A method and system for visualizing modeling of a mine area karst aquifer water outlet section

Abstract

This invention relates to the field of 3D modeling technology, specifically to a visualization modeling method and system for the water-bearing section of a karst aquifer in a mining area. It introduces dynamic monitoring features by determining the relative change in water level based on the relative change in resistivity between adjacent periods. Simultaneously, cluster analysis based on resistivity fluctuations and locations effectively divides spatial regions with similar evolutionary characteristics. Furthermore, the dynamic water-bearing reliability is comprehensively determined by combining the intensity of fluctuations at each monitoring point with the consistency coefficient of water level changes. This deeply filters geological background noise from the dual perspectives of electrical temporal activity and the synergy of hydrological evolution trends, achieving accurate identification and extraction of real fluid movement dynamic signals. Finally, visualization modeling based on the dynamic water-bearing reliability effectively eliminates interference from non-aquifer low-resistivity bodies, thereby improving the accuracy of locating and constructing the water-bearing section model of the karst aquifer in the mining area.

Description

Technical Field

[0001] This invention relates to the field of 3D modeling technology, specifically to a visualization modeling method and system for the water-bearing section of karst aquifers in mining areas. Background Technology

[0002] With the increasing depth of mineral resource extraction in my country, the threat of confined water in the foundation during deep mining is becoming increasingly serious. Due to the highly heterogeneous nature of karst caves and fissures within mining areas, accurate detection of hidden water-conducting channels and outlets is crucial for preventing mine water inrush accidents. Current technologies typically employ conventional geophysical exploration methods to detect water-bearing sections of aquifers. The core principle relies on the static conductivity differences of the underground medium; that is, by supplying electricity to the underground and measuring electrical parameters, areas exhibiting low resistivity anomalies are directly identified as water-filled caves or water-bearing fissures, thereby constructing a hydrogeological model.

[0003] However, due to the extreme complexity of underground geological structures, different geological bodies often exhibit similar physical responses. Existing technologies do not consider that non-flowing water areas, such as highly conductive metallic ore bodies or stable static aquifer fracture zones, can also exhibit significant low resistivity anomalies. This detection method, which relies solely on the inversion of single-period static physical parameters, cannot effectively remove geological background noise, accurately extract electrical anomaly signals caused solely by the dynamic movement of groundwater, or distinguish the boundary between real water bodies and rock masses. Consequently, existing technologies have low accuracy in locating and constructing models of karst aquifer water-bearing sections in mining areas using conventional geophysical exploration methods. Summary of the Invention

[0004] To address the low accuracy of existing technologies in locating and constructing models of karst aquifer outlet sections in mining areas using conventional geophysical exploration methods, this invention aims to provide a visual modeling method and system for karst aquifer outlet sections in mining areas. The specific technical solution adopted is as follows: The first aspect of this invention provides a method for visually modeling the aquifer section of a karst aquifer in a mining area, comprising: In the karst aquifer of the mining area to be tested, the resistivity of each monitoring point is obtained at each monitoring period; based on the relative change of resistivity of each monitoring point in adjacent monitoring periods, the relative change of water level in each monitoring period is determined. Cluster analysis was performed on the resistivity fluctuations and corresponding locations of each monitoring point to determine all monitoring point clusters. Within each monitoring point cluster, the degree of fluctuation was determined based on the temporal trend of resistivity changes at each monitoring point. The water level change consistency coefficient was determined based on the consistency of the relative water level changes at each monitoring point. Based on the degree of fluctuation and the consistency coefficient of water level change, the dynamic water content confidence of each monitoring point cluster is determined; based on the dynamic water content confidence, a visual model of the water-bearing section of the karst aquifer in the mining area is performed.

[0005] Furthermore, the process of obtaining the relative change in water level includes: Based on the relative deviation between the resistivity of each monitoring point in each monitoring period and the resistivity in the previous monitoring period, the corresponding resistivity period change value is determined; based on the resistivity period change value and the corresponding resistivity, the resistivity period change rate of each monitoring point in each monitoring period is determined; the resistivity period change value and the resistivity period change rate are positively correlated, and the resistivity and the resistivity period change rate are negatively correlated. By performing a negative correlation mapping on the resistivity period change rate, the relative water level change at each monitoring point in each monitoring period is determined.

[0006] Furthermore, the process of obtaining the clusters of monitoring points includes: Based on the overall magnitude of the resistivity period change rate at each monitoring point across all monitoring periods, determine the corresponding overall resistivity change rate. Obtain the three-dimensional spatial coordinates of each monitoring point; perform cluster analysis based on the three-dimensional spatial coordinates of each monitoring point and the corresponding overall resistivity change rate to determine all monitoring point clusters.

