Method for establishing topographic and geological model based on geophysical technology

Abstract

This invention discloses a method for establishing a topographic and geological model based on geophysical technology, belonging to the field of data processing technology. It addresses the technical problem of inaccurate topographic and geological models caused by surface data distortion due to vegetation interference in vegetated areas during current topographic and geological modeling. The method includes: acquiring surface resistivity data of the target area and meshing the surface resistivity data into two-dimensional resistivity profile data; determining the vegetation complexity of the target area based on the two-dimensional resistivity profile data; adaptively dividing the target area into multiple sub-regions based on the vegetation complexity and the two-dimensional resistivity profile data; performing local two-dimensional inversion optimization and adaptive vegetation stripping on each sub-region to obtain the geological parameters of each sub-region after vegetation stripping; and constructing a three-dimensional topographic and geological model of the target area based on the geological parameters after vegetation stripping.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a method for establishing a terrain and geological model based on geophysical exploration technology. Background Technology

[0002] Topographic and geological models, as digital representations of surface morphology and subsurface geological structures, have core application value in multiple fields such as resource exploration, engineering construction, geological hazard assessment, and environmental monitoring. They serve as a crucial technological foundation supporting scientific decision-making and practical work in these fields. Geophysical exploration technology can indirectly obtain subsurface topographic and geological information by measuring the physical properties of subsurface media. Therefore, exploring more reliable methods for building topographic and geological models based on geophysical technology is of great significance for improving the practicality and accuracy of model construction and promoting technological development in related fields.

[0003] Currently, existing 3D topographic and geological modeling technologies mostly rely on optical remote sensing or lidar technology to obtain topographic and geological information of the target area. These technologies can provide basic data for model building in some scenarios. For example, in areas with less vegetation cover or relatively simple terrain, the collection and modeling of topographic and geological information can be initially realized, providing some support for subsequent work.

[0004] However, existing modeling technologies based on optical remote sensing or lidar are insufficient to effectively address the interference of vegetation on surface signals in vegetated areas, especially those with complex vegetation distribution. This leads to discrepancies between the acquired topographic and surface data and the actual surface conditions, ultimately resulting in inaccurate topographic and geological models that fail to meet the actual needs of high-precision models in fields such as resource exploration and engineering construction. Consequently, the overall modeling effect falls short of application requirements. Summary of the Invention

[0005] To address the technical problem of inaccurate topographic and geological models caused by surface data distortion due to vegetation disturbance in vegetated areas during current topographic and geological modeling, this invention aims to provide a method for establishing topographic and geological models based on geophysical exploration technology. The specific technical solution adopted is as follows:

[0006] In a first aspect, the present invention provides a method for establishing a topographic and geological model based on geophysical technology, comprising: acquiring surface resistivity data of a target area and gridding the surface resistivity data into two-dimensional resistivity profile data; determining the vegetation complexity of the target area based on the two-dimensional resistivity profile data; wherein the vegetation complexity is used to characterize the degree of interference of vegetation on the surface resistivity signal in the target area; adaptively dividing the target area into multiple sub-regions based on the vegetation complexity and the two-dimensional resistivity profile data; performing local two-dimensional inversion optimization and adaptive vegetation stripping for each sub-region to obtain the geological parameters of each sub-region after vegetation stripping; wherein the geological parameters after vegetation stripping are used to characterize the true stratigraphic resistivity distribution after eliminating the influence of vegetation aquifers; and constructing a three-dimensional topographic and geological model of the target area based on the geological parameters after vegetation stripping.

[0007] In one possible implementation, the vegetation complexity of the target area is determined based on two-dimensional resistivity profile data. Specifically, this includes: dividing the target area into multiple initial regions based on the two-dimensional resistivity profile data; for each initial region, counting the number of low-resistivity points, and determining the vegetation density index based on the number of low-resistivity points and the total number of measurement points; wherein, low-resistivity points are measurement points whose resistivity is lower than a preset resistivity threshold among all measurement points; and calculating the vegetation complexity based on the vegetation density index, the area of ​​each initial region, and the area of ​​the two-dimensional resistivity profile.

[0008] In one possible implementation, vegetation complexity is calculated based on the vegetation density index, the area of ​​each initial region, and the area of ​​the two-dimensional resistivity profile. Specifically, this includes: determining the resistivity spatial variation coefficient of each initial region based on the resistivity standard deviation and resistivity mean of multiple initial regions; determining a reference area coefficient based on the vegetation density index, and determining a reference area based on the reference area coefficient and the area of ​​the two-dimensional resistivity profile; and calculating the vegetation complexity based on the resistivity spatial variation coefficient, the area of ​​each initial region, and the reference area.

[0009] In one possible implementation, the target region is adaptively divided based on vegetation complexity and two-dimensional resistivity profile data. Specifically, this includes: determining the mean and standard deviation of vegetation complexity in the initial region; determining a dynamic threshold based on the mean and standard deviation of vegetation complexity in the initial region, and a dynamic weighting coefficient; wherein the dynamic weighting coefficient is dynamically adjusted based on the data signal-to-noise ratio and the number of initial region divisions; for each initial region, iteratively dividing the initial region into multiple sub-regions based on its vegetation complexity and the dynamic threshold, combined with a preset clustering algorithm; wherein the preset clustering algorithm includes the K-means clustering algorithm.

[0010] In one possible implementation, local two-dimensional inversion optimization is performed for each sub-region, specifically including: determining a data weight matrix based on the vegetation complexity of the sub-region; wherein the data weight matrix is ​​used to control the inversion weights of the data fitting terms; constructing an inversion objective function based on the data weight matrix and two-dimensional resistivity profile data; wherein the inversion objective function includes data fitting terms and model constraint terms; and obtaining optimized geological parameters by solving the inversion objective function.

[0011] In one possible implementation, adaptive vegetation stripping is performed for each sub-region, specifically including: obtaining the vegetation layer depth and vegetation layer resistivity of the sub-region; constructing a vegetation stripping function based on the vegetation layer depth and vegetation layer resistivity of the sub-region; using the vegetation stripping function to process the optimized geological parameters and outputting the geological parameters after vegetation stripping.

[0012] In one possible implementation, a three-dimensional topographic and geological model of the target area is constructed based on the geological parameters after vegetation stripping. Specifically, this includes: converting the geological parameters after vegetation stripping into three-dimensional spatial point cloud data; and constructing a three-dimensional topographic and geological model based on the three-dimensional spatial point cloud data.

[0013] In one possible implementation, the geological parameters after vegetation stripping are converted into three-dimensional spatial point cloud data. Specifically, this includes: assigning three-dimensional spatial coordinates and physical property values ​​to each data point based on the horizontal position information, vertical depth information, and resistivity value contained in the geological parameters after vegetation stripping; and performing spatial interpolation processing on the data points with three-dimensional spatial coordinates and physical property values ​​based on preset distance parameters and preset interpolation algorithms to generate three-dimensional spatial point cloud data.

[0014] In one possible implementation, a three-dimensional topographic and geological model is constructed based on three-dimensional spatial point cloud data. Specifically, this includes: processing the three-dimensional spatial point cloud data according to a preset surface reconstruction algorithm, wherein the preset surface reconstruction algorithm uses a distance-weighted function to fit the data; mapping the stratigraphic properties of the fitted surface through physical property values ​​to construct a three-dimensional topographic and geological model containing stratigraphic property information.

[0015] In one possible implementation, before gridding the surface resistivity data into two-dimensional resistivity profile data, the method further includes performing data preprocessing on the surface resistivity data; wherein the data preprocessing includes data correction and noise filtering.

