Geophysical three-dimensional modeling method and system based on multi-dimensional data

Abstract

The application provides a geophysical three-dimensional modeling method and system based on multidimensional data, and relates to the technical field of geophysical modeling. The method comprises the following steps: acquiring geographic surveying and mapping data, extracting surveying and mapping data of a detection area, and forming target area surveying and mapping data; collecting geophysical survey data, combining the target area surveying and mapping data to analyze resistivity variation, and establishing a target area geophysical resistivity model; acquiring geological interpretation data, conducting geological structure analysis according to the target area geophysical resistivity model, and establishing a target area geophysical model. The method analyzes and models by reasonably analyzing multidimensional data, and improves the stability of modeling data accuracy and the reliability of data.

Description

Technical Field

[0001] This invention relates to the field of geophysical modeling technology, and more specifically, to a geophysical three-dimensional modeling method and system based on multi-dimensional data. Background Technology

[0002] Three-dimensional modeling in geophysics has gradually become an important means of geological analysis. It mainly involves using measuring instruments to acquire measurement data, processing and transforming it to form a three-dimensional model. This method is efficient, fast and accurate.

[0003] However, most current 3D modeling methods rely on a single data acquisition approach, primarily using measurement data from measuring instruments. While the resulting 3D model depends on the accuracy of the measuring instruments, it lacks comparable data for reference. Consequently, the model data based on measuring instruments is not capable of resisting data interference caused by the inaccuracy of the testing instruments.

[0004] Therefore, designing a geophysical 3D modeling method and system based on multi-dimensional data, and improving the stability and reliability of modeling data through reasonable multi-dimensional data analysis and modeling, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a geophysical 3D modeling method based on multi-dimensional data. This method involves collecting geographic survey data to obtain multi-dimensional geological data information other than resistivity measurement methods. The multi-dimensional data is then used to transform the target area into a 3D model. The 3D model data is then combined to locate the resistivity measurement positions and extract relevant resistivity measurement data. The resistivity data is corrected using actual sampled data to obtain more accurate resistivity data. Furthermore, resistivity data from different depths measured by different resistivity instruments are rationally processed, analyzed, and fused. Finally, a more accurate modeling data is established compared to modeling using only resistivity measurement data. This effectively improves the accuracy of the modeling while ensuring the reliability of the modeling data. After all, resistivity data verified by multi-dimensional data can effectively eliminate accuracy deviations and interference caused by instrument problems in resistivity measurement instruments.

[0006] The present invention also aims to provide a geophysical 3D modeling system based on multi-dimensional data. This system uses a data acquisition unit to obtain the basic raw data required for establishing a geophysical model. A surveying and analysis unit extracts data features from multi-dimensional data, including sampling measurement data and surveying data. A measurement and modeling unit, while acquiring resistivity measurement data, combines multi-dimensional data to correct and adjust the resistivity data, resulting in resistivity model data with higher accuracy and reliability. Finally, a geological interpretation unit completes the transformation into a geophysical model. Although the different units have different functions, they form a closely interconnected whole, forming an important material basis for establishing a multi-dimensional geophysical 3D model.

[0007] In a first aspect, the present invention provides a geophysical three-dimensional modeling method based on multi-dimensional data, comprising: acquiring geographic mapping data, extracting mapping data of the exploration area to form target area mapping data; collecting geophysical measurement data and combining it with the target area mapping data to perform resistivity change analysis and establish a geophysical resistivity model of the target area; acquiring geological interpretation data, performing geological structure analysis based on the geophysical resistivity model of the target area, and establishing a geophysical model of the target area.

[0008] In this invention, the method acquires multi-dimensional geological data information other than resistivity measurement by collecting geographic mapping data. It then uses this multi-dimensional data to transform the target area into a three-dimensional model. The three-dimensional model data is then combined to locate the resistivity measurement position and extract relevant resistivity measurement data. The resistivity data is corrected using actual sampled data to obtain more accurate resistivity data. Furthermore, resistivity data from different depths measured by different resistivity instruments are rationally processed, analyzed, and fused. Finally, a more accurate modeling data is established compared to modeling using only resistivity measurement data. This effectively improves the accuracy of the modeling while ensuring its reliability, as resistivity data verified by multi-dimensional data can effectively eliminate accuracy deviations and interference caused by instrument problems in resistivity measurement.

[0009] One possible approach involves acquiring geographic mapping data, extracting mapping data from the exploration area, and forming target area mapping data. This includes: extracting image mapping data from the geographic mapping data, performing image-based modeling analysis, and establishing a 3D image mapping model of the target area; extracting sampling point measurement data from the geographic mapping data, performing sampling parameter transformation analysis, and forming sampled measurement transformation data; calibrating the location of the sampling points in the 3D image mapping model based on the sampling point measurement data, and determining the sampling calibration location information corresponding to different sampling points; and combining the 3D image mapping model of the area, the sampling calibration location information of different sampling points, and the sampled measurement transformation data to form the target area mapping data.

[0010] In this invention, the extraction of surveying data based on geographic mapping data mainly includes two aspects. Firstly, it involves performing feature analysis on measurement data from different dimensions other than resistivity measurement to form corresponding data models. This data from different dimensions includes image data acquired from the target area, as well as sampling analysis data from the field. Secondly, the surveying data from different dimensions is primarily used to verify and correct subsequent measurement data collected using resistivity measuring instruments; therefore, the data needs to be converted into similar data that can match the resistivity measurement data.

[0011] One possible approach involves extracting image mapping data from geographic surveying data, performing image-based modeling analysis, and establishing a 3D image mapping model of the target area. This includes: performing image grayscale processing on the image mapping data to extract the target area's corresponding image grayscale data; performing elevation correction on the target area's image grayscale data for non-geological cover based on the overburden elevation grayscale mapping data to form target area image grayscale elevation correction data; and performing modeling processing based on the target area image grayscale elevation correction data to form a 3D image mapping model of the target area.

[0012] In this invention, the establishment of a geological 3D image model of the target area using acquired image data mainly considers two aspects. Firstly, it involves data transformation of the image data into geographic model data. Image data extraction primarily involves obtaining the elevations of different locations within the target area. Converting the acquired image data into accurate elevation data is a mature data acquisition method. This can be achieved using drones equipped with radar, cameras, and other surveying equipment. The acquired image data is then converted into point cloud data and fitted to form a 3D model. Secondly, considering that the image data contains vegetation information located on the ground surface, which affects the elevation of corresponding locations, elevation correction for vegetation-covered areas is necessary.

[0013] As one possible implementation, the grayscale mapping data of the vegetation cover layer is obtained by: collecting image data corresponding to vegetation at different heights and performing grayscale processing to form grayscale values ​​of vegetation images corresponding to different heights. 'n' represents the number corresponding to image data of different vegetation heights; image data of areas not covered by vegetation are collected and processed into grayscale values ​​to form surface image grayscale values. Based on different vegetation image grayscale values and grayscale values ​​of surface images Determine the grayscale mapping value of vegetation elevation. ,in, Based on different vegetation elevation grayscale mapping values and the corresponding vegetation height value Continuity fitting analysis was performed to establish a gray-scale mapping function for the elevation of the overburden layer. .

