Geological three-dimensional model source data evaluation method

Abstract

The invention discloses a geological three-dimensional model source data evaluation method, and relates to the technical field of geological models, and the method comprises the steps: obtaining a drilling data point set and grid system parameters, mapping drilling data points to a grid system, and obtaining a grid coverage statistical result; according to a grid coverage statistical result, calculating transverse and longitudinal distribution indexes, and performing distribution rationality evaluation processing to obtain a source data spatial distribution coefficient; based on the source data space distribution coefficient and the grid system parameters, obtaining a root-mean-square error precision report by calculating the shortest distance from the control point to the surface of the grid system; according to the root-mean-square error precision report and the grid system parameters, whether a blank zone exists in the geologic model or not is checked, a model integrity coefficient is calculated, and a final quality evaluation result is obtained. The invention provides a geological three-dimensional model source data evaluation method, and aims to solve the problem of inaccurate model blank zone detection caused by one-sided distribution evaluation and neglecting of vertical resolution and spatial correlation in the prior art.

Description

A method for evaluating source data of a geological 3D model Technical Field

[0001] This invention relates to the field of geological modeling technology, and in particular to a method for evaluating source data of a three-dimensional geological model. Background Technology

[0002] Existing technologies for source data evaluation often employ statistical distribution analysis (such as borehole point density calculation), but these methods are limited to two-dimensional planar evaluation and have not been extended to three-dimensional space. Appendix 1 of Document 1 mentions that traditional algorithms calculate data coverage through grid division, but do not incorporate stratigraphic influence parameters, making it difficult to quantify spatial continuity. Accuracy verification relies on manual methods, and model fitting accuracy evaluation requires manual measurement of point-to-surface distances. RMSE calculation is inefficient and lacks spatial indexing for acceleration. Integrity checks are not automated: blank zone identification relies on grid traversal but is not associated with geological object attribution; CM coefficient calculation requires manual labeling, which easily introduces subjective errors.

[0003] Existing technologies only consider the horizontal direction and ignore vertical resolution and spatial correlation, resulting in inaccurate detection of blank zones in the model. For example, in the Liaocheng City model case, some strata have insufficient coverage. The information disclosed in this background section is only intended to enhance the understanding of the overall background of the present invention and should not be regarded as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating source data of a geological three-dimensional model, in order to solve the problem that the existing technology has a one-sided distribution evaluation, ignores vertical resolution and spatial correlation, and leads to inaccurate detection of blank zones in the model.

[0005] To achieve the above objectives, this invention provides a method for evaluating source data of a geological 3D model, comprising the following steps: S1: Obtaining a set of borehole data points and grid system parameters, mapping the borehole data points to the grid system, and obtaining grid coverage statistics, wherein each borehole data point contains 3D coordinates (x, y, z) and a stratigraphic code, and the grid system parameters include horizontal grid resolution and vertical dimension range; S2: Calculating the horizontal and vertical distribution indices based on the grid coverage statistics, performing distribution rationality evaluation processing, and obtaining the source data spatial distribution coefficient; S3: Based on the source data spatial distribution coefficient and grid system parameters, obtaining a root mean square error accuracy report by calculating the shortest distance from the control point to the grid system surface; S4: Checking whether there are blank zones in the geological model based on the root mean square error accuracy report and grid system parameters, and calculating the model integrity coefficient to obtain the final quality evaluation result; S5: Testing and verifying the final quality evaluation result, and optimizing it based on the test results.

[0006] In one embodiment of the present invention, S1: Obtain a set of borehole data points and grid system parameters, map the borehole data points to the grid system, and obtain grid coverage statistics. Each borehole data point contains three-dimensional coordinates (x, y, z) and a formation code. The grid system parameters include horizontal grid resolution and vertical dimension range. This includes: obtaining the set of borehole data points and grid system parameters; determining the spatial range of the modeling area by calculating coordinate extrema; within the spatial range of the modeling area, dividing the horizontal range into regular grids according to the horizontal grid resolution, and dividing the vertical range into vertical layers according to the vertical dimension range, to obtain the divided three-dimensional grid; for each borehole point, calculating its horizontal grid index based on its coordinates and determining its vertical affiliation, mapping the point to the corresponding three-dimensional grid cell to obtain the grid after borehole point mapping; traversing all three-dimensional grid cells to count the number of borehole points in each cell, and counting the number of horizontal coverage grids according to the formation, generating grid coverage statistics.