[0016] Secondly, the present invention provides a topographic and geological model establishment system based on geophysical exploration technology, including: a data acquisition and processing module 11, a vegetation complexity analysis module 12, a region division module 13, a vegetation stripping calculation module 14, and a three-dimensional model construction module 15. The data acquisition and processing module 11 acquires surface resistivity data of the target area and meshes the surface resistivity data into two-dimensional resistivity profile data. The vegetation complexity analysis module 12 determines the vegetation complexity of the target area based on the two-dimensional resistivity profile data. The vegetation complexity is used to characterize the degree of interference of vegetation on the surface resistivity signal in the target area. The region division module 13 performs adaptive region division of the target area based on the vegetation complexity and the two-dimensional resistivity profile data to obtain multiple sub-regions. The vegetation stripping calculation module 14 performs local two-dimensional inversion optimization and adaptive vegetation stripping for each sub-region to obtain the geological parameters of each sub-region after vegetation stripping. The geological parameters after vegetation stripping are used to characterize the true stratigraphic resistivity distribution after eliminating the influence of vegetation aquifers. The three-dimensional model construction module 15 constructs a three-dimensional topographic and geological model of the target area based on the geological parameters after vegetation stripping.

[0017] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer execution instructions, and when the electronic device is running, the processor executes the computer execution instructions stored in the memory to cause the electronic device to perform the topographic and geological model establishment method based on geophysical technology as described in the first aspect and any possible implementation thereof.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by an electronic device of the present invention, cause the electronic device to perform a topographic and geological model building method based on geophysical technology as described in the first aspect and any possible implementation thereof.

[0019] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the electronic device of the present invention to perform the topographic and geological model building method based on geophysical technology as described in the first aspect and any possible implementation thereof.

[0020] In a sixth aspect, the present invention provides a chip system applied to a water meter data acquisition device; the chip system includes one or more interface circuits and one or more processors. The interface circuits and the processors are interconnected via lines; the interface circuits are used to receive signals from the memory of the water meter data acquisition device and send the signals to the processors, the signals including computer instructions stored in the memory. When the processor executes the computer instructions, the water meter data acquisition device performs the topographic geological model establishment method based on geophysical technology as described in the first aspect and any possible design embodiment.

[0021] This invention offers the following advantages: Raw surface resistivity data of the target area is collected using ground-based geophysical equipment. After data correction and moving average filtering to eliminate equipment and noise interference, the denoised data is gridded and converted into two-dimensional resistivity profile data. Subsequently, based on the two-dimensional resistivity profile data, initial regions are divided, the number of low-resistivity points is counted to determine the vegetation density index, and vegetation complexity is calculated by combining the resistivity spatial variation coefficient with the region area. Adaptive region division is achieved based on vegetation complexity and a dynamic threshold, using a clustering algorithm to avoid homogenizing different vegetation cover areas such as forests and grasslands, thus preserving the true topography of low-vegetation-complexity areas. For each sub-region, regularized least-squares inversion is performed. Local two-dimensional inversion optimization is performed, and then a stripping function associated with vegetation complexity is constructed to eliminate shallow false anomalies caused by vegetation aquifers, obtaining geological parameters of the true stratigraphic resistivity distribution. Finally, these geological parameters are converted into three-dimensional point cloud data containing three-dimensional spatial coordinates and physical property values. The moving least squares method is used to fit the surface and combined with resistivity to map stratigraphic properties, constructing a three-dimensional topographic and geological model containing stratigraphic properties. This ultimately solves the problem of inaccurate topographic and geological models caused by vegetation interference in existing technologies. The output model can accurately reflect the surface morphology and underground geological structure and properties, and can effectively support decision-making needs in fields such as resource exploration, engineering construction, geological hazard assessment, and environmental monitoring. Attached Figure Description

[0022] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of the architecture of a terrain and geological model building system based on geophysical technology provided in one embodiment of the present invention;

[0024] Figure 2This is a schematic diagram of the architecture of a vegetation complexity analysis module provided in one embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the architecture of a vegetation stripping calculation module provided in one embodiment of the present invention;

[0026] Figure 4 This is a flowchart illustrating a method for establishing a topographic and geological model based on geophysical exploration technology, provided in one embodiment of the present invention.

[0027] Figure 5 A flowchart illustrating another method for establishing a topographic and geological model based on geophysical exploration technology, provided in one embodiment of the present invention;

[0028] Figure 6 A flowchart illustrating another method for establishing a topographic and geological model based on geophysical exploration technology, provided in one embodiment of the present invention;

[0029] Figure 7 This is a flowchart illustrating another method for establishing a topographic and geological model based on geophysical exploration technology, provided as an embodiment of the present invention. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for establishing a topographic and geological model based on geophysical exploration technology proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] The technical terms involved in this invention are explained below:

[0033] 1. Geophysical exploration technology.

[0034] Geophysical exploration, or geophysical prospecting, is a technique that indirectly obtains information about the subsurface topography and geological structure by measuring the physical properties of underground media (such as resistivity, density, and magnetism). It eliminates the need for direct excavation or drilling, efficiently covers large areas, and is widely used in resource exploration, engineering construction, and geological hazard assessment. In this embodiment of the invention, geophysical exploration is primarily used to collect raw surface resistivity data of the target area, providing fundamental data support for subsequent topographic and geological model construction. This is a core technological foundation for solving vegetation interference problems and improving model accuracy.

[0035] 2. Topographic and geological model.

[0036] A topographic geological model is a digital representation of surface morphology (such as slope and elevation) and underground geological structure (such as stratigraphic distribution and rock properties). By transforming geological information into a calculable and visualizeable digital form, it provides intuitive evidence for decision-making in related fields, such as assisting in determining vein distribution in resource exploration and guiding foundation design in engineering construction. The topographic geological model constructed in this embodiment of the invention focuses on combining resistivity data obtained through geophysical exploration techniques, eliminating vegetation interference, and achieving accurate depiction of surface and underground geological information to meet the needs of high-precision applications.

[0037] 3. Two-dimensional resistivity profile.

[0038] A two-dimensional resistivity profile is a set of two-dimensional data along a specific profile direction, formed after preprocessing the collected raw surface resistivity data through data correction, noise filtering, and spatial gridding. It reflects the resistivity distribution of the subsurface medium at different locations and depths within the profile. Resistivity is a key physical property of the subsurface medium, closely related to vegetation root water content and stratigraphic lithology. Therefore, the two-dimensional resistivity profile is not only a core data carrier for subsequent calculations of vegetation complexity but also provides direct evidence for analyzing subsurface geological structures, serving as a crucial bridge connecting raw data with subsequent modeling and processing.

[0039] The following description, in conjunction with the accompanying drawings, details a specific scheme for establishing a topographic and geological model based on geophysical exploration technology provided by this invention.

[0040] Please see Figure 1 This diagram illustrates the architecture of a terrain and geological model building system based on geophysical technology, according to an embodiment of the present invention. The system 10 includes: a data acquisition and processing module 11, a vegetation complexity analysis module 12, a region division module 13, a vegetation stripping calculation module 14, and a 3D model construction module 15. Through the collaborative work of these modules, the system 10 can realize the entire process from data acquisition to model output. The modules are described below in sequence:

[0041] (1) Data acquisition and processing module 11.

[0042] The data acquisition and processing module 11 is responsible for acquiring the original geophysical data of the target area, eliminating interference, and converting the data format, so as to provide standardized analysis data for subsequent modules.

[0043] Optionally, the data acquisition and processing module 11 is used to acquire resistivity data of the target area, providing basic data support for subsequent modules. The resistivity data is used to characterize the physical properties of the underground medium and can indirectly reflect vegetation distribution and geological structure characteristics.

[0044] Specifically, the data acquisition and processing module 11 first collects the original surface resistivity data of the target area through ground geophysical exploration equipment. This surface resistivity data can indirectly reflect the vegetation distribution, and the resistivity is lower in areas with high root water content of vegetation.