[0014] In this invention, grayscale elevation mapping data of the vegetation cover layer is used to adjust the elevation of areas covered by vegetation, thereby avoiding the influence of vegetation height on the accuracy of the target area's surface elevation. The mapping data primarily establishes the correspondence between vegetation at different heights and their corresponding height values ​​in the grayscale data, thus accurately determining the required elevation adjustment for all vegetation-covered areas. Of course, the image data corresponding to the vegetation image grayscale values ​​must be collected using the same device or at the same height to minimize the impact of environmental and measurement equipment factors on data consistency. Furthermore, since grayscale value extraction cannot achieve reasonable continuous processing, discrete data extraction and fitting analysis are performed to achieve continuity in the mapping data. However, it is necessary to ensure a sufficient sample size for the discrete data to guarantee the accuracy of the fitting analysis. For grayscale values ​​of uncovered areas, the average of multiple discrete sample points can be used to improve the data's rationality and accuracy.

[0015] One possible approach is to extract sampling point measurement data from geographic mapping data, perform transformation analysis on sampling parameters, and form sampling measurement transformation data. This includes: extracting the geological parameters corresponding to each sampling point based on the sampling point measurement data; performing reverse transformation on the geological parameters corresponding to each sampling point based on geological interpretation data to form corresponding sampling point transformed resistivity information; and combining the sampling point transformed resistivity information corresponding to different sampling points to form sampling measurement transformation data.

[0016] In this invention, the analysis of sampling point measurement data serves two purposes. First, it provides new dimensions of data for the establishment of a three-dimensional geophysical model, enabling further verification and adjustment of resistivity data acquired using resistivity measuring instruments, thereby improving the accuracy and reliability of the resistivity data. Second, the raw data acquired at the sampling points primarily consists of parameters related to geological characteristics, including soil composition. This raw data is insufficient for verifying and adjusting resistivity data. Therefore, it is necessary to reasonably transform the sampling point measurement data into a data type consistent with resistivity data. This invention considers using geological interpretation data to reverse-transform the sampling point measurement data, generating corresponding resistivity data information. Geological interpretation data can be based on characteristic data obtained historically using resistivity measuring equipment for different geological conditions, or it can be derived from analyzing resistivity measurement data of the target area under different geological conditions. This ensures more reasonable data applicability.

[0017] One possible approach involves collecting geophysical measurement data and combining it with target area mapping data to analyze resistivity variations and establish a geophysical resistivity model for the target area. This includes: extracting shallow high-density detection data and deep low-density detection data from the geophysical measurement data; preprocessing the shallow high-density detection data to generate corresponding shallow high-density resistivity data; denoising the deep low-density detection data to generate corresponding deep low-density resistivity data; smoothing the interface based on the shallow high-density and deep low-density resistivity data to generate a resistivity model for the target area; mapping the target area resistivity model to coordinates based on the mapping 3D model to generate a 3D resistivity model for the target area; and verifying and analyzing the 3D resistivity model for the target area based on the sampled measurement data to form a geophysical resistivity model for the target area.

[0018] In this invention, to establish a geophysical resistivity model for the target area, it is first necessary to acquire resistivity data measured in the target area. Currently, the mainstream resistivity measurement method uses high-density electrical resistivity methods to perform high-density resistivity measurements in shallow areas to obtain shallow high-density detection data, and then uses the EH4 electromagnetic detection method to perform low-density resistivity measurements in deeper areas to obtain deep low-density resistivity data. Of course, the acquired raw data can be affected by geographical factors and equipment factors, which can affect the accuracy of the data. Therefore, targeted preprocessing is required to form accurate resistivity measurement data. Preprocessing methods are relatively mature. For example, preprocessing of shallow high-density detection data mainly includes removing skip points and terrain correction, while preprocessing of deep low-density detection data mainly includes noise removal and robust processing. Of course, the two types of detection data represent resistivity data at different depths within the target area. Therefore, it is necessary to fuse the two types of detection data to form resistivity model data, especially to establish a complete resistivity detection model of the target area. The established model needs to have coordinates to ensure data mapping at different locations within the detection area. A 3D mapping model can be used for coordinate positioning analysis of the model. However, the resulting data is still based on the data obtained by the resistivity measurement equipment and cannot fully guarantee the stability and accuracy of the data. After all, the accuracy of the data can also be affected by the equipment status. Therefore, the measurement data converted from sampling points is compared and adjusted to verify the data. While maintaining the stability of the data, the accuracy and precision of the data can be further improved based on the measured data. Here, it should also be noted that the coordinate mapping using the 3D mapping model mainly uses the geographical coordinates determined by the original resistivity model data to locate the data in the 3D mapping model. Then, based on the position information of the geographical coordinates in the resistivity model, the entire resistivity model is mapped to the coordinate system of the 3D mapping model to ensure that the resistivity model data matches the geographical data.

[0019] As one possible approach, based on shallow high-density resistivity data and deep low-density resistivity data, a smoothing process is performed on the interface to form a resistivity model for the target region. This includes: performing a fitting analysis on the continuous variation of resistivity based on the shallow high-density resistivity data to form a corresponding initial model for the shallow high-density resistivity. ,in, , This represents the resistivity values ​​at different locations in the initial model of shallow high-density resistivity. This represents the abscissa parameter of the shallow model in the initial model of shallow high-density resistivity, and , This represents the minimum shallow model x-axis value of the initial shallow high-density resistivity model. This represents the maximum abscissa value of the initial shallow surface high-density resistivity model. This represents the ordinate parameter of the shallow model in the initial model of shallow high-density resistivity, and , This represents the minimum shallow model ordinate value of the initial shallow high-density resistivity model. This represents the maximum shallow surface model ordinate value of the initial model for shallow high-density resistivity. This represents the axis coordinate parameters of the shallow model in the initial model of shallow high-density resistivity, and , Represents the minimum shallow model axis coordinate values ​​of the initial model for shallow high-density resistivity. This represents the maximum shallow model axis coordinate value of the initial model for shallow high-density resistivity; based on the deep low-density resistivity data, a fitting analysis is performed to address the continuous variation of resistivity, resulting in a corresponding deep low-density resistivity initial model. ,in, , This represents the resistivity values ​​at different locations in the initial low-density resistivity model. This represents the abscissa parameter of the depth model in the initial model of low-density resistivity at depth, and , This represents the ordinate parameter of the depth model in the initial model of low-density resistivity at depth, and , This represents the depth model axis coordinate parameters in the initial model of low-density resistivity at depth, and , This represents the minimum depth model axis coordinate value of the initial model for shallow high-density resistivity. This represents the maximum depth model axis coordinate value of the initial model for shallow high-density resistivity. Extracting the initial model of shallow high-density resistivity exist Resistivity data within the specified range are denoted as shallow overlapping range resistivity data; an initial model for low-density resistivity in depth is extracted. exist Resistivity data within the specified range is denoted as resistivity data within the overlapping depth range; based on the parameter value range corresponding to the horizontal axis parameter. and the range of values ​​for the corresponding ordinate parameter. Randomly select m planar location points and mark them as smooth sample location points. Using the axial direction as a reference, the positions of different smoothed sample points in the resistivity data of the shallow overlapping range are determined according to the order of axial coordinate parameters from shallow to deep. Corresponding shallow sample resistivity change function Using the axial direction as a reference, the positions of different smoothed sample points in the resistivity data within the overlapping depth range are determined according to the order of axial coordinate parameters from lightest to darkest. Corresponding resistivity variation function of depth sample For each smoothed sample location point Determine the corresponding shallow sample resistivity change function and resistivity variation function of depth sample cumulative resistivity between samples ,in, For each smoothed sample location point Determine the corresponding shallow sample resistivity change function and resistivity variation function of depth sample Cumulative rate of change between samples ,in, , Represents the function of resistivity change of shallow sample The derivative with respect to the axial coordinate parameters, Represents the resistivity variation function of the depth sample The derivative with respect to the axial coordinate parameters; based on different smoothing sample locations. Corresponding sample cumulative resistivity and cumulative rate of change of the sample Perform the following smoothing analysis: If ,and Then, taking the resistivity and resistivity change rate of the shallow overlapping resistivity data as a reference, an initial model of the deep low-density resistivity is developed based on the continuous variation law of resistivity. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. By merging these elements, a resistivity model for the target region can be formed. Indicates the limit of cumulative resistivity difference. This indicates the limit of the cumulative difference in resistivity change rate; if ,and Then, taking the average resistivity of the shallow overlapping resistivity data and the deep overlapping resistivity data at corresponding locations, as well as the resistivity change rate of the shallow overlapping resistivity data, as references, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. The resistivity model of the target region is formed by fusion; if ,and Then, taking the average resistivity change rate at corresponding locations of the shallow overlapping resistivity data and the deep overlapping resistivity data, as well as the resistivity in the shallow overlapping resistivity data, as a reference, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. The resistivity model of the target region is formed by fusion; if ,and Then, taking the average resistivity change rate and average resistivity at corresponding locations of the shallow overlapping resistivity data and the deep overlapping resistivity data as references, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. The resistivity model of the target region is formed by fusion.