[0007] Furthermore, the process of obtaining the intensity of the fluctuation includes: Within each monitoring point cluster, the corresponding spatiotemporal activity factor is determined based on the mean of the resistivity period change rate of all monitoring points in each monitoring period. Based on the overall magnitude of the spatiotemporal activity factor of each monitoring point cluster across all monitoring periods, the corresponding potential change degree is determined. Based on the discrete characteristics of the spatiotemporal activity factor of each monitoring point cluster across all monitoring periods, the corresponding potential fluctuation degree is determined. Based on the degree of potential fluctuation and the degree of potential change, the degree of fluctuation intensity of each monitoring point cluster is determined; the degree of potential fluctuation and the degree of fluctuation intensity are positively correlated, and the degree of potential change and the degree of fluctuation intensity are negatively correlated.

[0008] Furthermore, the process of obtaining the water level change consistency coefficient includes: The relative water level changes of each monitoring point in all monitoring periods are arranged in chronological order to determine the corresponding relative water level change sequence; the overall water level change is determined based on the total absolute value of the relative water level changes of each monitoring point in all monitoring periods. Based on the sequence similarity characteristics of the relative water level change sequences among the monitoring points in each cluster, the corresponding trend consistency is determined. The degree of water level fluctuation is determined based on the relative dispersion of the overall water level change of all monitoring points in each monitoring point cluster. Based on the trend consistency and the degree of water level fluctuation, a water level change consistency coefficient is determined for each monitoring point cluster. The trend consistency is positively correlated with the water level change consistency coefficient, and the degree of water level fluctuation is negatively correlated with the water level change consistency coefficient.

[0009] Furthermore, the process of obtaining the degree of water level fluctuation includes: The degree of dispersion of water level changes is determined based on the standard deviation of the overall water level change of all monitoring points in each monitoring point cluster. The standard value of water level change is determined based on the overall magnitude of the absolute value of the overall water level change of all monitoring points in each monitoring point cluster. The dispersion of water level changes is standardized using the standard value of water level change to determine the degree of water level fluctuation for each monitoring point cluster.

[0010] Furthermore, the process of obtaining the dynamic moisture content reliability includes: The dynamic water content reliability of each monitoring point cluster is determined by weighted fusion of the fluctuation intensity and the water level change consistency coefficient.

[0011] Furthermore, the process of visually modeling the aquifer section of the karst aquifer in the mining area based on the dynamic water content confidence level includes: Monitoring points in the cluster corresponding to dynamic water content confidence levels greater than or equal to the preset confidence threshold are taken as confidence points of the water outlet section; and a visual model of the water outlet section of the karst aquifer in the mining area is performed based on all confidence points of the water outlet section.

[0012] Furthermore, the process of obtaining the trend consistency includes: Within each monitoring point cluster, each monitoring point is sequentially designated as the target point, and the other monitoring points outside the target points are designated as reference points. The DTW distance between the relative water level change sequence of the target point and the relative water level change sequence of each corresponding reference point is calculated based on the dynamic time warping algorithm, and negative correlation mapping is performed to determine the sequence similarity between the target point and each corresponding reference point. Based on the overall magnitude of the sequence similarity between the target point and all corresponding reference points, the reference similarity of the target point is determined. Based on the overall magnitude of the reference similarity of all monitoring points in each monitoring point cluster, the corresponding trend consistency is determined.

[0013] Secondly, the present invention provides a visualization modeling system for the aquifer section of a karst aquifer in a mining area, the system comprising: The data acquisition and preprocessing module is used to acquire the resistivity measured at each monitoring point in the karst aquifer of the mining area under test, and to determine the relative change in water level in each monitoring period based on the relative change in resistivity at each monitoring point in adjacent monitoring periods. The parameter determination module is used to perform cluster analysis based on the resistivity fluctuations and corresponding locations of each monitoring point to determine all monitoring point clusters; within each monitoring point cluster, the module determines the degree of fluctuation based on the temporal trend of resistivity changes at each monitoring point; and determines the water level change consistency coefficient based on the consistency of the relative water level changes at each monitoring point. The visualization modeling module is used to determine the dynamic water content reliability of each monitoring point cluster based on the intensity of the fluctuation and the consistency coefficient of water level change; and to perform visualization modeling of the water-bearing section of the karst aquifer in the mining area based on the dynamic water content reliability.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to perform the method as described in the first aspect or any embodiment of the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer program product comprising computer program code, which, when executed, performs the method as described in the first aspect or any embodiment of the first aspect of the present invention.

[0016] Fifthly, the present invention provides a computer-readable storage medium storing computer program code that, when executed, performs the method as described in the first aspect or any embodiment of the first aspect of the present invention.