[0045] Optionally, the data acquisition and processing module 11 performs preprocessing on the raw data. First, it corrects the data by setting the device calibration coefficient and baseline offset to eliminate device system errors. Then, it uses a moving average filtering method to filter noise and remove environmental interference.

[0046] Furthermore, the data acquisition and processing module 11 performs spatial gridding processing on the denoised data, and converts the one-dimensional resistivity data into two-dimensional resistivity profile data by setting the horizontal distance between the grid and the measuring point.

[0047] (2) Vegetation complexity analysis module 12.

[0048] The vegetation complexity analysis module 12 is responsible for calculating the vegetation disturbance quantification index of the target area based on the two-dimensional resistivity profile data of the data acquisition module 11, providing a basis for judgment in subsequent differentiated disturbance processing.

[0049] Optionally, the vegetation complexity analysis module 12 is used to determine the vegetation complexity of the target area based on two-dimensional resistivity profile data. The vegetation complexity characterizes the degree of interference of vegetation within the target area on the surface resistivity signal.

[0050] For example, such as Figure 2 As shown, the vegetation complexity analysis module 12 may include three sub-modules: an initial region coarse molecular module 121, a vegetation density index calculation sub-module 122, and a vegetation complexity calculation sub-module 123. These sub-modules are described below:

[0051] (2.1) Initial region coarse molecular module 121.

[0052] Optionally, the initial region coarse molecule module 121 is used to divide the target region into multiple initial regions based on two-dimensional resistivity profile data.

[0053] Specifically, the initial region coarse molecular module 121 takes the two-dimensional resistivity profile data output by the data acquisition module 11 as input and performs an average coarse division of the target region according to the profile length (for example, a 200m long two-dimensional resistivity profile is divided into 4 initial regions), ensuring that the spatial scale of each initial region is suitable for subsequent vegetation density and complexity calculations, and avoiding interference quantification deviations caused by excessively large regions.

[0054] (2.2) Vegetation density index calculation submodule 122.

[0055] Optionally, the vegetation density index calculation submodule 122 is used to count the number of low-resistivity points for each initial area and determine the vegetation density index based on the number of low-resistivity points and the total number of measurement points. Low-resistivity points are those among all measurement points whose resistivity is lower than a preset resistivity threshold. The preset resistivity threshold is generally set to 10%, or may be determined according to requirements in practical applications.

[0056] Specifically, the vegetation density index calculation submodule 122 counts the "number of low-resistivity points" and the "total number of measurement points" for each initial area: where low-resistivity points are defined as resistivity measurement points in the last 10% of the profile (low resistance is directly related to the water content of vegetation roots, and the more low-resistivity points there are, the higher the vegetation density); then, the vegetation density index V for each initial area is calculated using the formula "vegetation density index V = number of low-resistivity points / total number of measurement points", which quantifies the vegetation density in the area.

[0057] (2.3) Vegetation complexity calculation submodule 123.

[0058] Optionally, the vegetation complexity calculation submodule 123 is used to calculate the vegetation complexity based on the vegetation density index, the area of ​​each initial region, and the area of ​​the two-dimensional resistivity profile.

[0059] Specifically, the vegetation complexity calculation submodule 123 calculates the vegetation complexity of each initial region based on the results of the first two submodules: First, the vegetation complexity calculation submodule 123 calculates the resistivity spatial variation coefficient according to the resistivity standard deviation and resistivity mean of each initial region; second, it adjusts the reference area coefficient according to the vegetation density index and calculates the reference area by combining it with the total area of ​​the two-dimensional resistivity profile, so as to avoid the absolute dominance of the region area on the vegetation complexity calculation; finally, it outputs the vegetation complexity of each initial region.

[0060] The vegetation complexity and vegetation density index output by the vegetation complexity analysis module 12 directly provide the segmentation basis for the adaptive partitioning of the region division module 13, and at the same time provide weight adjustment parameters for the inversion optimization and vegetation stripping of the vegetation stripping operation module 14.

[0061] (3) Sub-region division module 13.

[0062] The sub-region segmentation module 13 is responsible for adaptively and iteratively segmenting the target region based on the output of the vegetation complexity analysis module 12, ensuring that regions with different vegetation disturbance intensities can be treated in a targeted manner.

[0063] Optionally, the sub-region division module 13 is used to adaptively divide the target region into multiple sub-regions based on vegetation complexity and two-dimensional resistivity profile data.

[0064] Specifically, the sub-region division module 13 takes the vegetation complexity of all initial regions output by the vegetation complexity analysis module 12 as input and calculates two key indicators: one is the mean of vegetation complexity of all initial regions, which reflects the overall vegetation disturbance intensity of the target region; the other is the standard deviation of vegetation complexity of all initial regions, which reflects the spatial variability of vegetation disturbance within the target region, i.e. the degree of difference in disturbance intensity between different regions.

[0065] Furthermore, the sub-region division module 13 calculates a dynamic threshold for determining whether a region needs further subdivision: first, it sets the initial values ​​of the dynamic weight coefficients (for example, the initial empirical values ​​of the two weight coefficients are 1.2 and 0.8, respectively), then dynamically adjusts the two weight coefficients based on the normalized signal-to-noise ratio of the current region and the number of subdivisions of the current region; finally, it obtains the dynamic threshold by multiplying the mean vegetation complexity by the adjusted first weight coefficient, and adding the standard deviation of vegetation complexity by the adjusted second weight coefficient.

[0066] Finally, the sub-region segmentation module 13 uses the K-means clustering algorithm for adaptive iterative segmentation: if the vegetation complexity of an initial region is greater than the dynamic threshold (the interference intensity exceeds the appropriate processing range and needs to be subdivided), then clustering is performed according to the resistivity value within the region to obtain smaller sub-regions; if the vegetation complexity of an initial region is less than or equal to the dynamic threshold (the interference intensity is moderate and no subdivision is needed), then the region is retained as the final sub-region. After iteration, multiple sub-regions are output, with relatively uniform vegetation interference intensity within each sub-region.

[0067] Understandably, the multiple sub-regions output by the region division module 13 are directly used as the subdivision processing units for the vegetation stripping operation module 14 to perform local two-dimensional inversion optimization, ensuring that the subsequent interference elimination operation can accurately match the vegetation interference characteristics of different regions.

[0068] (4) Vegetation stripping calculation module 14.

[0069] The vegetation stripping calculation module 14 is responsible for adjusting the weight of interference data and eliminating shallow false anomalies for each sub-region output by the region division module 13, and finally outputting geological parameters that reflect the true strata.

[0070] For example, such as Figure 3 As shown, the vegetation stripping calculation module 14 includes two sub-modules: a local two-dimensional inversion optimization sub-module 141 and an adaptive vegetation stripping sub-module 142.

[0071] (4.1) Local two-dimensional inversion optimization submodule 141.

[0072] The local two-dimensional inversion optimization submodule 141 optimizes geological parameters for each sub-region by combining the vegetation complexity of that sub-region output by the vegetation complexity analysis module 12, so as to reduce the influence of interference data.

[0073] Optionally, the local two-dimensional inversion optimization submodule 141 first determines the data weight matrix based on the vegetation complexity of the sub-region; then, it constructs an inversion objective function based on the data weight matrix and the two-dimensional resistivity profile data; finally, it obtains the optimized geological parameters by solving the inversion objective function. The data weight matrix controls the inversion weights of the data fitting terms, and the inversion objective function includes data fitting terms and model constraint terms.

[0074] (4.2) Adaptive vegetation stripping submodule 142.

[0075] The adaptive vegetation stripping submodule 142 further eliminates shallow false anomalies caused by vegetation aquifers based on the optimized geological parameters, such as avoiding misjudging the low resistivity characteristics of vegetation aquifers as underground cavities.