[0020] In this invention, the smoothing process applied to the two resistivity measurements primarily aims to prevent abrupt changes in resistivity within a region due to overlapping data when fusing the two measurements to establish a complete resistivity model. Such abrupt changes contradict the continuous resistivity variations observed in actual geology, affecting the accuracy of the established resistivity model data and leading to inaccurate or even erroneous geological condition analyses, especially in overlapping areas. The smoothing process involves a reasonable comparative analysis of the two resistivity measurements to determine the most reasonable resistivity and the magnitude of the continuous resistivity variation rate, thereby completing the data fusion in that region. Of course, the smoothing analysis needs to consider two aspects: First, shallow resistivity model data has higher precision and accuracy but a smaller measurement scope, while deep resistivity models have relatively lower precision and accuracy but a larger measurement scope. Therefore, the two data types need to be considered together during smoothing fusion. Second, the model data is complete volumetric data; direct comparison would result in a large amount of data for comparison. Therefore, it is necessary to reasonably select data information for comparative analysis to achieve smooth fusion. Considering these two aspects, this application first uses resistivity data at the same locations on different models, referencing the horizontal and vertical coordinates, as representative data for smoothing analysis. This avoids the large amount of data required for analysis caused by directly comparing model data. The number of locations can be set as needed, or determined by combining the coordinates of actual measurement points to improve the accuracy of the extracted data. Axial resistivity variation information is obtained at the extracted locations, matching the cross-sectional data of the measurement data itself. This minimizes deviations caused by the fitted data between wall measurement points, allowing for a comparison of resistivity variations at corresponding locations. The comparison includes the cumulative difference in resistivity within the overlapping range and the cumulative difference in the rate of resistivity change. It is understandable that if both cumulative values ​​are within the limits, the difference between the two data can be considered small. Therefore, the data from the shallow resistivity model can be used as a reference to correct the less accurate deep resistivity model data. If the difference between the two data exceeds the limit, the difference between the two data is considered relatively large. Although the shallow resistivity data is more accurate, the influence of the deep data cannot be ruled out. Therefore, the average value is used for adjustment. It is necessary to consider the resistance change of the overlapping area and the boundary area of ​​the two data after the adjustment is implemented in the overlapping area. Therefore, the boundary area can be smoothed, or all data can be adjusted directly to match the adjusted data of the overlapping area.

[0021] As one possible approach, based on the converted sampling measurement data, a three-dimensional resistivity model of the target area is validated and analyzed to form a geophysical resistivity model of the target area. This includes: determining the resistivity values ​​of different sampling points based on the converted sampling measurement data. k represents the number of different sampling points; based on the sampling calibration location information corresponding to the sampling points, the points are located in the three-dimensional resistivity model of the target area, and the model resistivity corresponding to the located location is determined. Based on the resistivity values ​​of different sampling points and model resistivity The following verification analysis is performed: If all sampling points satisfy... Then the three-dimensional resistivity model of the target area is determined as the geophysical resistivity model of the target area, where, Indicates the resistivity verification limit; if there are sampling points that satisfy... For sampling points that meet the conditions, the resistivity value of the sampling point is used. Replacement model resistivity And based on the rate of change of resistivity in the three-dimensional resistivity model of the target area, the resistivity is adjusted over the entire range to form a geophysical resistivity model of the target area.

[0022] In this invention, the verification analysis of the three-dimensional resistivity model of the target area mainly ensures that the resistivity in the measured resistivity model data is closer to the actual value, thus ensuring the accuracy and reliability of the data. Verification is achieved by comparing the sampled data corresponding to the sampling point with the resistivity data at the corresponding location on the model. If the difference between the two sets of data does not exceed the verification limit, the resistivity data of the model itself is considered to be within the allowable deviation range, and no adjustment based on the sampled data is required. However, if the difference exceeds the verification limit, the resistivity data error of the model is considered to be relatively large, and adjustment is necessary. The adjustment method is based on the resistivity change rate of the model, directly replacing the resistivity of the sampling points exceeding the limit with the actual measured resistivity data, and then updating the resistivity values ​​across the entire region.

[0023] One possible approach is to acquire geological interpretation data, conduct geological structural analysis based on the geophysical resistance model of the target area, and establish a geophysical model of the target area. This includes: calibrating geological parameters at different locations on the geophysical resistance model of the target area based on the geological interpretation data; and rendering the calibrated geological parameters at different locations using different color information to form a geophysical model of the target area.

[0024] In this invention, the purpose of geological structure analysis is primarily to convert resistivity data into corresponding geological parameter data, because different geological bodies correspond to different resistivities, forming a one-to-one mapping. Therefore, after determining the corresponding geological parameters at different locations, rendering can be used to calibrate different geological bodies, thereby providing a more intuitive and clear display and presentation of the geological conditions of the target area.