[0007] In one embodiment of the present invention, S2: Based on the grid coverage statistics, calculate the lateral and longitudinal distribution indices, perform distribution rationality assessment processing, and obtain the source data spatial distribution coefficient, including: based on the grid coverage statistics, calculate the ratio of the number of cover grids of each layer in the horizontal direction to the total number of deployable grids as the lateral distribution index, and combine the ratio of the number of borehole point distribution layers in the vertical direction to the total number of layers as the longitudinal distribution index; weight and fuse the lateral and longitudinal distribution indices to obtain the source data spatial distribution coefficient; perform graded evaluation of the spatial distribution coefficient according to a preset threshold, judge the rationality of the data distribution in three-dimensional space, and obtain the source data spatial distribution coefficient.

[0008] In one embodiment of the present invention, S2: Based on the grid coverage statistics, calculate the horizontal and vertical distribution indices, perform distribution rationality assessment processing, and obtain the source data spatial distribution coefficient. It also includes: Based on the configuration of the stratum influence range, perform three-dimensional radiation simulation processing, calculate the coverage effect of each borehole point within the horizontal and vertical influence grid, and obtain the spatial continuity index; Based on the continuity index, perform model integrity prediction processing, and output a blank zone risk area report to guide supplementary data collection.

[0009] In one embodiment of the present invention, S3: Based on the spatial distribution coefficient of the source data and the grid system parameters, a root mean square error accuracy report is obtained by calculating the shortest distance from the control point to the surface of the grid system. This includes: performing spatial index construction processing on the control point data based on the grid system parameters and the spatial distribution coefficient of the source data to accelerate distance query and obtain a set of candidate triangles; performing adaptive subdivision and distance calculation processing on the set of candidate triangles to obtain the minimum distance of each point and summarizing the RMSE to obtain a root mean square error accuracy report.

[0010] In one embodiment of the present invention, S4: Based on the root mean square error accuracy report and grid system parameters, check whether there are blank zones in the geological model, calculate the model integrity coefficient, and obtain the final quality assessment result, including: performing grid attribution check processing based on grid system parameters, traversing the geological object correlation of each grid unit to obtain blank zone marking results; and performing volume percentage calculation processing based on the blank zone marking results to obtain the integrity coefficient.

[0011] In one embodiment of the present invention, S5: The final quality assessment result is tested and verified, and optimized according to the test result, including: performing performance testing based on the final quality assessment result, recording the processing time and memory usage to obtain the test result; and performing algorithm adjustment based on the test result to obtain the optimized algorithm parameters.

[0012] On the other hand, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the geological three-dimensional model source data evaluation method as claimed in claim .

[0013] On the other hand, a non-transitory computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the geological three-dimensional model source data evaluation method as described in claim .

[0014] On the other hand, a computer program product includes a computer program that, when executed by a processor, implements the geological three-dimensional model source data evaluation method as described in claim .

[0015] Compared with existing technologies, the geological three-dimensional model source data evaluation method of the present invention introduces the stratigraphic influence range parameter, calculates the coverage grid number statistics and uniformity index, realizes three-dimensional spatial continuity evaluation, improves the accuracy of distribution rationality analysis, and supports multi-stratum comparison; combined with grid indexing and triangular subdivision technology, it accelerates the calculation of point-to-surface distance, automatically outputs RMSE and error distribution, adapts to large-scale data, and improves accuracy compared with traditional methods; by automatically marking blank zones through grid assignment, formulaically calculating CM coefficient, performing integrity verification, eliminating human error, and ensuring that the model is complete. Attached Figure Description

[0016] Figure 1 is a flowchart of a geological three-dimensional model source data evaluation method according to an embodiment of the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0018] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0019] As shown in Figure 1, a method for evaluating source data of a geological three-dimensional model according to a preferred embodiment of the present invention is presented.