[0076] Optionally, the adaptive vegetation stripping submodule 142 first obtains the vegetation layer depth and vegetation layer resistivity of the sub-region; then, based on the vegetation layer depth and vegetation layer resistivity of the sub-region, it constructs a vegetation stripping function; finally, it uses the vegetation stripping function to process the optimized geological parameters and outputs the geological parameters after vegetation stripping.

[0077] Therefore, the geological parameters output by the vegetation stripping calculation module 14 after vegetation stripping directly provide accurate modeling data for the three-dimensional model construction module 15, which is the key to ensuring the accuracy of the terrain and geological model.

[0078] (5) 3D model construction module 15.

[0079] The 3D model building module 15 is responsible for converting the two-dimensional real geological parameters output by the vegetation stripping calculation module 14 into a visualized 3D topographic and geological model containing stratigraphic attributes.

[0080] Optionally, the 3D model building module 15 constructs a 3D topographic and geological model of the target area based on the geological parameters after vegetation stripping.

[0081] Specifically, the 3D model building module 15 first converts the geological parameters after vegetation stripping into 3D spatial point cloud data; then, it constructs a 3D topographic and geological model based on the 3D spatial point cloud data.

[0082] The 3D model building module 15 outputs a 3D topographic and geological model that can meet the application needs of resource exploration, engineering construction, geological disaster assessment, environmental monitoring and other fields, and realize the digital and visual expression of surface morphology and underground geological structure.

[0083] The above describes the terrain and geological model establishment system 10 and its included modules.

[0084] For example, such as Figure 4 The diagram shown is a flowchart illustrating a method for establishing a terrain and geological model based on geophysical exploration technology according to an embodiment of the present invention, including the following steps:

[0085] S401. Obtain the surface resistivity data of the target area and grid the surface resistivity data into two-dimensional resistivity profile data.

[0086] Understandably, the target area refers to various regions where accurate topographic and geological models need to be constructed using geophysical exploration techniques, and where vegetation cover can easily interfere with the acquisition of surface and subsurface geological signals. For example, in the low mountain and hilly forest areas of southern China, the dense tree roots have high water content, which can easily lead to distortion of shallow resistivity data; in the grassland-shrub mixed areas around mining areas, it is necessary to assess the impact of mining on underground rock strata, but herbaceous vegetation can interfere with the acquisition of surface signals. These are all typical target areas that this solution can address specifically.

[0087] In one possible implementation, this step can be performed by the data acquisition and processing module 11 in the topographic and geological model establishment system 10 described above. Specifically, the data acquisition and processing module 11, in order to obtain surface response data and physical signals of vegetation cover areas, uses ground geophysical equipment to collect the original surface resistivity of the target area, forming surface resistivity data.

[0088] When collecting data, the data acquisition and processing module 11 combines the vegetation distribution and topography of the target area, and lays out survey lines to cover areas with different vegetation types. This ensures that the data includes the planar location information of each measuring point and the corresponding original resistivity measurement value. At the same time, it avoids environmental factors that affect the accuracy of the data, such as strong electromagnetic interference and precipitation, and provides basic data for subsequent processing.

[0089] Optionally, after acquiring surface resistivity data, the data acquisition and processing module 11 performs data preprocessing on the surface resistivity data. For example, the preprocessing consists of two steps: data correction and noise filtering. For instance, data correction takes the original resistivity measurement value as input, sets a typical value of 1.0-1.05 for the device calibration coefficient and baseline offset, and outputs corrected data that eliminates the device's own bias. Noise filtering takes the corrected data as input, uses a moving average filter, sets an empirical sliding window size of 5×5 and an empirical value of 3 for the measurement point location, and outputs denoised surface resistivity data that eliminates external interference, providing high-quality data for subsequent steps.

[0090] Furthermore, the data acquisition and processing module 11 uses the denoised surface resistivity data as input to perform spatial gridding, sets the horizontal distance between the grid and the measuring points with reference to the measuring point spacing, constructs a planar grid, assigns values ​​to the denoised surface resistivity data according to location, and then extracts the "horizontal position-vertical depth-resistivity value" information along the measuring line direction to output two-dimensional resistivity profile data, realizing the conversion from one-dimensional resistivity to two-dimensional profile, and providing a core data carrier for subsequent analysis.

[0091] S402. Determine the vegetation complexity of the target area based on the two-dimensional resistivity profile data.

[0092] Among them, vegetation complexity is used to characterize the degree of interference of vegetation on the surface resistivity signal in the target area.

[0093] In one possible implementation, this step can be performed by the vegetation complexity analysis module 12 in the topographic and geological model establishment system 10 described above. Specifically, it may include: the vegetation complexity analysis module 12 first divides the target area into multiple initial areas based on the two-dimensional resistivity profile data; second, for each initial area, the vegetation complexity analysis module 12 counts the number of low resistivity points and determines the vegetation density index based on the number of low resistivity points and the total number of measurement points; finally, the vegetation complexity analysis module 12 calculates the vegetation complexity based on the vegetation density index, the area of ​​each initial area, and the area of ​​the two-dimensional resistivity profile. Low resistivity points are measurement points whose resistivity is lower than a preset resistivity threshold among all measurement points. It should be noted that the specific process of the vegetation complexity analysis module 12 determining the vegetation complexity of the target area through the above three sub-steps is described in S501-S503 below, and will not be repeated here.

[0094] Therefore, based on this step, the vegetation complexity analysis module 12 can provide an operable quantitative path for determining vegetation complexity: by dividing the initial region to avoid interference calculation deviations caused by the region being too large, by establishing the correlation between resistivity data and vegetation distribution through the vegetation density index, and finally by combining the vegetation complexity calculated by multiple parameters, it can accurately characterize the interference intensity of vegetation on resistivity signals in different regions, providing a key judgment basis for adaptive region division and differentiated vegetation stripping in subsequent steps, and avoiding the destruction of the real terrain of low vegetation complexity areas by using homogenization processing for different vegetation coverage areas (such as forests and grasslands).

[0095] S403. Based on vegetation complexity and two-dimensional resistivity profile data, the target area is adaptively divided into multiple sub-regions.

[0096] In one possible implementation, this step can be performed by the sub-region division module 13 in the topographic and geological model establishment system 10 described above. Based on the initial region determined in S402, the sub-region division module 13 can perform S403 through the following steps: First, the sub-region division module 13 determines the mean and standard deviation of the vegetation complexity of the initial region; second, the sub-region division module 13 determines a dynamic threshold based on the mean and standard deviation of the vegetation complexity of the initial region and the dynamic weight coefficient; finally, for each initial region, the sub-region division module 13 iteratively divides the initial region according to the vegetation complexity and the dynamic threshold, combined with a preset clustering algorithm, to obtain multiple sub-regions. The dynamic weight coefficient is dynamically adjusted according to the data signal-to-noise ratio and the number of initial regions. The preset clustering algorithm includes the K-means clustering algorithm. It should be noted that the specific process of the sub-region division module 13 dividing multiple sub-regions through the above three sub-steps is described in S601-S603 below, and will not be repeated here.

[0097] Therefore, the sub-region division module 13 can provide a feasible quantitative operation path for adaptive region division: by matching the actual interference characteristics of the region with dynamic thresholds, and combining clustering algorithms to achieve fine segmentation, it avoids the use of homogeneous division for different vegetation coverage areas such as forests and grasslands, and ensures that the vegetation interference intensity in each sub-region is relatively uniform. This provides a suitable processing unit for subsequent precise local two-dimensional inversion optimization and adaptive vegetation stripping for each sub-region, and ensures the targeting of subsequent interference elimination from the partitioning level, avoiding the destruction of the real terrain in low vegetation complexity areas.

[0098] S404. For each sub-region, perform local two-dimensional inversion optimization and adaptive vegetation stripping to obtain the geological parameters of each sub-region after vegetation stripping.

[0099] Among them, the geological parameters after vegetation stripping are used to characterize the true formation resistivity distribution after eliminating the influence of the vegetation aquifer.

