fitting function). Where a, b, c, d, e, and f are the fitting parameters to be solved, x and y correspond to planar coordinates, and z corresponds to depth coordinates; the three-dimensional coordinates (x, y, z) of the effective intersection points and their corresponding weight values ​​are input into the fitting algorithm. The algorithm first performs a quantitative analysis of the distribution characteristics of the intersection points, calculates the average rate of change of depth z for every 100m increase in the inline direction x, and the continuous deviation value of depth z for every 100m increase in the crossline direction y, thereby verifying the suitability of the order of the fitting function (if the continuous deviation value is <0.3m, the quadratic term is retained; if it is >0.3m, a cubic term is added). To improve fitting accuracy, based on the distribution characteristic analysis results, the fitting parameters are solved by minimizing the weighted sum of squared residuals (the residual is the difference between the actual depth z of the intersection point and the depth z' calculated by the fitting function). At the same time, a first-order 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 changes in the curve and ensure a smooth transition between adjacent intersection points. The above fitting operation generates a three-dimensional boundary curve that runs through all effective intersection points. This curve is the optimized boundary line, and its spatial shape can match the actual intersection boundary formed by the top and bottom interfaces of the geological envelope due to changes in lithology and small-scale faults. The fit is higher than that of the traditional unweighted fitting method.

[0074] Figure 9 This is a schematic diagram of the address envelope boundary line after step 6 in a scene. Figure 9 As shown, for Figures 3-4 In the application scenario described above, after the processing in step 6, the boundary lines (outermost edges) of the top and bottom interfaces completely intersect.

[0075] Optionally, step 7, based on the optimized boundary line, involves trimming and closing the target work area to generate a 3D visualization model of the geological envelope, specifically including: 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.

[0076] Preferably, the specific implementation process of step 71 is as follows: The optimized boundary line generated in step 6 is used as the clipping standard, and the boundary constraints based on the top and bottom interfaces constructed in step 5 are retrieved simultaneously. A spatial geometric judgment algorithm is used to perform spatial position judgment processing on each grid point in the top and bottom interface boundary constraints to determine whether the grid point is located within the area enclosed by the optimized boundary line (i.e., whether it is a grid point within the intersection point of the top and bottom interfaces). Grid point data located outside the intersection point are deleted, while grid point data within the intersection point are retained. The retained grid point data is then integrated to generate the clipped effective data for the top and bottom interfaces. This data only contains depth information of the actual range of the geological envelope, avoiding interference from redundant data in subsequent processing.

[0077] Specifically, in one scenario, the implementation of step 71 above includes: calling the optimized boundary line generated in step 6 as the clipping criterion, wherein the optimized boundary line contains the geometric morphological parameters of the intersection region of the top and bottom interfaces of the geological envelope—if it is a polygonal boundary, then recording the set of smoothed vertex coordinates. If it is a curved boundary, record the coefficients of the high-precision fitted curve equation (such as the coefficients of a cubic polynomial); at the same time, retrieve the data based on the top interface boundary constraints and bottom interface boundary constraints constructed in step 5. These two types of data contain the planar coordinates (x, y), corresponding depth values ​​(z), and previously marked boundary attribute identifiers ("inside the boundary" or "outside the boundary") of all grid points in the target work area; call the improved ray method spatial geometry judgment algorithm adapted to the geological grid scenario (the technical essence of this algorithm is: for the grid characteristics of equally spaced geological survey lines, fix the direction of the virtual ray to be parallel to the longitudinal survey line (Inline), avoiding the misjudgment caused by the random ray direction coinciding with the vertex and edge of the boundary line in the traditional ray method, and at the same time, by pre-calculating the slope range of each side of the boundary line, quickly eliminate the combination of rays and edge lines without intersection points, improving the judgment efficiency); this algorithm applies the top interface boundary constraints and bottom interface boundary constraints to each grid. The system performs spatial location determination by emitting virtual rays parallel to the Inline direction from the grid points. It counts the number of intersections between these rays and each side of the optimized boundary line. If the number of intersections is odd, the grid point is determined to be within the area enclosed by the optimized boundary line (i.e., grid points within the intersection of the top and bottom interfaces). If the number is even, it is determined to be outside the area. Based on the determination, grid point data outside the intersection points (including the grid point's coordinates, depth value, and attribute identifier) ​​is deleted, while grid point data within the intersection points are retained. The retained top and bottom interface grid point data are then categorized and integrated according to the survey line index (Inline number, crossline number) to generate trimmed effective top and bottom interface data. This data only contains depth information and corresponding coordinates of the actual range of the geological envelope, avoiding interference from redundant external grid point data in subsequent data closure, 3D modeling, and other processing stages, thus reducing computational load.