[0025] Secondly, this invention provides a geophysical three-dimensional modeling system based on multi-dimensional data, comprising: a data acquisition unit for acquiring geographic mapping data, geophysical measurement data, and geological interpretation data; a mapping analysis unit for extracting mapping data from the geographic mapping data acquired by the data acquisition unit and establishing mapping data for the target area; a measurement modeling unit for performing resistivity change analysis based on the geophysical measurement data acquired by the data acquisition unit and combined with the mapping data for the target area formed by the mapping analysis unit, and establishing a geophysical resistivity model for the target area; and a geological interpretation unit for performing geological structural analysis on the geological interpretation data acquired by the data acquisition unit and combined with the geophysical resistivity model for the target area formed by the measurement modeling unit, and forming a geophysical model for the target area.

[0026] In this invention, the system uses a data acquisition unit to obtain the basic raw data required for establishing a geophysical model. A surveying and analysis unit extracts data features from multi-dimensional data, including sampling measurement data and surveying data. A measurement and modeling unit, while acquiring resistivity measurement data, combines the multi-dimensional data to correct and adjust the resistivity data, resulting in resistivity model data with higher accuracy and reliability. Finally, a geological interpretation unit completes the transformation into a geophysical model. Although the different units have different functions, they form a closely interconnected whole, forming a crucial material basis for establishing a multi-dimensional geophysical three-dimensional model.

[0027] The beneficial effects of the geophysical three-dimensional modeling method and system based on multi-dimensional data provided by this invention are as follows: This method acquires multi-dimensional geological data information, excluding resistivity measurement, by collecting geographic mapping data. It then uses this multi-dimensional data to transform the target area into a 3D model. The 3D model data is combined to locate the resistivity measurement positions and extract relevant resistivity measurement data. The resistivity data is corrected using actual sampled data to obtain more accurate resistivity data. Furthermore, resistivity data from different depths measured by different resistivity instruments are rationally processed, analyzed, and fused. Finally, a more accurate modeling dataset is established compared to modeling using only resistivity measurement data. This effectively improves modeling accuracy while ensuring data reliability, as resistivity data verified through multi-dimensional data can effectively eliminate accuracy deviations and interference caused by instrument malfunctions in resistivity measurement.

[0028] The system uses a data acquisition unit to obtain the basic raw data required for establishing a geophysical model. A mapping and analysis unit extracts data features from multi-dimensional data, including sampling and mapping data. A measurement and modeling unit, while acquiring resistivity measurement data, combines this multi-dimensional data to correct and adjust the resistivity data, resulting in more accurate and reliable resistivity model data. Finally, a geological interpretation unit completes the transformation into a geophysical model. Although the different units have different functions, they form a closely interconnected whole, providing a crucial material foundation for establishing a multi-dimensional geophysical three-dimensional model. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A step diagram illustrating the geophysical three-dimensional modeling method based on multi-dimensional data provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the structure of a geophysical three-dimensional modeling system based on multi-dimensional data provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0032] Three-dimensional modeling in geophysics has gradually become an important means of geological analysis. It mainly involves using measuring instruments to acquire measurement data, processing and transforming it to form a three-dimensional model. This method is efficient, fast and accurate.

[0033] However, most current 3D modeling methods rely on a single data acquisition approach, primarily using measurement data from measuring instruments. While the resulting 3D model depends on the accuracy of the measuring instruments, it lacks comparable data for reference. Consequently, the model data based on measuring instruments is not capable of resisting data interference caused by the inaccuracy of the testing instruments.

[0034] refer to Figures 1-2This invention provides a geophysical 3D modeling method based on multi-dimensional data. This method acquires multi-dimensional geological data information, excluding resistivity measurement methods, by collecting geographic survey data. It then uses this multi-dimensional data to transform the target area into a 3D model. The 3D model data is combined to locate the resistivity measurement position and extract relevant resistivity measurement data. The resistivity data is corrected using actual sampled data to obtain more accurate resistivity data. Furthermore, resistivity data from different depths measured by different resistivity instruments are rationally processed, analyzed, and fused. Finally, a more accurate modeling data is established compared to modeling using only resistivity measurement data. This effectively improves the accuracy of the modeling while ensuring its reliability, as resistivity data verified by multi-dimensional data can effectively eliminate accuracy deviations and interference caused by instrument problems in resistivity measurement instruments.

[0035] The geophysical 3D modeling method based on multi-dimensional data specifically includes the following steps: S1: Acquire geographic mapping data, extract mapping data of the exploration area, and form mapping data of the target area.

[0036] The process involves acquiring geographic mapping data, extracting mapping data from the exploration area, and forming target area mapping data. This includes: extracting image mapping data from the geographic mapping data, performing image-based modeling analysis, and establishing a 3D image mapping model of the target area; extracting sampling point measurement data from the geographic mapping data, performing sampling parameter transformation analysis, and forming sampled measurement transformation data; calibrating the location of the sampling points in the 3D image mapping model based on the sampling point measurement data, and determining the sampling calibration location information corresponding to different sampling points; and combining the 3D image mapping model of the area, the sampling calibration location information of different sampling points, and the sampled measurement transformation data to form the target area mapping data.

[0037] Data extraction based on geographic mapping data mainly involves two aspects. Firstly, it involves feature analysis of measurement data from different dimensions besides resistivity measurements to form corresponding data models. This data includes image data acquired from the target area, as well as sampling analysis data from the field. Secondly, the different dimensions of mapping data are primarily used to verify and correct subsequent measurement data collected using resistivity measuring instruments; therefore, the data needs to be transformed into similar data that can be matched with resistivity measurement data.

[0038] Image mapping data is extracted from geographic surveying data, and image-based modeling analysis is performed to establish a 3D image mapping model of the target area. This includes: performing image grayscale processing on the image mapping data to extract the grayscale data of the target area; performing elevation correction on the grayscale data of the target area image for non-geological cover based on the grayscale mapping data of the overburden layer, forming grayscale elevation correction data of the target area image; and performing modeling processing based on the grayscale elevation correction data of the target area image to form a 3D image mapping model of the target area.

[0039] Building a geological 3D image model of a target area using acquired image data mainly considers two aspects. First, it involves data transformation of the image data into geographic model data. Image data extraction primarily involves obtaining the elevations of different locations within the target area. Converting the acquired image data into accurate elevation data is a mature data acquisition method. This can be achieved using drones equipped with radar, cameras, and other surveying equipment. The acquired image data is then converted into point cloud data and fitted to form a 3D model. Second, considering that the image data includes vegetation information on the ground surface, which affects the elevation of corresponding locations, elevation correction for vegetation-covered areas is necessary.

[0040] The elevation grayscale mapping data of the vegetation cover layer is obtained by: collecting image data corresponding to vegetation at different heights and performing grayscale processing to form grayscale values ​​of vegetation images corresponding to different heights. 'n' represents the number corresponding to image data of different vegetation heights; image data of areas not covered by vegetation are collected and processed into grayscale values ​​to form surface image grayscale values. Based on different vegetation image grayscale values and grayscale values ​​of surface images Determine the grayscale mapping value of vegetation elevation. ,in, Based on different vegetation elevation grayscale mapping values and the corresponding vegetation height value Continuity fitting analysis was performed to establish a gray-scale mapping function for the elevation of the overburden layer. .