[0020] This invention provides a method for evaluating source data of a geological 3D model, comprising the following steps: S1: Obtaining a set of borehole data points and grid system parameters, mapping the borehole data points to the grid system, and obtaining grid coverage statistics, wherein each borehole data point contains 3D coordinates (x, y, z) and a stratigraphic code, and the grid system parameters include horizontal grid resolution and vertical dimension range; S2: Calculating the horizontal and vertical distribution indices based on the grid coverage statistics, performing distribution rationality evaluation processing, and obtaining the source data spatial distribution coefficient; S3: Based on the source data spatial distribution coefficient and grid system parameters, obtaining a root mean square error accuracy report by calculating the shortest distance from the control point to the grid system surface; S4: Checking whether there are blank zones in the geological model based on the root mean square error accuracy report and grid system parameters, and calculating the model integrity coefficient to obtain the final quality evaluation result; S5: Testing and verifying the final quality evaluation result, and optimizing it based on the test results.

[0021] In this embodiment of the invention, a full-process assessment of source data quality is achieved through five interconnected steps: Step S1 transforms the raw data into structured grid statistics, providing a foundation for assessment; Step S2 quantifies the rationality of the distribution and identifies data defects; Step S3 assesses the accuracy of the model and locates fitting deviations; Step S4 verifies the integrity of the model to ensure no blank areas; Step S5 verifies the reliability of the method through testing; subjective quality judgments are transformed into objective quantitative indicators, improving assessment efficiency and accuracy; large-scale data (such as 20,313 borehole points in the Liaocheng City model) can be efficiently processed in a normal PC environment, supporting the promotion and application of geological survey informatization standards, providing a standardized tool for the quality assessment of three-dimensional geological models, and significantly improving the reliability and application value of geological modeling.

[0022] As shown in Figure 1, S1: Obtain the set of borehole data points and grid system parameters, map the borehole data points to the grid system, and obtain the grid coverage statistics. Each borehole data point contains three-dimensional coordinates (x, y, z) and a formation code. The grid system parameters include the horizontal grid resolution and the vertical dimension range, including: S11: Obtain the set of borehole data points and grid system parameters, and determine the spatial range of the modeling area by calculating the coordinate extreme values; S12: Within the spatial range of the modeling area, divide the horizontal range into regular grids according to the horizontal grid resolution, and divide the vertical layers according to the vertical dimension range to obtain the divided three-dimensional grid; S13: For each borehole point, calculate the horizontal grid index according to its coordinates and determine the vertical assignment, and map the point to the corresponding three-dimensional grid cell to obtain the grid after the borehole point is mapped; S14: Traverse all three-dimensional grid cells to count the number of borehole points in each cell, and count the number of horizontal coverage grids according to the formation to generate the grid coverage statistics.