[0100] In one possible implementation, this step can be performed by the vegetation stripping calculation module 14 in the terrain and geological model establishment system 10 described above, which can be divided into two parts: local two-dimensional inversion optimization and adaptive vegetation stripping.

[0101] For example, the vegetation stripping operation module 14 performs local two-dimensional inversion optimization, specifically including: first, determining the data weight matrix based on the vegetation complexity of the sub-region; second, constructing an inversion objective function based on the data weight matrix and two-dimensional resistivity profile data; and finally, obtaining the optimized geological parameters by solving the inversion objective function. The data weight matrix is ​​used to control the inversion weights of the data fitting terms, and the inversion objective function includes data fitting terms and model constraint terms. It should be noted that the specific process of the vegetation stripping operation module 14 implementing local two-dimensional inversion optimization through the aforementioned three sub-steps can be found in S701-S703 below, and will not be repeated here.

[0102] For example, the vegetation stripping calculation module 14 performs adaptive vegetation stripping, specifically including: first, obtaining the vegetation layer depth and vegetation layer resistivity of the sub-region; second, constructing a vegetation stripping function based on the vegetation layer depth and vegetation layer resistivity of the sub-region; and finally, processing the optimized geological parameters using the vegetation stripping function and outputting the geological parameters after vegetation stripping. It should be noted that the specific process of the vegetation stripping calculation module 14 implementing adaptive vegetation stripping through the aforementioned three sub-steps can be found in S704-S706 below, and will not be repeated here.

[0103] Therefore, the vegetation stripping calculation module 14 can, based on the local two-dimensional inversion optimization process, weaken the impact of high vegetation interference data on the inversion results by dynamically adjusting data weights, while ensuring the continuity of geological parameters with the help of model constraint terms, providing optimized basic geological data for subsequent vegetation stripping; at the same time, it can also, based on the adaptive vegetation stripping process, specifically eliminate shallow false anomalies caused by vegetation aquifers (such as avoiding misjudging low resistivity vegetation aquifers as underground cavities). The two work together to completely eliminate vegetation interference, ensuring that the output geological parameters can truly reflect the formation resistivity distribution, and providing accurate data support for subsequent three-dimensional modeling.

[0104] S405. Based on the geological parameters after vegetation stripping, construct a three-dimensional topographic and geological model of the target area.

[0105] In one possible implementation, this step can be performed by the three-dimensional model building module 15 in the terrain and geological model building system 10 described above, specifically including the following steps:

[0106] (1) Convert the geological parameters after vegetation stripping into three-dimensional spatial point cloud data.

[0107] Optionally, the 3D model construction module 15 assigns 3D spatial coordinates and physical property values ​​to each data point based on the horizontal position information, vertical depth information, and resistivity value contained in the geological parameters after vegetation stripping. Specifically, the horizontal position information corresponds to the x-coordinate (x) and y-coordinate (y) in 3D spatial coordinates, the vertical depth information corresponds to the z-coordinate (z) in 3D spatial coordinates, and the resistivity value is the physical property value, so that each data point has the dual characteristics of "spatial position + stratigraphic properties".

[0108] Furthermore, the 3D model construction module 15, based on preset distance parameters and preset interpolation algorithms, performs spatial interpolation processing on data points with 3D spatial coordinates and physical property values ​​to generate 3D spatial point cloud data. Specifically, the preset distance parameters reference the spacing between measurement points in the target area, maintaining consistency with the measurement point spacing in the previous data acquisition stage to ensure data density matching. The preset interpolation algorithm is used to fill in the gaps between data points, avoiding point cloud data loss caused by uneven distribution of the original measurement points, ultimately generating continuous 3D spatial point cloud data covering the entire target area.

[0109] (2) Construct a three-dimensional topographic and geological model based on three-dimensional point cloud data.

[0110] Optionally, the 3D model construction module 15 processes the 3D spatial point cloud data according to a preset surface reconstruction algorithm. The preset surface reconstruction algorithm uses a distance-weighted function for data fitting. Specifically, the preset surface reconstruction algorithm is the moving least squares method, and the distance-weighted function is a Gaussian weight kernel. This weight kernel strengthens the influence of neighboring data points on the fitted surface and weakens the interference of distant data points, ensuring that the fitted surface and the underground geological interface are smooth and continuous, conforming to the actual terrain and geological characteristics.

[0111] Furthermore, the 3D model construction module 15 maps the stratigraphic properties of the fitted surface using physical property values, constructing a topographic and geological 3D model containing stratigraphic property information. Specifically, stratigraphic types are classified based on differences in physical property values ​​(resistivity values), and different stratigraphic types are associated with corresponding areas of the fitted surface. This allows the 3D model to not only visually present the surface morphology but also clearly reflect the distribution and properties of underground strata, achieving the goal of constructing a 3D network model containing stratigraphic properties.

[0112] Therefore, the 3D model construction module 15 transforms the "geological parameters after vegetation stripping" obtained by eliminating vegetation interference in the previous text from a two-dimensional data form into a three-dimensional visualization model with spatial location and stratigraphic attributes. This solves the problems in existing topographic and geological models that "only reflect the surface morphology and lack underground geological attributes" or "are distorted due to vegetation interference". The final output topographic and geological 3D model can be directly applied to fields such as resource exploration, engineering construction, geological disaster assessment, and environmental monitoring, providing accurate geological basis for decision-making in relevant scenarios.

[0113] Based on the above technical solution, this embodiment of the invention acquires surface resistivity data of the target area and meshes it into two-dimensional resistivity profile data, providing basic data support for subsequent vegetation interference analysis. Then, based on the two-dimensional resistivity profile data, vegetation complexity, which characterizes the degree of interference of vegetation on the surface resistivity signal, is determined. Combining this vegetation complexity with the two-dimensional resistivity profile data, the target area is adaptively divided into multiple sub-regions, avoiding homogenization of regions with different vegetation interference intensities. Subsequently, local two-dimensional inversion optimization and adaptive vegetation stripping are performed on each sub-region to effectively eliminate the interference of vegetation (including vegetation aquifers) on geological parameters, obtaining geological parameters after vegetation stripping that reflect the true distribution of stratigraphic resistivity. Finally, a three-dimensional topographic and geological model of the target area is constructed based on these geological parameters, successfully solving the problem of inaccurate topographic and geological models caused by vegetation interference in existing technologies. The constructed model can accurately depict surface morphology and underground geological structure, reliably supporting practical application needs in fields such as resource exploration, engineering construction, geological hazard assessment, and environmental monitoring.

[0114] For example, in combination Figure 4 ,like Figure 5 The diagram shown is a flowchart illustrating a method for establishing a topographic and geological model based on geophysical exploration technology, according to an embodiment of the present invention. In this method, the vegetation complexity of the target area is determined based on two-dimensional resistivity profile data, specifically including the following steps:

[0115] S501. Based on the two-dimensional resistivity profile data, the target area is divided into multiple initial regions.

[0116] In one possible implementation, this step can be performed by the initial region coarse molecule module 121 in the vegetation complexity analysis module 12 described above.

[0117] Specifically, the initial region coarse molecular module 121 uses the two-dimensional resistivity profile data obtained in S401 as input to perform initial region division on the two-dimensional resistivity profile corresponding to the target region. For example, considering the actual situation of the two-dimensional resistivity profile length of 200m in this embodiment, it is divided into four initial regions on average, each with a length of 50m and an area denoted as . Where k=1, 2, 3, 4, corresponding to 4 initial regions respectively. The purpose of dividing the initial regions is to avoid distortion of the correlation between vegetation distribution and resistivity distribution due to excessively large regional spatial scale, and to ensure that the vegetation cover characteristics and resistivity characteristics in each initial region have local consistency, laying the foundation for accurate calculation of vegetation density index and vegetation complexity in the future.