[0078] Preferably, in the specific technical implementation of step 72: using the effective data of the clipped top and bottom interfaces generated in step 71 as the processing object, a data closure algorithm based on grid point connectivity is called; this algorithm identifies and fills the gaps between the top and bottom interfaces caused by data sparsity or clipping by analyzing the spatial positional relationship and depth value continuity of the grid points of the top and bottom interfaces (such as areas where the depth values ​​of adjacent grid points jump too much, supplementing transitional depth values ​​according to the trend); ensuring 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 breaks and with a complete shape; after the closure process, effective geological envelope data with complete structure and continuous data is generated, providing a coherent data source for subsequent three-dimensional modeling. This technology differs from the traditional method of simply filling fixed values, by supplementing gaps through depth value trends, making the envelope shape more consistent with the actual geological structure.

[0079] Specifically, in one scenario, the implementation of step 72 above includes: using the clipped effective data of the top and bottom interfaces generated in step 71 as the processing object, which includes the planar coordinates (x, y) and depth value (z_top) of the effective grid points of the top interface and the planar coordinates (x, y) and depth value (z_bottom) of the effective grid points of the bottom interface, and gaps caused by missing grid points in some areas due to data sparsity or clipping; calling a data closure algorithm based on grid point connectivity adapted to the geological scenario, the technical essence of this algorithm is: combining the characteristic that the depth of geological strata changes continuously with the direction of the survey line, firstly, through "neighborhood connectivity analysis", the coordinate distance (set ≤ 1.2 times the grid spacing as effective adjacency) and depth difference (set a threshold based on the rate of change of stratum thickness in the target work area, such as ≤ 5m as continuous) of the adjacent grid points (adjacent in the Inline direction and crossline direction) of the top and bottom interfaces are calculated (a threshold is set with reference to the rate of change of stratum thickness in the target work area, such as ≤ 5m as continuous) are identified, and gap areas where the depth difference exceeds the threshold or where grid points are missing (such as grids i and i+1 in the Inline direction of the top interface). (For areas with depth differences of up to 8m), the algorithm extracts the depth values ​​of 3-5 effective grid points around the gap. It then fits the depth change trend using local weighted linear regression (weights decrease as distance from the gap increases), calculates and supplements the transition depth values ​​of the grid points at the gaps according to the trend (e.g., the depth value of the top interface gap point is assigned according to the linear trend of the surrounding effective point depths). Simultaneously, the algorithm verifies the connection between the top and bottom interface grid points at the optimized boundary line, ensuring that the top interface depth value (z_top) at the boundary line is always less than the corresponding bottom interface depth value (z_bottom). If a deviation exists, the bottom interface depth value is adjusted based on the normal thickness range of the strata, ensuring a complete connection and closure between the top and bottom interfaces at the boundary line. After the above gap filling and connection verification, a complete and intact 3D spatial data form is formed. Finally, a complete and continuous effective geological envelope data is generated, containing the coordinates and depth values ​​of all effective grid points at the top and bottom interfaces, providing a coherent data source for subsequent 3D modeling. This technology differs from the traditional method of simply filling fixed values. It fills the gaps by using depth value trends, making the shape of the envelope more consistent with the continuous variation of stratum depth in actual geological structures.

[0080] Preferably, in a scenario, step 73 is specifically implemented as follows: using the valid geological envelope data generated in step 72 as the data source, a commonly used 3D modeling tool in the geological field (such as Paradim and Surfer) is called; the valid geological envelope data is input into the modeling tool, and rendering parameters are configured (such as assigning differentiated materials to different depth ranges and setting transparency that meets the needs of geological observation so as to clearly observe the internal structure); the modeling tool performs 3D model rendering processing according to the parameters, transforming discrete depth data into an intuitive 3D solid form; finally, a 3D visualization model of the geological envelope that can be used in scenarios such as mineral resource exploration and geological hazard assessment is generated. This model supports exporting to common geological data formats such as SEGY and LAS, has high compatibility, and is convenient for subsequent use in conjunction with other geological analysis tools.