[0023] In this embodiment of the invention, through systematic spatial mapping processing, discrete borehole data is transformed into structured grid coverage statistics, providing quantitative input for subsequent distribution rationality assessment; S11 calculates coordinate extrema (xmin, xmax, ymin, ymax, zmin, zmax) by reading the borehole data point set and grid system parameters, determines the spatial boundary of the modeling area, establishes a three-dimensional spatial reference frame, ensures that the grid division covers all data points, and avoids boundary omissions. In one embodiment of the invention, the Liaocheng City model case uses this step to determine the range (X: 397116m~423178m, Y: -4,044,030m~-4,025,340m, Z: S12 (70.28m~38.51m) provides a precise basis for grid division; S12 divides the modeling area into regular three-dimensional grids according to the horizontal grid resolution and vertical dimension range, realizing spatial discretization and converting continuous space into grid cells, which facilitates data indexing and statistics. The vertical direction is flexibly layered according to the stratigraphic code or resolution. In one embodiment of the present invention, a 100×100×500 grid is used to balance calculation accuracy and efficiency; the grid size is adjustable to adapt to different accuracy requirements; S13 maps each borehole point to the corresponding three-dimensional grid cell according to its coordinates, and establishes the correspondence between points and grids through index calculation to realize data positioning; combined with the stratigraphic code, it ensures that the point data is correctly classified in the vertical direction. In one embodiment of the present invention, the vertical index is assigned according to the stratigraphic code (such as "0-2-2-3"); boundary checks are used to avoid index out-of-bounds and improve mapping accuracy; S14 iterates through all grid cells, counts the number of boreholes in each cell, and summarizes the number of horizontally covered grid cells according to the formation code; generates a coverage matrix (e.g., Count[i][j][k]) to visually display the data distribution density. In one embodiment of the present invention, the number of covered grid cells for each formation is output (e.g., formation "0-2-2-3" covers 3,563,209 grid cells); the statistical results are used to quickly locate sparse data areas and provide a basis for data supplementation; the statistical results are directly used to calculate distribution rationality indicators (e.g., S1, S2). In one embodiment of the present invention, the CV value (75.34%) is calculated based on this; through the concatenation of four sub-steps, a complete transformation from raw data to grid statistics is achieved: S11 establishes the spatial range, S12 constructs the grid framework, S13 completes the point mapping, and S14 generates statistical results, providing standardized and quantitative input data for distribution rationality assessment.

[0024] As shown in Figure 1, S2: Based on the grid coverage statistics, calculate the horizontal and vertical distribution indices, perform distribution rationality assessment, and obtain the source data spatial distribution coefficient, including: S21: Based on the grid coverage statistics, calculate the ratio of the number of grids covering each layer in the horizontal direction to the total number of grids that can be distributed as the horizontal distribution index, and combine it with the ratio of the number of borehole point distribution layers to the total number of layers in the vertical direction as the vertical distribution index; S22: Weight and fuse the horizontal and vertical distribution indices to obtain the source data spatial distribution coefficient; S23: Based on the preset threshold, perform a graded evaluation of the spatial distribution coefficient, judge the rationality of the data distribution in three-dimensional space, and obtain the source data spatial distribution coefficient.

[0025] In this embodiment of the invention, based on the grid coverage statistics generated in step S1, the spatial distribution coefficient of the source data is finally obtained by calculating and weighting the lateral and longitudinal distribution indices; distribution quantification transforms abstract spatial distribution characteristics into quantifiable numerical indicators, eliminating subjective judgment bias; three-dimensional comprehensive evaluation considers the distribution characteristics in both the horizontal and vertical directions to achieve true three-dimensional spatial assessment; hierarchical guidance provides clear direction for data quality improvement through hierarchical evaluation using preset thresholds; efficiency improvement is achieved through rapid calculation based on grid statistics results; the lateral distribution index quantifies the uniformity of data distribution on the horizontal plane by calculating the ratio of the number of cover grids in each layer of the formation to the total number of deployable grids; coverage assessment directly reflects the coverage integrity of the data in the horizontal direction; sparse area identification quickly locates sparse areas of data by the ratio, guiding subsequent data supplementation work; strata The system supports horizontal distribution comparison between different strata to identify stratigraphic units with abnormal distribution. Vertical distribution index calculation assesses the continuity of data distribution in the vertical direction by calculating the ratio of the number of borehole points distributed in the vertical direction to the total number of strata. Vertical continuity assessment reflects the sampling density and distribution range of data in the vertical direction. Stratigraphic integrity check identifies strata or layers with missing vertical data. Three-dimensional distribution balance, combined with the lateral distribution index, avoids the evaluation bias of "emphasizing horizontal over vertical". Weighted fusion obtains spatial distribution coefficients by fusing lateral and vertical distribution indices through weighting to generate comprehensive spatial distribution coefficients. Comprehensive quantification generates a single numerical index, facilitating comparison between different models. Adjustable weights allow adjustment of the lateral and vertical weight ratios according to actual needs, adapting to different evaluation scenarios. Weighted fusion eliminates the influence of dimensions, making the results comparable. Lateral distribution index: Longitudinal distribution index: Spatial distribution coefficient: ,in, The horizontal distribution coefficient is... The longitudinal distribution coefficient is... The spatial distribution coefficient is α, and the weighting coefficients are β, where α+β=1. The weights are adjusted according to actual needs. The tiered evaluation evaluates the spatial distribution coefficient based on a preset threshold to determine the rationality of the data distribution in three-dimensional space. The continuous spatial distribution coefficient is discretized into levels such as "excellent," "good," "medium," and "poor" for easy understanding. It provides a clear basis for whether to accept the current data quality or whether supplementary data is needed. It ensures the consistency and comparability of evaluation results across different projects.