[0118] S502. For each initial area, count the number of low-resistivity points and determine the vegetation density index based on the number of low-resistivity points and the total number of measurement points.

[0119] The low-resistivity point is the measurement point whose resistivity is lower than the preset resistivity threshold among all measurement points. The preset resistivity threshold is generally set to 10% of the maximum resistivity of all measurement points, or it can be determined according to the requirements of the actual application.

[0120] In one possible implementation, this step can be performed by the vegetation density index calculation submodule 122 in the vegetation complexity analysis module 12 described above.

[0121] It is understandable that since low resistivity is related to the water content of vegetation roots, resistivity can reflect the distribution of vegetation (forest areas have high root water content, low resistivity, and high vegetation density). Therefore, the vegetation density index V of each area can be obtained by dividing the number of low resistivity points by the total number of measurement points.

[0122] Specifically, the vegetation density index calculation submodule 122 counts the "number of low-resistivity points" and the "total number of measurement points" for each initial area: where low-resistivity points are defined as resistivity measurement points in the last 10% of the profile (low resistance is directly related to the water content of vegetation roots, and the more low-resistivity points there are, the higher the vegetation density); then, the vegetation density index V for each initial area is calculated using the formula "vegetation density index V = number of low-resistivity points / total number of measurement points", which quantifies the vegetation density in the area.

[0123] S503. Calculate the vegetation complexity based on the vegetation density index, the area of ​​each initial region, and the area of ​​the two-dimensional resistivity profile.

[0124] In one possible implementation, this step can be performed by the vegetation complexity calculation submodule 123 in the vegetation complexity analysis module 12 described above, specifically including the following steps:

[0125] (1) Determine the spatial variation coefficient of resistivity for each initial region based on the standard deviation and mean resistivity of the resistivity of multiple initial regions.

[0126] For example, the vegetation complexity calculation submodule 123 first obtains the average resistivity of all measurement points in each initial region (denoted as ). (reflecting the average resistivity of the region) and the standard deviation of resistivity (denoted as ) (This reflects the degree of resistivity dispersion in the region). Then, by the ratio of the standard deviation of resistivity to the mean resistivity, the spatial variation coefficient of resistivity is obtained. This coefficient is a dimensionless parameter. The larger the value, the more uneven the vegetation distribution in the region and the more dispersed the interference to the resistivity signal.

[0127] (2) Determine the reference area coefficient based on the vegetation density index, and determine the reference area based on the reference area coefficient and the area of ​​the two-dimensional resistivity profile.

[0128] For example, the vegetation complexity calculation submodule 123 dynamically determines the reference area coefficient of each initial region based on the vegetation density index V. The specific determination process is as follows: First, a benchmark reference area coefficient is preset. Specifically, the preset benchmark reference area coefficient The value of needs to ensure the initial reference area. The area is on the same order of magnitude as the typical area of ​​a single initial region to reasonably balance the impact of the area factor in subsequent calculations. As an example of a feasible approach, it can be... Set it to the reciprocal of the initial number of regions N, or empirically set it to a constant in the range of 0.001 to 0.1, for example, take... =0.01.

[0129] Subsequently, if an initial region has a value V > 0.3, then the region is considered to have dense vegetation, and a smaller reference area coefficient is assigned to it. If the density V of an initial region and its two adjacent regions (a total of three consecutive regions) is greater than a high density threshold (e.g., V > 0.5), then the region is determined to be a contiguous high-vegetation-density area, and a larger reference area coefficient is assigned to it. For initial regions that do not meet the aforementioned two conditions, the reference area coefficient shall be the baseline value, i.e. .

[0130] Furthermore, the vegetation complexity calculation submodule 123 combines the total area of ​​the two-dimensional resistivity profile (denoted as A, in this embodiment, the total area corresponding to a 200m long profile is A = 200m * 50m = 10000m²), and calculates it using the formula... Calculate reference area (Unit: m²), ensuring fairness in vegetation complexity calculation for initial areas of different sizes.

[0131] (3) Calculate vegetation complexity based on resistivity spatial variation coefficient, area of ​​each initial region, and reference area.

[0132] For example, the vegetation complexity calculation submodule 123 calculates the vegetation complexity using the following formula:

[0133]

[0134] in, Indicates the area vegetation complexity; Indicates the area The standard deviation of resistivity; Indicates the area The average resistivity; Indicates the area The area; Indicates the reference area; This represents the area of ​​the entire two-dimensional resistivity profile. The coefficient representing the reference area is taken as an empirical value of 0.01. The specific method for determining this has been explained in the previous sub-step and will not be repeated here.

[0135] Based on the above technical solution, this embodiment of the invention divides the initial region by the average length of the two-dimensional resistivity profile, avoiding the problem of distortion in the correlation between vegetation distribution and resistivity distribution caused by excessively large regional spatial scales. This ensures that the vegetation cover characteristics and resistivity characteristics of each initial region have local consistency, laying a precise spatial foundation for subsequent calculations. By statistically analyzing low resistivity points and calculating the vegetation density index, the correlation between low resistivity and vegetation root water content is utilized to transform vegetation density into a quantifiable index parameter, realizing a direct correlation between vegetation distribution and resistivity data. Furthermore, by combining the resistivity spatial variation coefficient and reference area to calculate vegetation complexity, the interference intensity of vegetation on the surface resistivity signal in different initial regions is accurately quantified. Ultimately, this provides a core basis for interference quantification for subsequent adaptive region division, effectively avoiding the problem of subsequent homogenization processing caused by the lack of accurate quantification of vegetation interference, and ensuring the accuracy of the topographic and geological model construction from the source.

[0136] For example, in combination Figure 4 ,like Figure 6 The diagram shown is a flowchart illustrating a method for establishing a topographic and geological model based on geophysical technology, according to an embodiment of the present invention. In this method, the target area is adaptively divided based on vegetation complexity and two-dimensional resistivity profile data, specifically including the following steps:

[0137] S601. Determine the mean and standard deviation of vegetation complexity in the initial region.

[0138] In this step, the sub-region division module 13 takes the vegetation complexity of all initial regions output by the vegetation complexity analysis module 12 as input and calculates two key indicators: one is the mean of vegetation complexity of all initial regions, which reflects the overall vegetation disturbance intensity of the target region; the other is the standard deviation of vegetation complexity of all initial regions, which reflects the spatial variability of vegetation disturbance within the target region, i.e. the degree of difference in disturbance intensity between different regions.

[0139] S602. Determine the dynamic threshold based on the mean and standard deviation of vegetation complexity in the initial region and the dynamic weighting coefficient.

[0140] The dynamic weighting coefficients are dynamically adjusted based on the data signal-to-noise ratio and the initial number of regions. The dynamic weighting coefficients include α and β. For example, the sub-region partitioning module 13 first sets the initial values ​​of the dynamic weighting coefficients α and β, and then calculates the data signal-to-noise ratio S using the following formula:

[0141]

[0142]

[0143]

[0144] in, This represents the normalized signal-to-noise ratio of the current region; and This represents the initial value of the weighting coefficients. and This represents the dynamically updated value of the weight coefficients; n represents the number of current region divisions, which is a positive integer greater than 2. and These are preset initial values, with sizes taken from empirical values ​​of 1.2 and 0.8 respectively; This represents the average resistivity within the current region. This represents the variance of resistivity within the current region; Represents the minimum value, used to prevent variance from being affected in special cases. A value of zero renders the calculation meaningless.

[0145] Furthermore, the sub-region division module 13 calculates the dynamic threshold according to the following formula:

[0146]

[0147] in, This represents the dynamic threshold for determining whether to proceed with further segmentation; This represents the mean of vegetation complexity across all regions. The standard deviation representing the vegetation complexity of all regions; and This represents the weighting coefficient.