[0041] Grayscale elevation mapping data of vegetation cover is used to adjust the elevation of vegetated areas to avoid the vegetation height affecting the accuracy of the target area's surface elevation. The mapping data primarily establishes the correspondence between vegetation heights at different levels and their corresponding height values ​​in grayscale data, thus accurately determining the required elevation adjustment for all vegetated areas. Of course, the image data corresponding to the vegetation image grayscale values ​​must be acquired using the same equipment or at the same height to minimize the impact of environmental and measurement equipment factors on data consistency. Furthermore, since grayscale value extraction cannot achieve reasonable continuous processing, discrete data extraction and fitting analysis are performed to achieve continuity in the mapping data. However, it is necessary to ensure a sufficient sample size for the discrete data to guarantee the accuracy of the fitting analysis. For grayscale values ​​of uncovered areas, the average of multiple discrete sample points can be used to improve the data's rationality and accuracy.

[0042] Extracting sampling point measurement data from geographic surveying data, performing sampling parameter transformation analysis, and forming sampling measurement transformation data includes: extracting the geological parameters corresponding to each sampling point based on the sampling point measurement data; performing reverse transformation on the geological parameters corresponding to each sampling point based on geological interpretation data to form the corresponding sampling point transformed resistivity information; and combining the sampling point transformed resistivity information corresponding to different sampling points to form sampling measurement transformation data.

[0043] Analyzing the measurement data from sampling points serves two purposes. First, it provides new dimensions of data for establishing a three-dimensional geophysical model, allowing for further verification and adjustment of resistivity data acquired using resistivity measuring instruments, thereby improving the accuracy and reliability of the resistivity data. Second, the raw data obtained from sampling points primarily consists of parameters related to geological characteristics, including soil composition. This raw data is insufficient for verifying and adjusting resistivity data. Therefore, it is necessary to reasonably transform the sampling point measurement data into a data type consistent with resistivity data. This approach considers using geological interpretation data to reverse-transform the measurement data obtained from sampling points, generating corresponding resistivity data. Geological interpretation data can be based on characteristic data obtained historically using resistivity measuring equipment under different geological conditions, or it can be derived from analyzing resistivity measurement data newly conducted on the target area under different geological conditions. This ensures more reasonable data applicability.

[0044] S2: Collect geophysical measurement data and combine it with the target area mapping data to analyze resistivity changes and establish a geophysical resistance model for the target area.

[0045] Geophysical survey data was collected and resistivity variation analysis was performed in conjunction with target area mapping data to establish a geophysical resistivity model for the target area. This process included: extracting shallow high-density detection data and deep low-density detection data from the geophysical survey data; preprocessing the shallow high-density detection data to generate corresponding shallow high-density resistivity data; denoising the deep low-density detection data to generate corresponding deep low-density resistivity data; smoothing the interface based on the shallow high-density and deep low-density resistivity data to form a resistivity model for the target area; mapping the target area resistivity model to coordinates based on the mapping 3D model to form a 3D resistivity model for the target area; and verifying and analyzing the 3D resistivity model for the target area based on the sampled measurement data to finalize the geophysical resistivity model for the target area.

[0046] To establish a geophysical resistivity model for a target area, it is first necessary to acquire resistivity data measured in that area. Currently, the mainstream resistivity measurement method involves using high-density electrical resistivity methods to perform high-density resistivity measurements in shallow areas to obtain shallow high-density data, and then using the EH4 electromagnetic detection method to perform low-density resistivity measurements in deeper areas to obtain deep low-density resistivity data. Of course, the accuracy of the acquired raw data can be affected by factors, especially geographical and equipment factors. Therefore, targeted preprocessing is necessary to generate accurate resistivity measurement data. Preprocessing methods are relatively mature; for example, preprocessing of shallow high-density data mainly includes removing skip points and terrain correction, while preprocessing of deep low-density data mainly includes noise removal and robust processing. Of course, the two types of detection data represent resistivity data at different depths within the target area. Therefore, it is necessary to fuse the two types of detection data to form resistivity model data, especially to establish a complete resistivity detection model of the target area. The established model needs to have coordinates to ensure data mapping at different locations within the detection area. A 3D mapping model can be used for coordinate positioning analysis of the model. However, the resulting data is still based on the data obtained by the resistivity measurement equipment and cannot fully guarantee the stability and accuracy of the data. After all, the accuracy of the data can also be affected by the equipment status. Therefore, the measurement data converted from sampling points is compared and adjusted to verify the data. While maintaining the stability of the data, the accuracy and precision of the data can be further improved based on the measured data. Here, it should also be noted that the coordinate mapping using the 3D mapping model mainly uses the geographical coordinates determined by the original resistivity model data to locate the data in the 3D mapping model. Then, based on the position information of the geographical coordinates in the resistivity model, the entire resistivity model is mapped to the coordinate system of the 3D mapping model to ensure that the resistivity model data matches the geographical data.