[0026] As shown in Figure 1, S2: Based on the grid coverage statistics, calculate the horizontal and vertical distribution indices, perform distribution rationality assessment, and obtain the source data spatial distribution coefficient. It also includes: S24: Based on the configuration of the stratum influence range, perform three-dimensional radiation simulation processing, calculate the coverage effect of each borehole point in the horizontal and vertical influence grid, and obtain the spatial continuity index; S25: Based on the continuity index, perform model integrity prediction processing, output a report of blank zone risk areas, and use it to guide the collection of supplementary data.

[0027] In this embodiment of the invention, the S24 three-dimensional radiation simulation processing, based on the configuration of the stratum influence range (horizontal / vertical grid range), effectively solves the "hard boundary" problem of traditional grid statistics. Each borehole point is no longer considered isolated data, but a radiation source with a specific influence radius, which is more consistent with the actual influence characteristics of geological data. By simulating the radiation coverage effect, "pseudo-continuous" areas with surface data but insufficient actual influence are identified. Different influence range parameters can be set for different strata. In one embodiment of the invention, the stratum "0-2-2-3" has a horizontal range of 25 grids and a vertical range of 30 grids, while "0-1-1-5" has only 5 grids, reflecting geological differences and upgrading the spatial continuity assessment from a simple "presence / absence of data" to "effective influence range," significantly improving the assessment accuracy. Based on the original CV value of 75.34% in the Liaocheng City model, an additional 15% of the influence is identified. The S25 model integrity prediction processing accurately predicts risks in blank zones. Based on continuity indicators, it performs model integrity prediction processing, achieving a leap from "current status assessment" to "risk warning." By comparing continuity indicators with integrity thresholds, it quantifies the probability of blank zone generation. It accurately outputs the spatial coordinates and risk levels of risk areas to guide targeted supplementary exploration. It automatically prioritizes processing based on risk levels, optimizing resource allocation. The report generated includes: a list of high-risk areas (coordinate range, risk value, affected strata), a distribution map of medium and low-risk areas, data supplementation suggestions, and an assessment of expected improvement effects. The prediction processing improves data supplementation efficiency by more than 60%, avoiding the waste of resources from blind exploration. Guided by the risk report, the city model specifically supplemented borehole data in three key areas, increasing the model integrity coefficient (CM) from 92% to 98%.

[0028] As shown in Figure 1, S3: Based on the spatial distribution coefficient of the source data and the grid system parameters, the root mean square error accuracy report is obtained by calculating the shortest distance from the control point to the grid system surface. This includes: S31: Based on the control point data of the grid system parameters and the spatial distribution coefficient of the source data, spatial index construction is performed to accelerate distance query and obtain a set of candidate triangles; S32: Based on the set of candidate triangles, adaptive subdivision and distance calculation are performed to obtain the minimum distance of each point and summarize the RMSE to obtain the root mean square error accuracy report.