[0017] This application has the following beneficial effects: This invention introduces dynamic monitoring features by acquiring resistivity data from monitoring points over multiple monitoring periods and determining the relative change in water level based on the relative change in resistivity between adjacent periods. This overcomes the shortcomings of existing technologies that rely solely on single-period static electrical parameters and are easily affected by static geological bodies such as low-resistivity ore bodies. Simultaneously, cluster analysis based on resistivity fluctuations and locations effectively delineates spatial regions with similar evolutionary characteristics. Furthermore, by combining the intensity of fluctuations at each monitoring point with the consistency coefficient of water level changes, the dynamic water content reliability is comprehensively determined. This deeply filters geological background noise from the dual perspectives of electrical temporal activity and the synergy of hydrological evolution trends, achieving accurate identification and extraction of real fluid movement dynamic signals. Finally, based on the dynamic water content reliability, visual modeling is performed, effectively eliminating interference from non-aquifer low-resistivity bodies, thereby improving the accuracy of locating and constructing models of the water-bearing sections of karst aquifers in mining areas. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a flowchart of a visualization modeling method for the water-bearing section of a karst aquifer in a mining area, provided in one embodiment of the present invention. Figure 2 This is a structural diagram of a visualization modeling system for the water-bearing section of a karst aquifer in a mining area, provided in one embodiment of the present invention. Figure 3 This is a schematic diagram of a computer device structure provided in one embodiment of the present invention. Detailed Implementation

[0020] 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 visualization modeling method and system for the water-bearing section of a karst aquifer in a mining area proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0021] 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.

[0022] The following description, in conjunction with the accompanying drawings, details the specific scheme of the visualization modeling method and system for the water-bearing section of a karst aquifer in a mining area provided by this invention.

[0023] This invention provides a method for visually modeling the aquifer section of a karst aquifer in a mining area. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a visualization modeling method for the water-bearing section of a karst aquifer in a mining area, provided by an embodiment of the present invention. The method includes: Step S101: In the karst aquifer of the mining area to be tested, obtain the resistivity measured at each monitoring point during each monitoring period; based on the relative change of resistivity at each monitoring point during adjacent monitoring periods, determine the relative change of water level during each monitoring period.

[0024] When collecting data on the water-bearing sections of karst aquifers in mining areas, a high-precision fixed monitoring network must first be established in the target detection area. Based on the geological background of the mining area and the complexity of karst development, a detection array consisting of multiple parallel survey lines is deployed on the surface. To establish a unified spatial benchmark, a three-dimensional rectangular coordinate system is established with the existing engineering survey control points in the mining area as the origin. The X-axis is parallel to the main survey line direction, the Y-axis is perpendicular to the main survey line direction, and the Z-axis defines depth downwards along the direction of gravity. Under this three-dimensional coordinate system, a total station is used to accurately calibrate and record the spatial coordinates of the starting point, ending point, and key intermediate points of each survey line, serving as the sole spatial benchmark for repeated data collection at different monitoring periods. Specifically, the survey line spacing in shallow karst development areas is set at 5 to 10 meters, and the electrode spacing on the survey line is 2 to 5 meters; in deeper areas, the survey line spacing is appropriately increased to 10 to 20 meters.

[0025] A monitoring point is defined as the center of a pre-divided inversion grid cell within the three-dimensional space below the probe array, and its spatial position is strictly constrained by this three-dimensional coordinate system. Specifically, based on the calibrated survey line trajectory, the underground space to be measured is divided into several grids with three-dimensional coordinates (X, Y, Z), and the center point of each grid cell is defined as a monitoring point. The horizontal side length of the grid cell is 0.5 to 1 times the electrode spacing on the corresponding survey line, and the vertical side length is set at equal intervals or increasing with depth. For example, in an area with an electrode spacing of 2 meters, the three-dimensional size of the grid cell can be set to 1m × 1m × 1m, thereby constructing a high-density fixed three-dimensional monitoring point array underground corresponding to the surface survey network. During each independent monitoring period, the high-density electrical resistivity meter acquires initial electrical signals by switching different electrode combinations, and based on the fixed three-dimensional monitoring point array, maps the initial electrical signals acquired at the surface to each grid cell underground using a three-dimensional resistivity inversion method. The values ​​of the center of each grid cell after inversion processing are extracted to determine the resistivity measured at each monitoring point during each monitoring period. It should be noted that the three-dimensional resistivity inversion method is a well-known technique in the art, and will not be further limited or elaborated here.