[0148] S603. For each initial region, based on the vegetation complexity and dynamic threshold of the initial region, the initial region is iteratively segmented using a preset clustering algorithm to obtain multiple sub-regions.

[0149] Among them, the preset clustering algorithm types include K-means clustering algorithm.

[0150] For example, since low-resistivity areas in grasslands or forests are relatively concentrated, if the current area... Greater than When the K-means clustering algorithm is used to perform clustering and segmentation according to resistivity value, the specific steps are as follows: (1) Segmentation determination: compare the vegetation complexity and dynamic threshold of each initial region one by one; (2) K-means clustering segmentation: for the initial region to be segmented, the K-means clustering algorithm is used to cluster according to the resistivity value in the region. This is because resistivity is directly related to vegetation distribution, and clustering according to resistivity can ensure that the vegetation interference characteristics in the sub-regions after segmentation are consistent.

[0151] Therefore, after the above iterative segmentation, the two-dimensional resistivity profile data is transformed into a set of sub-regions divided according to vegetation complexity. The vegetation disturbance intensity in each sub-region is relatively uniform, which meets the requirement of avoiding homogenization of forest and grassland areas and provides a suitable processing unit for subsequent local two-dimensional inversion optimization and adaptive vegetation stripping.

[0152] Based on the above technical solution, this embodiment of the invention accurately captures the overall vegetation disturbance intensity and spatial variability of the target area by calculating the mean and standard deviation of the vegetation complexity of the initial area. This provides realistic basic data for subsequent segmentation judgment and avoids segmentation deviation caused by relying solely on a single disturbance index. By dynamically adjusting the weight coefficients based on the data signal-to-noise ratio and the number of initial area divisions to determine the dynamic threshold, the segmentation standard can be flexibly adapted to the regional data quality and division scale. This avoids data redundancy caused by over-segmentation of high signal-to-noise ratio areas and interference mixing caused by insufficient segmentation of low signal-to-noise ratio areas, solving the problem that a fixed threshold cannot match the disturbance characteristics of different areas. Furthermore, the initial area is iteratively segmented using a clustering algorithm, and the high vegetation disturbance area is finely divided according to the resistivity value. This ensures that the vegetation disturbance intensity in each sub-region is relatively uniform, effectively avoiding the problem of destroying the real terrain of low vegetation complexity areas by using homogeneous division of different vegetation coverage areas such as forests and grasslands.

[0153] For example, in combination Figure 4 ,like Figure 7The diagram shown is a flowchart illustrating a method for establishing a topographic and geological model based on geophysical technology, according to an embodiment of the present invention. In this method, for each sub-region, local two-dimensional inversion optimization and adaptive vegetation stripping are performed to obtain the geological parameters of each sub-region after vegetation stripping. Specifically, the method includes the following steps:

[0154] S701. Determine the data weight matrix based on the vegetation complexity of the sub-region.

[0155] The data weight matrix is ​​used to control the inversion weights of the data fitting terms.

[0156] In one possible implementation, S701-S703 can be executed by the local two-dimensional inversion optimization submodule 141 in the vegetation stripping operation module 14 described above.

[0157] Understandably, since the low resistivity information formed by the vegetation layer can obscure the true surface morphology, it is necessary to quantify vegetation parameters to achieve accurate vegetation stripping. Therefore, this invention performs regularized least squares inversion in each local region, utilizing the vegetation complexity of the sub-region. The data weight matrix guides the inversion optimization. However, due to the low reliability of data in areas with high vegetation complexity, its inversion weight needs to be reduced. For example, the local two-dimensional inversion optimization submodule 141 can determine the data weight matrix using the following formula:

[0158]

[0159] in, Indicates the first The weight of each data point; The inhibitory factor has a range of [1,3], and the embodiment in this application is 2.0; This represents a small perturbation term to avoid a denominator of 0. When in areas with low vegetation complexity... , , for The mean, The first mean weight is used, specifically within the range of [0.5, 0.7]. In this embodiment of the invention, an empirical value of 0.6 is generally used. Small, Improvement, for local two-dimensional resistivity profile data High level of trust; when in areas with high vegetation complexity , , The second mean weight is taken as [1.3, 1.5]. In this embodiment of the invention, an empirical value of 1.4 is generally used. big, Reducing this will weaken the impact of interfering data.

[0160] S702. Construct the inversion objective function based on the data weight matrix and the two-dimensional resistivity profile data.

[0161] The inversion objective function includes data fitting terms and model constraint terms. For example, the local two-dimensional inversion optimization submodule 141 can construct the inversion objective function using the following formula:

[0162]

[0163] in, This represents the local two-dimensional inversion optimization result output by the inversion objective function; This represents the model parameter vector, which includes resistivity and depth. This represents local two-dimensional resistivity profile data; This represents the forward modeling operator, obtained using the finite element method (FEM). This represents the regularization factor, with a value range of [0.1, 1.0]. In this embodiment of the invention, an empirical value of 0.5 is used. The model weight matrix is ​​obtained using the second-order difference method; This represents the data weight matrix.

[0164] S703. By solving the inversion objective function, the optimized geological parameters are obtained.

[0165] In this step, based on the aforementioned S701-S702, the local two-dimensional inversion optimization submodule 141 performs local regularized least squares inversion, and then, in combination with vegetation complexity, performs adaptive vegetation interference stripping in the local area, outputting the two-dimensional geological parameters after preliminary vegetation interference stripping.

[0166] S704. Obtain the vegetation layer depth and vegetation layer resistivity of the sub-region.

[0167] In one possible implementation, S701-S703 can be executed by the adaptive vegetation stripping submodule 142 in the vegetation stripping operation module 14 described above.

[0168] For example, the adaptive vegetation stripping submodule 142 uses on-site drilling sampling or vegetation remote sensing interpretation to determine the maximum distribution depth of vegetation roots within the current sub-region, in meters; based on the surface resistivity data collected by S401, it calculates the average resistivity within the distribution range of vegetation roots in the sub-region, in meters. .

[0169] S705. Based on the vegetation layer depth and vegetation layer resistivity of the sub-region, construct a vegetation stripping function.

[0170] Understandably, the vegetation aquifer influences topographic information; for example, its low resistivity might be mistaken for underground cavities. Therefore, the adaptive vegetation stripping submodule 142 constructs a vegetation stripping function to adaptively eliminate shallow false anomalies caused by the vegetation aquifer. For example, the adaptive vegetation stripping submodule 142 constructs the vegetation stripping function using the following formula:

[0171]

[0172] in, Indicates geological parameters after vegetation stripping; This represents the result of the two-dimensional inversion optimization; This represents the vertical depth coordinates of the two-dimensional resistivity profile after initial removal of vegetation disturbance. This represents the vertical depth coordinates of the two-dimensional resistivity profile after the final removal of vegetation disturbance. Indicates the depth of the vegetation layer; Indicates the resistivity of the vegetation layer; Indicates the peel strength factor. , is a dimensionless parameter; D represents the preset feature depth, for example, it can be 1m, used to make the depth difference dimensionless.

[0173] S706. Use the vegetation stripping function to process the optimized geological parameters and output the geological parameters after vegetation stripping.

[0174] Specifically, the adaptive vegetation stripping submodule 142 substitutes the optimized geological parameters, the vegetation layer depth and vegetation layer resistivity obtained by S704, and the stripping intensity factor calculated by vegetation complexity into the vegetation stripping function, and outputs the geological parameters after vegetation stripping.