[0029] In this embodiment of the invention, the core step in the accuracy assessment of the source data evaluation method for geological 3D models is to generate a root mean square error (RMSE) accuracy report by accurately calculating the shortest distance from control points to the model surface, providing a key quantitative indicator for model quality; transforming model fitting quality into a quantifiable RMSE indicator, eliminating subjective judgment bias; significantly improving the efficiency of large-scale data processing through spatial indexing and adaptive subdivision technology; not only providing overall accuracy assessment, but also identifying fitting problems in specific regions or strata; providing a scientific basis for model optimization and data analysis; and using the S31 spatial index construction processing, an efficient query acceleration mechanism, to construct a spatial index based on control point data with grid system parameters and spatial distribution coefficients of source data, creating... A 3D mesh index structure is constructed; revolutionary improvement in computational efficiency, with local search replacing global traversal: the computational complexity of point-triangle distance is reduced from O(N×M) to nearly O(NlogM), where N is the number of points and M is the number of triangles; in one embodiment of the invention, the computation time for 20,313 control points and 4,921,282 triangles is reduced from hours to minutes; dynamic memory management, with the index structure optimizing memory usage, keeps memory usage stable within 500MB in one embodiment of the invention, avoiding memory overflow issues when processing large models; parallel computing support, spatial indexing inherently supports parallelization, and multi-core processors can be used to accelerate computation; enhanced quality control, precise screening of candidate triangles, and rapid location of possible triangles for each control point. The system includes a set of triangles containing the nearest points to avoid unnecessary distance calculations; robust boundary handling automatically processes points located on grid boundaries to ensure index integrity; a fault-tolerance mechanism provides robustness to outlier data, preventing index construction failures; and S32 adaptive subdivision and distance calculation processing ensures both accuracy and detail optimization. Based on the candidate triangle set, adaptive subdivision and distance calculation are performed to ensure distance calculation accuracy while optimizing computational efficiency. The core value of adaptive subdivision lies in improving the accuracy of large triangles. Triangles larger than the grid cell size are subdivided to avoid distance calculation errors caused by excessively large triangles. In one embodiment of the invention, a subdivision threshold based on grid size is used to ensure that each subdivided triangle matches the grid cell scale. Computational resource optimization is also implemented. Subdivision is performed only when necessary, avoiding the waste of computational resources caused by global subdivision; geometric feature preservation maintains the original triangle shape features during subdivision without introducing new geometric distortions; the advantage of distance calculation algorithms is utilized, employing the Moller-Trumbore algorithm and a highly efficient point-triangle intersection detection algorithm to accurately calculate the shortest distance; multiple verification mechanisms include distance calculations for various cases such as points within triangles, points to edges, and points to vertices, ensuring the accuracy of results; real-time progress feedback supports progress monitoring during large-scale computations, improving the user experience; RMSE summary and report generation not only calculates the overall RMSE but also outputs stratified accuracy indicators by stratum; visualization support generates error distribution maps to intuitively display the fitting quality of each region of the model;Historical data comparison supports comparative analysis of multiple evaluation results, tracking the effectiveness of model optimization.

[0030] As shown in Figure 1, S4: Based on the root mean square error accuracy report and grid system parameters, check whether there are blank zones in the geological model, calculate the model integrity coefficient, and obtain the final quality assessment result, including: S41: Based on the grid system parameters, perform grid attribution check processing, traverse the geological object correlation of each grid unit, and obtain the blank zone marking result; S42: Based on the blank zone marking result, perform volume proportion calculation processing to obtain the integrity coefficient.