[0047] Based on shallow high-density resistivity data and deep low-density resistivity data, smoothing processing is performed on the interface to form a resistivity model for the target region. This includes: performing fitting analysis on the continuous resistivity variation based on the shallow high-density resistivity data to form a corresponding initial model for the shallow high-density resistivity. ,in, , This represents the resistivity values ​​at different locations in the initial model of shallow high-density resistivity. This represents the abscissa parameter of the shallow model in the initial model of shallow high-density resistivity, and , This represents the minimum shallow model x-axis value of the initial shallow high-density resistivity model. This represents the maximum abscissa value of the initial shallow surface high-density resistivity model. This represents the ordinate parameter of the shallow model in the initial model of shallow high-density resistivity, and , This represents the minimum shallow model ordinate value of the initial shallow high-density resistivity model. This represents the maximum shallow surface model ordinate value of the initial model for shallow high-density resistivity. This represents the axis coordinate parameters of the shallow model in the initial model of shallow high-density resistivity, and , Represents the minimum shallow model axis coordinate values ​​of the initial model for shallow high-density resistivity. This represents the maximum shallow model axis coordinate value of the initial model for shallow high-density resistivity; based on the deep low-density resistivity data, a fitting analysis is performed to address the continuous variation of resistivity, resulting in a corresponding deep low-density resistivity initial model. ,in, , This represents the resistivity values ​​at different locations in the initial low-density resistivity model. This represents the abscissa parameter of the depth model in the initial model of low-density resistivity at depth, and , This represents the ordinate parameter of the depth model in the initial model of low-density resistivity at depth, and , This represents the depth model axis coordinate parameters in the initial model of low-density resistivity at depth, and , This represents the minimum depth model axis coordinate value of the initial model for shallow high-density resistivity. This represents the maximum depth model axis coordinate value of the initial model for shallow high-density resistivity. Extracting the initial model of shallow high-density resistivity exist Resistivity data within the specified range are denoted as shallow overlapping range resistivity data; an initial model for low-density resistivity in depth is extracted. exist Resistivity data within the specified range is denoted as resistivity data within the overlapping depth range; based on the parameter value range corresponding to the horizontal axis parameter. and the range of values ​​for the corresponding ordinate parameter. Randomly select m planar location points and mark them as smooth sample location points. Using the axial direction as a reference, the positions of different smoothed sample points in the resistivity data of the shallow overlapping range are determined according to the order of axial coordinate parameters from shallow to deep. Corresponding shallow sample resistivity change function Using the axial direction as a reference, the positions of different smoothed sample points in the resistivity data within the overlapping depth range are determined according to the order of axial coordinate parameters from lightest to darkest. Corresponding resistivity variation function of depth sample For each smoothed sample location point Determine the corresponding shallow sample resistivity change function and resistivity variation function of depth sample cumulative resistivity between samples ,in, For each smoothed sample location point Determine the corresponding shallow sample resistivity change function and resistivity variation function of depth sample Cumulative rate of change between samples ,in, , Represents the function of resistivity change of shallow sample The derivative with respect to the axial coordinate parameters, Represents the resistivity variation function of the depth sample The derivative with respect to the axial coordinate parameters; based on different smoothing sample locations. Corresponding sample cumulative resistivity and cumulative rate of change of the sample Perform the following smoothing analysis: If ,and Then, taking the resistivity and resistivity change rate of the shallow overlapping resistivity data as a reference, an initial model of the deep low-density resistivity is developed based on the continuous variation law of resistivity. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. By merging these elements, a resistivity model for the target region can be formed. Indicates the limit of cumulative resistivity difference. This indicates the limit of the cumulative difference in resistivity change rate; if ,and Then, taking the average resistivity of the shallow overlapping resistivity data and the deep overlapping resistivity data at corresponding locations, as well as the resistivity change rate of the shallow overlapping resistivity data, as references, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. The resistivity model of the target region is formed by fusion; if ,and Then, taking the average resistivity change rate at corresponding locations of the shallow overlapping resistivity data and the deep overlapping resistivity data, as well as the resistivity in the shallow overlapping resistivity data, as a reference, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. The resistivity model of the target region is formed by fusion; if ,and Then, taking the average resistivity change rate and average resistivity at corresponding locations of the shallow overlapping resistivity data and the deep overlapping resistivity data as references, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial model of shallow high-density resistivity. The resistivity model of the target region is formed by fusion.

[0048] The smoothing process applied to the two resistivity measurements primarily aims to prevent abrupt changes in resistivity within a region when fusing the two sets of data to establish a complete resistivity model. Such abrupt changes contradict the continuous resistivity variations observed in actual geology, impacting the accuracy of the established resistivity model and leading to inaccurate or even erroneous geological condition analyses, especially in overlapping areas. Smoothing involves a reasonable comparative analysis of the two resistivity measurements to determine the most appropriate resistivity and the magnitude of the continuous resistivity variation rate, thereby completing the data fusion in that region. Of course, the smoothing analysis needs to consider two aspects: First, shallow resistivity models have higher precision and accuracy but a smaller measurement scope, while deep resistivity models have relatively lower precision and accuracy but a larger measurement scope. Therefore, the two data sets need to be considered together during smoothing fusion. Second, the model data is complete volumetric data; direct comparison would result in a large amount of data for comparison. Therefore, it is necessary to rationally select data information for comparative analysis to achieve smooth fusion. Considering these two aspects, this application first uses resistivity data at the same locations on different models, referencing the horizontal and vertical coordinates, as representative data for smoothing analysis. This avoids the large amount of data required for analysis caused by directly comparing model data. The number of locations can be set as needed, or determined by combining the coordinates of actual measurement points to improve the accuracy of the extracted data. Axial resistivity variation information is obtained at the extracted locations, matching the cross-sectional data of the measurement data itself. This minimizes deviations caused by the fitted data between wall measurement points, allowing for a comparison of resistivity variations at corresponding locations. The comparison includes the cumulative difference in resistivity within the overlapping range and the cumulative difference in the rate of resistivity change. It is understandable that if both cumulative values ​​are within the limits, the difference between the two data can be considered small. Therefore, the data from the shallow resistivity model can be used as a reference to correct the less accurate deep resistivity model data. If the difference between the two data exceeds the limit, the difference between the two data is considered relatively large. Although the shallow resistivity data is more accurate, the influence of the deep data cannot be ruled out. Therefore, the average value is used for adjustment. It is necessary to consider the resistance change of the overlapping area and the boundary area of ​​the two data after the adjustment is implemented in the overlapping area. Therefore, the boundary area can be smoothed, or all data can be adjusted directly to match the adjusted data of the overlapping area.

[0049] Based on the converted sampling and measurement data, the three-dimensional resistivity model of the target area is validated and analyzed to form a geophysical resistivity model of the target area, including: determining the resistivity values ​​of different sampling points based on the converted sampling and measurement data. k represents the number of different sampling points; based on the sampling calibration location information corresponding to the sampling points, the points are located in the three-dimensional resistivity model of the target area, and the model resistivity corresponding to the located location is determined. Based on the resistivity values ​​of different sampling points and model resistivity The following verification analysis is performed: If all sampling points satisfy... Then the three-dimensional resistivity model of the target area is determined as the geophysical resistivity model of the target area, where, Indicates the resistivity verification limit; if there are sampling points that satisfy... For sampling points that meet the conditions, the resistivity value of the sampling point is used. Replacement model resistivity And based on the rate of change of resistivity in the three-dimensional resistivity model of the target area, the resistivity is adjusted over the entire range to form a geophysical resistivity model of the target area.

[0050] The verification analysis of the three-dimensional resistivity model of the target area mainly ensures that the resistivity in the measured resistivity model data is closer to the actual value, thus ensuring the accuracy and reliability of the data. Verification is achieved by comparing the sampled data at the corresponding sampling point with the resistivity data at the corresponding location on the model. If the difference between the two sets of data does not exceed the verification limit, the resistivity data of the model itself is considered to be within the allowable deviation range, and no adjustment based on the sampled data is required. However, if the difference exceeds the verification limit, the resistivity data error of the model is considered to be relatively large, and adjustment is necessary. The adjustment method is based on the resistivity change rate of the model; the resistivity of the sampling points exceeding the limit is directly replaced with the actual measured resistivity data, and then the resistivity values ​​are updated across the entire region.

[0051] S3: Obtain geological interpretation data, conduct geological structural analysis based on the geophysical resistivity model of the target area, and establish a geophysical model of the target area.

[0052] Obtain geological interpretation data, conduct geological structural analysis based on the geophysical resistance model of the target area, and establish a geophysical model of the target area, including: calibrating geological parameters at different locations on the geophysical resistance model of the target area based on the geological interpretation data; and rendering the calibrated geological parameters at different locations using different color information to form a geophysical model of the target area.

[0053] The purpose of geological structural analysis is primarily to convert resistivity data into corresponding geological parameter data, because different geological bodies correspond to different resistivities, forming a one-to-one mapping. Therefore, after determining the corresponding geological parameters at different locations, rendering can be used to calibrate different geological bodies, thereby providing a more intuitive and clear display and presentation of the geological conditions of the target area.