[0175] Based on the above technical solution, this embodiment of the invention, for multiple sub-regions obtained by adaptive region division, first uses the two-dimensional resistivity profile data and vegetation complexity of the sub-regions as input to construct an inversion objective function containing data fitting terms and model constraint terms. The data weight matrix is ​​dynamically adjusted through vegetation complexity, and the objective function is solved to obtain optimized geological parameters, initially eliminating the impact of vegetation interference on data reliability. Then, the vegetation layer depth and vegetation layer resistivity of the sub-regions are obtained. Based on these parameters and vegetation complexity, a vegetation stripping function is constructed to process the optimized geological parameters, eliminating shallow false anomalies caused by vegetation aquifers, and finally outputting the geological parameters after vegetation stripping. Thus, precise elimination of vegetation interference in a layered manner is achieved, ensuring both the spatial continuity of geological parameters and their accurate reflection of the underground stratum resistivity distribution. This provides high-quality two-dimensional data support for subsequent three-dimensional topographic and geological modeling, effectively solving the problem of geological data distortion caused by vegetation interference in existing technologies, and achieving the core objective of constructing accurate topographic geological models using geophysical technology.

[0176] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0177] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for establishing a topographic and geological model based on geophysical exploration technology, characterized in that, The method includes: Acquire surface resistivity data of the target area and grid the surface resistivity data into two-dimensional resistivity profile data; Based on the two-dimensional resistivity profile data, the vegetation complexity of the target area is determined; wherein, the vegetation complexity is used to characterize the degree of interference of vegetation on the surface resistivity signal in the target area. Based on the vegetation complexity and the two-dimensional resistivity profile data, the target area is adaptively divided into multiple sub-regions. For each sub-region, local two-dimensional inversion optimization and adaptive vegetation stripping are performed to obtain the geological parameters of each sub-region after vegetation stripping; wherein, the geological parameters after vegetation stripping are used to characterize the true formation resistivity distribution after eliminating the influence of vegetation aquifer. Based on the geological parameters after vegetation stripping, a three-dimensional topographic and geological model of the target area is constructed. Based on the two-dimensional resistivity profile data, the vegetation complexity of the target area is determined, specifically including: Based on the two-dimensional resistivity profile data, the target region is divided into multiple initial regions; For each initial region, the number of low-resistivity points is counted, and the ratio of the number of low-resistivity points to the total number of measurement points is determined as the vegetation density index; wherein, the low-resistivity points are the measurement points whose resistivity is lower than a preset resistivity threshold among all measurement points. The vegetation complexity is calculated based on the vegetation density index, the area of ​​each initial region, and the area of ​​the two-dimensional resistivity profile. This includes: determining the resistivity spatial variation coefficient of each initial region based on the resistivity standard deviation and mean resistivity of the multiple initial regions; presetting a baseline reference area coefficient; if the vegetation density index of the initial region is greater than a first index threshold, then setting the reference area coefficient to half of the baseline reference area coefficient; if the vegetation density index of the initial region and the two adjacent regions of the initial region are both greater than a second index threshold, then setting the reference area coefficient to twice the baseline reference area coefficient; if the vegetation density index of the initial region is less than or equal to the first index threshold, then setting the baseline reference area coefficient as the reference area coefficient; wherein the first index threshold is less than the second index threshold; multiplying the reference area coefficient by the area of ​​the two-dimensional resistivity profile to determine the reference area; wherein the reference area is used to ensure fairness in the vegetation complexity calculation for initial regions of different areas; and multiplying the square root of the ratio of the area of ​​each initial region to the reference area by the resistivity spatial variation coefficient to obtain the vegetation complexity. Based on the vegetation complexity and the two-dimensional resistivity profile data, the target region is adaptively divided, specifically including: Determine the mean and standard deviation of vegetation complexity in the initial region; Based on dynamic weighting coefficients, the mean and standard deviation of vegetation complexity in the initial region are weighted and summed to obtain a dynamic threshold. The dynamic weighting coefficients include dynamic weighting coefficient α and dynamic weighting coefficient β. Dynamic weighting coefficient α is used to weight the mean of vegetation complexity, and dynamic weighting coefficient β is used to weight the standard deviation of vegetation complexity. Dynamic weighting coefficient α is determined based on a first preset initial value and the signal-to-noise ratio of the initial region, and dynamic weighting coefficient β is determined based on a second preset initial value and the total number of initial regions. For each initial region, the initial region is iteratively segmented according to the vegetation complexity of the initial region and the dynamic threshold, combined with a preset clustering algorithm, to obtain multiple sub-regions; if the vegetation complexity of the initial region is greater than the dynamic threshold, then the region is clustered and segmented according to the resistivity value of the region to obtain smaller sub-regions; if the vegetation complexity of the initial region is less than or equal to the dynamic threshold, then the region is retained as the final sub-region; wherein, the type of the preset clustering algorithm includes K-means clustering algorithm; For each of the sub-regions, a local two-dimensional inversion optimization is performed, specifically including: Based on the vegetation complexity of the sub-region, inversion weights are set for each measurement point in the sub-region, and the inversion weights are inversely proportional to the vegetation complexity. The inversion weights of all measurement points together constitute a data weight matrix. Based on the data weight matrix and the two-dimensional resistivity profile data, an inversion objective function is constructed. The formula for the inversion objective function is: in, This represents the local two-dimensional inversion optimization result output by the inversion objective function; This represents the model parameter vector, which includes resistivity and depth. This represents local two-dimensional resistivity profile data; Represents the forward operand operator; Represents the regularization factor; Represents the model weight matrix; Represents the data weight matrix; The optimized geological parameters are obtained by solving the inversion objective function; For each of the sub-regions, the adaptive vegetation stripping is performed, specifically including: Obtain the vegetation layer depth and vegetation layer resistivity of the sub-region; Based on the vegetation layer depth and vegetation layer resistivity of the sub-region, a vegetation stripping function is constructed. The formula for the vegetation stripping function is: in, Indicates geological parameters after vegetation stripping; This represents the result of the two-dimensional inversion optimization; This represents the vertical depth coordinates of the two-dimensional resistivity profile after initial removal of vegetation disturbance. This represents the vertical depth coordinates of the two-dimensional resistivity profile after the final removal of vegetation disturbance. Indicates the depth of the vegetation layer; Indicates the resistivity of the vegetation layer; Indicates the peel strength factor. ; D represents the vegetation complexity; D represents the preset feature depth. The optimized geological parameters are processed using the vegetation stripping function, and the geological parameters after vegetation stripping are output.

2. The method for establishing a topographic and geological model based on geophysical exploration technology according to claim 1, characterized in that, The construction of a three-dimensional topographic and geological model of the target area based on the geological parameters after vegetation stripping specifically includes: The geological parameters after the vegetation was stripped were converted into three-dimensional spatial point cloud data. A three-dimensional terrain and geological model is constructed based on the three-dimensional spatial point cloud data.

3. The method for establishing a topographic and geological model based on geophysical exploration technology according to claim 2, characterized in that, The process of converting the geological parameters after vegetation stripping into three-dimensional spatial point cloud data specifically includes: Based on the horizontal position information, vertical depth information and resistivity value contained in the geological parameters after vegetation stripping, each data point is assigned three-dimensional spatial coordinates and physical property values. Based on preset distance parameters and preset interpolation algorithms, spatial interpolation processing is performed on data points with the three-dimensional spatial coordinates and physical property values ​​to generate the three-dimensional spatial point cloud data.

4. The method for establishing a topographic and geological model based on geophysical exploration technology according to claim 3, characterized in that, The construction of a three-dimensional terrain and geological model based on the three-dimensional spatial point cloud data specifically includes: The three-dimensional spatial point cloud data is processed according to a preset surface reconstruction algorithm, which uses a distance-weighted function for data fitting. By mapping the stratigraphic properties of the fitted surface using the physical property values, a three-dimensional topographic and geological model containing stratigraphic property information is constructed.

5. The method for establishing a topographic and geological model based on geophysical exploration technology according to any one of claims 1-4, characterized in that, Before gridding the surface resistivity data into two-dimensional resistivity profile data, the method further includes: The surface resistivity data is preprocessed; wherein the preprocessing includes data correction and noise filtering.

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