[0031] In this embodiment of the invention, a complete technical path from grid data to integrity assessment is established, transforming the abstract concept of "model integrity" into a specific numerical indicator (CM coefficient). This enables automatic identification and marking of blank zones, avoiding the subjectivity and omissions of manual inspection, providing clear quantitative standards for model acceptance, supporting scientific decision-making, and guiding data supplementation and model optimization through blank zone location. The S41 grid attribution check and processing mechanism accurately identifies blank zones and performs grid attribution checks based on grid system parameters, systematically traversing the geological object associations of each grid unit. Unit-level checks determine attribution based on grid units, ensuring sufficiently fine detection granularity. Association verification checks whether each grid unit establishes an association with a specific geological object (strata, rock mass, etc.). Boundary handling properly addresses grid boundary conditions, avoiding boundary... Misjudgments caused by effects; the value of quality control, systematic traversal ensures all grid units are checked; real-time marking, blank zones are marked immediately upon discovery, supporting subsequent visualization; type recording, distinguishing the belonging relationships of different types, providing a basis for detailed analysis; S42 volume proportion calculation processing, based on the blank zone marking results, performs volume proportion calculation processing to generate model integrity coefficient; calculation based on grid unit volume is more consistent with the spatial characteristics of geological models; the ratio of blank zone volume to total volume intuitively reflects the degree of integrity; generating 0-100% standardized coefficients facilitates comparison between different models; steps S41 and S42 form a complete "detection-evaluation" closed loop, S41 provides accurate blank zone marking results, and S42 performs scientific quantitative evaluation based on the marking results, the two working together to achieve the leap from qualitative judgment to quantitative evaluation.

[0032] As shown in Figure 1, S5: The final quality assessment result is tested and verified, and optimization is performed based on the test results, including: S51: Based on the final quality assessment result, performance testing is performed, and processing time and memory usage are recorded to obtain test results; S52: Based on the test results, algorithm adjustment is performed to obtain optimized algorithm parameters.

[0033] In this embodiment of the invention, a feedback mechanism from evaluation results to method optimization is established. Reliability verification verifies the universality and stability of the method through multi-scenario testing. Performance optimization is based on test data to specifically optimize algorithm parameters and improve method efficiency. Standardization forms a verified standardized parameter system, promoting the application of the method and establishing an iterative optimization mechanism to support continuous method evolution. S51 performance testing processing and a multi-dimensional performance evaluation system are used to conduct comprehensive performance testing based on the final quality evaluation results, establishing a scientific performance evaluation system. Multi-data type coverage uses five different types of data, including coal mine 3D models, ore body models, and urban geological models, to verify the universality of the method. Multi-scale data testing, from files ranging from 30MB to 100MB, evaluates the scalability of the method. Multi-indicator monitoring is also implemented. The system synchronously records key performance indicators such as processing time, memory usage, and CPU utilization. The S52 algorithm is adjusted and data-driven, with precise algorithm adjustments based on test results to continuously optimize method performance. Tolerance parameter optimization dynamically adjusts distance tolerance according to model accuracy requirements, balancing accuracy and efficiency. Adaptive grid resolution automatically adjusts grid granularity based on data scale, optimizing computational resource usage. Index structure optimization selects the optimal spatial index structure (KD-Tree or grid index) for different data characteristics. Steps S51 and S52 form a complete "test-optimization" iterative loop. S51 provides comprehensive performance data collection, using performance analysis to pinpoint bottlenecks. S52 optimizes parameters and algorithms to address these bottlenecks, retesting and verifying the optimization effect to form a closed loop.

[0034] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for evaluating source data of a geological three-dimensional model, characterized in that, Includes the following steps: S1: Obtain the set of borehole data points and grid system parameters, map the borehole data points to the grid system, and obtain grid coverage statistics. Each borehole data point contains three-dimensional coordinates (x, y, z) and a formation code. The grid system parameters include horizontal grid resolution and vertical dimension range. S2: Based on the grid coverage statistics, calculate the horizontal and vertical distribution indices, perform distribution rationality assessment, and obtain the source data spatial distribution coefficient. S3: Based on the source data spatial distribution coefficient and grid system parameters, calculate the shortest distance from the control point to the grid system surface to obtain the root mean square error accuracy report. S4: Based on the root mean square error accuracy report and grid system parameters, check for blank zones in the geological model and calculate the model integrity coefficient to obtain the final quality assessment result; S5: Test and verify the final quality assessment result, and optimize it based on the test results.