[0054] This invention also provides a geophysical three-dimensional modeling system based on multi-dimensional data. The system includes: a data acquisition unit for acquiring geographic mapping data, geophysical measurement data, and geological interpretation data; a mapping analysis unit for extracting mapping data from the geographic mapping data acquired by the data acquisition unit and establishing mapping data for the target area; a measurement modeling unit for analyzing resistivity changes based on the geophysical measurement data acquired by the data acquisition unit and the mapping data for the target area formed by the mapping analysis unit, and establishing a geophysical resistivity model for the target area; and a geological interpretation unit for analyzing the geological structure of the geological interpretation data acquired by the data acquisition unit and the geophysical resistivity model for the target area formed by the measurement modeling unit, and forming a geophysical model of the target area.

[0055] The system uses a data acquisition unit to obtain the basic raw data required for establishing a geophysical model. A mapping and analysis unit extracts data features from multi-dimensional data, including sampling and mapping data. A measurement and modeling unit, while acquiring resistivity measurement data, combines this multi-dimensional data to correct and adjust the resistivity data, resulting in more accurate and reliable resistivity model data. Finally, a geological interpretation unit completes the transformation into a geophysical model. Although the different units have different functions, they form a closely interconnected whole, providing a crucial material foundation for establishing a multi-dimensional geophysical three-dimensional model.

[0056] In summary, the beneficial effects of the geophysical three-dimensional modeling method and system based on multi-dimensional data provided in the embodiments of the present invention are as follows: This method acquires multi-dimensional geological data information, excluding resistivity measurement, by collecting geographic mapping data. It then uses this multi-dimensional data to transform the target area into a 3D model. The 3D model data is combined to locate the resistivity measurement positions and extract relevant resistivity measurement data. The resistivity data is corrected using actual sampled data to obtain more accurate resistivity data. Furthermore, resistivity data from different depths measured by different resistivity instruments are rationally processed, analyzed, and fused. Finally, a more accurate modeling dataset is established compared to modeling using only resistivity measurement data. This effectively improves modeling accuracy while ensuring data reliability, as resistivity data verified through multi-dimensional data can effectively eliminate accuracy deviations and interference caused by instrument malfunctions in resistivity measurement.

[0057] The system uses a data acquisition unit to obtain the basic raw data required for establishing a geophysical model. A mapping and analysis unit extracts data features from multi-dimensional data, including sampling and mapping data. A measurement and modeling unit, while acquiring resistivity measurement data, combines this multi-dimensional data to correct and adjust the resistivity data, resulting in more accurate and reliable resistivity model data. Finally, a geological interpretation unit completes the transformation into a geophysical model. Although the different units have different functions, they form a closely interconnected whole, providing a crucial material foundation for establishing a multi-dimensional geophysical three-dimensional model.

[0058] In the embodiments of this application, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In the specific implementation process, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a relationship between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. At the same time, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0059] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be repeated here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In the specific implementation process, the required indication method can be selected according to specific needs. This application embodiment does not limit the selected indication method; therefore, the indication methods involved in this application embodiment should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0060] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this application embodiment. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0061] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This application does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or communication device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0062] The “protocol” mentioned in this application embodiment may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to future communication systems. This application embodiment does not specifically limit this.

[0063] In the embodiments of this application, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0064] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0065] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0066] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0067] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0068] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0069] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0070] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0071] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0072] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0073] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0074] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0075] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0076] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0077] The above description is merely a specific embodiment of this application, but the scope of protection of this application 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 geophysical three-dimensional modeling method based on multi-dimensional data, characterized in that, include: Acquire geographic mapping data, extract mapping data from the exploration area, and form mapping data for the target area; Collect geophysical measurement data and combine it with the target area mapping data to analyze resistivity changes and establish a geophysical resistance model for the target area; Geological interpretation data is obtained, and geological structural analysis is performed based on the geophysical resistivity model of the target area to establish a geophysical model of the target area.

2. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 1, characterized in that, The process of acquiring geographic mapping data, extracting mapping data from the exploration area, and forming target area mapping data includes: Image mapping data is extracted from the geographic mapping data, and image-based modeling analysis is performed to establish a three-dimensional image mapping model of the target area. Extract the sampling point measurement data from the geographic mapping data, perform transformation analysis on the sampling parameters, and form transformed sampling measurement data; Based on the measurement data of the sampling points, position calibration is performed in the three-dimensional model of the regional image mapping to determine the sampling calibration position information corresponding to different sampling points; The target area mapping data is formed by combining the three-dimensional model of the area image, the sampling calibration location information of different sampling points, and the sampling measurement conversion data.

3. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 2, characterized in that, The step of extracting image mapping data from the geographic mapping data, performing image-based modeling analysis, and establishing a three-dimensional image mapping model of the target area includes: The image mapping data is subjected to grayscale processing of the target region, and the grayscale data of the target region image corresponding to the target region is extracted; Based on the grayscale mapping data of the overburden elevation, the grayscale data of the target area image is corrected for non-geological overburden to form grayscale elevation correction data of the target area image. The target area image grayscale elevation correction data is used for modeling to form a three-dimensional model of the target area image.

4. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 3, characterized in that, The elevation grayscale mapping data of the overburden layer is obtained through the following methods: Image data corresponding to vegetation at different heights were collected and processed into grayscale values ​​to form vegetation image grayscale values ​​corresponding to different vegetation heights. , where n represents the number corresponding to the image data of vegetation at different heights; Image data of unvegetated areas are collected and converted to grayscale to form grayscale values ​​of the land surface. ; Based on different grayscale values ​​of the vegetation images and the grayscale value of the surface image Determine the grayscale mapping value of vegetation elevation. ,in, ; Based on different vegetation elevation grayscale mapping values and the corresponding vegetation height value Continuity fitting analysis was performed to establish a gray-scale mapping function for the elevation of the overburden layer. .

5. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 4, characterized in that, The step of extracting sampling point measurement data from the geographic mapping data, performing sampling parameter transformation analysis, and forming transformed sampling measurement data includes: For different sampling points, the geological parameters corresponding to each sampling point are extracted based on the measurement data of the sampling points; Based on the geological interpretation data, the geological parameters of each sampling point are reverse-transformed to form the corresponding sampling point transformed resistivity information; The resistivity information of the sampling points corresponding to different sampling points is collected to form the sampling measurement conversion data.

6. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 5, characterized in that, The collected geophysical measurement data, combined with the target area mapping data, is used to analyze resistivity changes and establish a geophysical resistivity model for the target area, including: Based on the geophysical survey data, shallow high-density detection data and deep low-density detection data are extracted respectively. The shallow high-density detection data is preprocessed to form corresponding shallow high-density resistivity data; The low-density depth detection data is denoised to generate corresponding low-density depth resistivity data. Based on the shallow high-density resistivity data and the deep low-density resistivity data, a smoothing process is performed on the interface to form a resistivity model of the target region. Based on the three-dimensional model, coordinate mapping is performed on the resistivity model of the target area to form a three-dimensional resistivity model of the target area. Based on the sampled measurement data, the three-dimensional resistivity model of the target area is verified and analyzed to form the geophysical resistivity model of the target area.

7. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 6, characterized in that, The step of smoothing the interface based on the shallow high-density resistivity data and the deep low-density resistivity data to form a resistivity model for the target region includes: Based on the shallow high-density resistivity data, a fitting analysis is performed on the continuous variation of resistivity to form a corresponding initial model for shallow high-density resistivity. ,in, , This represents the resistivity values ​​at different locations in the initial model of shallow high-density resistivity. This represents the abscissa parameter of the shallow model in the initial model of shallow high-density resistivity, and , This represents the minimum shallow model x-axis value of the initial shallow high-density resistivity model. This represents the maximum abscissa value of the initial shallow surface high-density resistivity model. This represents the ordinate parameter of the shallow model in the initial model of shallow high-density resistivity, and , This represents the minimum shallow model ordinate value of the initial shallow high-density resistivity model. This represents the maximum shallow surface model ordinate value of the initial model for shallow high-density resistivity. This represents the axis coordinate parameters of the shallow model in the initial model of shallow high-density resistivity, and , Represents the minimum shallow model axis coordinate values ​​of the initial model for shallow high-density resistivity. This represents the maximum shallow model axis coordinate value of the initial model for shallow high-density resistivity. Based on the aforementioned low-density resistivity data, a fitting analysis is performed to address the continuous variation in resistivity, resulting in a corresponding initial model for low-density resistivity in the deep region. ,in, , This represents the resistivity values ​​at different locations in the initial low-density resistivity model. This represents the abscissa parameter of the depth model in the initial model of low-density resistivity at depth, and , This represents the ordinate parameter of the depth model in the initial model of low-density resistivity at depth, and , This represents the depth model axis coordinate parameters in the initial model of low-density resistivity at depth, and , This represents the minimum depth model axis coordinate value of the initial model for shallow high-density resistivity. This represents the maximum depth model axis coordinate value of the initial model for shallow high-density resistivity. ; Extracting the initial model of shallow high-density resistivity exist Resistivity data within the specified range are denoted as shallow overlapping range resistivity data. Extracting the initial model of low-density resistivity in depth exist Resistivity data within the specified range are denoted as resistivity data within the overlapping depth range. Based on the range of values ​​corresponding to the horizontal axis parameter and the range of values ​​for the corresponding ordinate parameter. Randomly select m planar location points and mark them as smooth sample location points. ; Using the axial direction as a reference, the different smoothed sample positions in the resistivity data of the shallow overlapping range are determined according to the order of axial coordinate parameters from shallow to deep. Corresponding shallow sample resistivity change function ; Using the axial direction as a reference, the different smoothed sample positions in the resistivity data within the overlapping depth range are determined according to the axial coordinate parameters from lightest to darkest. Corresponding resistivity variation function of depth sample ; For each of the smoothed sample location points Determine the resistivity change function corresponding to the shallow sample. and the resistivity change function of the depth sample cumulative resistivity between samples ,in, ; For each of the smoothed sample location points Determine the resistivity change function corresponding to the shallow sample. and the resistivity change function of the depth sample Cumulative rate of change between samples ,in, , Represents the resistivity change function of the shallow sample The derivative with respect to the axial coordinate parameters, The resistivity variation function of the depth sample The derivative with respect to the axial coordinate parameters; Based on different smoothing sample location points The corresponding sample cumulative resistivity and the cumulative rate of change of the sample The following smoothing analysis was performed: like ,and Then, taking the resistivity and resistivity change rate of the resistivity data in the shallow overlapping range as a reference, an initial model of the deep low-density resistivity is developed based on the continuous change law of resistivity. Resistivity adjustment was performed, and the adjusted model was compared with the initial shallow high-density resistivity model. The resistivity model of the target region is formed by fusion. Indicates the limit of cumulative resistivity difference. This indicates the limit of the cumulative difference in resistivity change rate; like ,and Then, taking the average resistivity of the shallow overlapping resistivity data and the deep overlapping resistivity data at corresponding locations, as well as the resistivity change rate of the shallow overlapping resistivity data, as references, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial shallow high-density resistivity model. The resistivity model of the target region is formed by fusion. like ,and Then, taking the average resistivity change rate at corresponding locations of the shallow overlapping resistivity data and the deep overlapping resistivity data, as well as the resistivity on the shallow overlapping resistivity data, as a reference, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial shallow high-density resistivity model. The resistivity model of the target region is formed by fusion. like ,and Then, taking the average resistivity change rate and average resistivity at corresponding locations of the shallow overlapping resistivity data and the deep overlapping resistivity data as references, an initial model of the deep low-density resistivity is developed based on the continuous resistivity variation law. Resistivity adjustment was performed, and the adjusted model was compared with the initial shallow high-density resistivity model. The resistivity model of the target region is formed by fusion.

8. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 7, characterized in that, The step of verifying and analyzing the three-dimensional resistivity model of the target area based on the sampled measurement data to form the geophysical resistivity model of the target area includes: Based on the sampled measurement data, determine the resistivity values ​​of different sampling points. k represents the number of the different sampling points; Based on the sampling calibration location information corresponding to the sampling point, the target area resistivity is located in the three-dimensional model and the model resistivity corresponding to the located location is determined. ; Based on the resistivity values ​​of the sampling points corresponding to different sampling points and the resistivity of the model The following verification and analysis methods will be used: If all sampling points satisfy Then the three-dimensional resistivity model of the target area is determined as the geophysical resistivity model of the target area, wherein, Indicates the resistivity verification limit; If there exist sampling points that satisfy Then, for the sampling points that meet the conditions, the resistivity value of the sampling points is used. Replace the resistivity of the model And based on the rate of change of resistivity in the three-dimensional resistivity model of the target area, the resistivity is adjusted over the entire range to form the geophysical resistivity model of the target area.

9. The geophysical three-dimensional modeling method based on multi-dimensional data according to claim 8, characterized in that, The process of acquiring geological interpretation data, performing geological structural analysis based on the geophysical resistivity model of the target area, and establishing a geophysical model of the target area includes: Based on the geological interpretation data, the geological parameters of the geophysical resistance model of the target area are calibrated at different locations; Based on the geological parameters marked at different locations, different color information is used for rendering to form a geophysical model of the target area.

10. A geophysical three-dimensional modeling system based on multi-dimensional data, employing the geophysical three-dimensional modeling method based on multi-dimensional data as described in any one of claims 1-9, characterized in that, include: The data acquisition unit is used to acquire geographic mapping data, geophysical survey data, and geological interpretation data. The surveying and mapping analysis unit is used to extract surveying and mapping data from the geographic surveying and mapping data acquired by the data acquisition unit and establish surveying and mapping data for the target area. The measurement modeling unit is used to perform resistivity change analysis based on the geophysical measurement data obtained by the data acquisition unit and the target area mapping data formed by the mapping analysis unit, and to establish a geophysical resistivity model of the target area. The geological interpretation unit is used to analyze the geological structure of the target area by combining the geological interpretation data obtained by the data acquisition unit with the geophysical resistivity model of the target area formed by the measurement and modeling unit, and to form a geophysical model of the target area.

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