[0081] Specifically, in one scenario, the implementation of step 73 above includes: using the valid geological envelope data generated in step 72 as the data source, which contains the three-dimensional coordinates (x, y, z) of all valid grid points on the top and bottom interfaces and the corresponding lithological association identifiers of the strata (such as the classification codes of sandstone and mudstone), first performing format preprocessing on the valid geological envelope data to convert it into a structured grid format (such as GRD format, where the row index corresponds to the inline number of the longitudinal survey line, the column index corresponds to the crossline number of the transverse survey line, and the cell value corresponds to the depth z) that can be recognized by 3D modeling tools (such as Paradim and Surfer), ensuring that the coordinates, depth, and attribute words of the data fields are consistent with those of the tool interface. One-to-one matching; establishing a communication connection by calling the standardized API interface of the 3D modeling tool to transmit preprocessed and effective geological envelope data to the modeling tool; simultaneously configuring rendering parameters adapted to the observation needs of the geological scene, specifically: dividing the target work area into layers according to the preset stratigraphic depth range (e.g., 0-300m, 300-600m, 600-1000m), assigning differentiated material properties to each depth range (light gray semi-transparent material corresponding to loose sedimentary layers in the shallow 0-300m range, medium gray material corresponding to transitional rock layers in the middle 300-600m range, and dark gray material corresponding to dense bedrock in the deep 600-1000m range), and setting the overall transparency parameter to 35%-45% (…). This section clearly presents the outer spatial outline of the envelope and allows observation of the depth distribution of internal grid points and lithological boundaries through the surface. After receiving the data and parameters, the modeling tool activates a specialized surface rendering algorithm adapted to the geological data. This algorithm differs from traditional general surface rendering algorithms by adjusting the smoothness of the grid surface based on the lithological association identifiers in the effective geological envelope data. Continuous areas with consistent lithological codes (such as pure sandstone sections) are optimized with Bézier curves to make the surface shape smoother, while areas with abrupt changes in lithological codes (such as sandstone-mudstone interfaces) retain slight edges to reflect geological interface characteristics. Then, the material and transparency attributes in the rendering parameters are accurately mapped to the corresponding grid surfaces. Through this 3D model rendering process, the modeling tool can deeply... The data is transformed into an intuitive three-dimensional form that fits the actual geological structure. Finally, the modeling tool generates a three-dimensional visualization model of the geological envelope that can be used in scenarios such as mineral resource exploration (e.g., locating the spatial overlap area between the envelope and the ore body) and geological hazard assessment (e.g., analyzing the contact relationship between the envelope and the fault). The tool's format export module is then called to enable the model to be exported to common geological data formats such as SEGY and LAS (these formats are compatible with the input formats of mainstream geological analysis tools such as GeoFrame and Landmark). This provides high compatibility and facilitates the direct import of the three-dimensional visualization model into other tools for analysis such as reserve calculation and stability simulation, reducing redundant data format conversion operations.

[0082] Figure 10 This is a 3D visualization model of the geological envelope obtained after step 7 in an application scenario. For example... Figure 10 As shown, for Figures 3-4 The application scenarios shown present geological envelopes in a visually intuitive 3D form, facilitating analysis and research by geological engineers.

[0083] Here, it should be noted that the above... Figures 2-10 It is merely based on specific application scenarios. Figure 1 The methods shown are illustrated by way of example, and the content shown in the figures is not intended to limit the scope of this application.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the 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: Pick the sparse stratigraphic positions of the top and bottom interfaces of the geological envelope within the defined target work area to determine the top interface stratigraphic position, bottom interface stratigraphic position, 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 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 processing of the third layer data in both longitudinal and transverse directions using equally spaced control lines to determine the top interface layer, bottom interface layer, and corresponding top and bottom interface data.

4. 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 approximate location 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.

5. 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.

6. The method according to claim 5, 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.

7. The method according to claim 6, 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.

8. The method according to claim 6, 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.

9. 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.

10. 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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