2. The geological three-dimensional model source data evaluation method as described in claim 1, characterized in that, S1: Obtain the set of borehole data points and grid system parameters, map the borehole data points to the grid system, and obtain grid coverage statistics. Each borehole data point contains three-dimensional coordinates (x, y, z) and a formation code. The grid system parameters include horizontal grid resolution and vertical dimension range. This includes: obtaining the set of borehole data points and grid system parameters; determining the spatial range of the modeling area by calculating coordinate extrema; within the spatial range of the modeling area, dividing the horizontal range into regular grids according to the horizontal grid resolution, and dividing the vertical range into vertical layers according to the vertical dimension range to obtain the divided three-dimensional grids; for each borehole point, calculating its horizontal grid index and determining its vertical affiliation based on its coordinates, mapping the point to the corresponding three-dimensional grid cell to obtain the grid after the borehole point mapping; traversing all three-dimensional grid cells to count the number of borehole points in each cell, and counting the number of horizontal coverage grids according to the formation to generate grid coverage statistics.

3. The geological three-dimensional model source data evaluation method as described in claim 1, characterized in that, S2: Based on the grid coverage statistics, calculate the lateral and longitudinal distribution indices, perform distribution rationality assessment, and obtain the source data spatial distribution coefficient. This includes: based on the grid coverage statistics, calculating the ratio of the number of cover grids for each layer in the horizontal direction to the total number of available grids as the lateral distribution index, and combining the ratio of the number of borehole point distribution layers to the total number of layers in the vertical direction as the longitudinal distribution index; weighting and fusing the lateral and longitudinal distribution indices to obtain the source data spatial distribution coefficient; and classifying and evaluating the spatial distribution coefficient according to a preset threshold to determine the rationality of the data distribution in three-dimensional space, thereby obtaining the source data spatial distribution coefficient.

4. The geological three-dimensional model source data evaluation method as described in claim 1, characterized in that, S2: Based on the grid coverage statistics, calculate the horizontal and vertical distribution indices, perform distribution rationality assessment, and obtain the source data spatial distribution coefficient. It also includes: based on the configuration of the stratum influence range, perform three-dimensional radiation simulation processing, calculate the coverage effect of each borehole point within the horizontal and vertical influence grid, and obtain the spatial continuity index; based on the continuity index, perform model integrity prediction processing, and output a report on the risk area of ​​the blank zone to guide the collection of supplementary data.

5. The geological three-dimensional model source data evaluation method as described in claim 1, characterized in that, S3: Based on the spatial distribution coefficients of the source data and the grid system parameters, the root mean square error (RMSE) accuracy report is obtained by calculating the shortest distance from the control points to the grid system surface. This includes: performing spatial index construction processing on the control point data based on the grid system parameters and the spatial distribution coefficients of the source data to accelerate distance queries and obtain a set of candidate triangles; and performing adaptive subdivision and distance calculation processing on the set of candidate triangles to obtain the minimum distance of each point and summarizing the RMSE to obtain the root mean square error accuracy report.

6. The geological three-dimensional model source data evaluation method as described in claim 1, characterized in that, S4: Based on the root mean square error accuracy report and grid system parameters, check whether there are blank zones in the geological model, calculate the model integrity coefficient, and obtain the final quality assessment result, including: based on the grid system parameters, perform grid assignment check processing, traverse the geological object correlation of each grid unit, and obtain the blank zone marking result; based on the blank zone marking result, perform volume proportion calculation processing to obtain the integrity coefficient.

7. The geological three-dimensional model source data evaluation method as described in claim 1, characterized in that, S5: The final quality assessment results are tested and verified, and optimization is performed based on the test results, including: performance testing based on the final quality assessment results, recording processing time and memory usage to obtain test results; and algorithm adjustment based on the test results to obtain optimized algorithm parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the geological three-dimensional model source data evaluation method as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the geological three-dimensional model source data evaluation method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the geological three-dimensional model source data evaluation method as described in any one of claims 1 to 7.

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