[0026] Given the complexity of underground geological structures in mining areas, static low-resistivity geological bodies such as metallic ore bodies or stable karst aquifers often exhibit a high degree of physical similarity to dynamic water inflow sections during single-period resistivity exploration. Relying solely on absolute resistivity values ​​for analysis makes it difficult to accurately isolate geological background interference from a spatiotemporal perspective. Therefore, after acquiring time-series resistivity data from each monitoring point, it is necessary to calculate the relative resistivity change at each monitoring point in adjacent monitoring periods, and use this to determine the relative water level change during each monitoring period. The aim is to effectively distinguish between static rock mass characteristics and dynamic water transport signals by extracting the instantaneous dynamic differences in resistivity, providing crucial hydrological evidence for the subsequent accurate identification of active dynamic water inflow channels.

[0027] Step S102: Perform cluster analysis based on the resistivity fluctuations and corresponding locations of each monitoring point to determine all monitoring point clusters; within each monitoring point cluster, determine the corresponding fluctuation intensity based on the temporal trend of resistivity changes at each monitoring point; determine the corresponding water level change consistency coefficient based on the consistency of the relative water level changes at each monitoring point.

[0028] Considering the high heterogeneity and spatial connectivity of karst systems in mining areas, scattered and isolated monitoring data cannot fully reflect the true structural characteristics of groundwater transport channels. Therefore, this embodiment of the invention extracts the dynamic change signals of each monitoring point and performs cluster analysis based on the resistivity fluctuations of each monitoring point and its corresponding position in three-dimensional space to determine all monitoring point clusters. By comprehensively integrating spatial topological relationships and electrical activity characteristics for collaborative partitioning, monitoring points with similar evolutionary patterns and similar spatial distributions can be aggregated into geological entities with clear physical boundaries. This not only transforms discrete data points into continuous structural information but also effectively eliminates spurious anomaly signals with chaotic spatial distribution, laying a structural foundation for subsequent accurate quantification of the activity state of water-conducting channels and the consistency of water flow trends at the overall level.

[0029] Furthermore, considering that each monitoring point cluster may contain static geological bodies whose electrical characteristics remain unchanged over time, this embodiment of the invention further determines the corresponding degree of fluctuation within each monitoring point cluster based on the temporal variation trend of the resistivity of each monitoring point. By quantifying the electrical activity state and fluctuation characteristics of the cluster throughout the entire monitoring period, it is possible to effectively identify whether there is an active fluid exchange process caused by the alternation of groundwater inflow and outflow in the area. This step eliminates static water-bearing rock masses or ore bodies with weak electrical fluctuations, accurately anchors potential water inrush risk sources with highly dynamic water flow characteristics, and greatly improves the ability to identify real dynamic water-bearing sections from complex geological backgrounds.

[0030] After clarifying the electrical activity characteristics of each cluster, the actual rise and fall of groundwater levels and discontinuous geological structures such as fractures and faults can cause local electrical fluctuations to deviate from the actual hydrological connectivity. To verify whether a continuous and complete water-conducting channel is formed within the cluster, this embodiment of the invention determines the corresponding water level change consistency coefficient based on the consistency of the relative changes in water level at each monitoring point. By analyzing the spatial synergy of water flow rise and fall within the cluster, areas blocked by faults or disconnected by fractures can be effectively eliminated, ensuring that the identified abnormal areas conform to the natural laws of groundwater connectivity and transport.

[0031] Step S103: Based on the degree of fluctuation and the consistency coefficient of water level change, determine the dynamic water content reliability of each monitoring point cluster; based on the dynamic water content reliability, perform a visual modeling of the water-bearing section of the karst aquifer in the mining area.

[0032] To more comprehensively and objectively evaluate the actual risk of each cluster as a potential water source, the dynamic water content reliability of each monitoring point cluster was further determined based on the degree of fluctuation and the consistency coefficient of water level changes. This process deeply integrates and quantifies the electrical fluctuation characteristics that characterize fluid activity with the hydrological evolution trend that characterizes channel connectivity, rigorously filtering out false anomalies from multiple dimensions and achieving accurate screening of dynamic water-producing sections of karst aquifers in mining areas.

[0033] To visually represent the spatial distribution and discharge patterns of concealed water-conducting channels, this invention employs a visualization modeling method for the water-bearing sections of karst aquifers in mining areas based on dynamic water content reliability. By transforming highly reliable dynamic water-bearing areas into three-dimensional spatial forms, the specific locations and spatial relationships of karst caves, fissures, and water-bearing sections can be clearly and intuitively depicted, resulting in higher accuracy in the visualization modeling of water-bearing sections of karst aquifers in mining areas.

[0034] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the relative change in water level includes: Based on the relative deviation between the resistivity of each monitoring point in each monitoring period and the resistivity in the previous monitoring period, the corresponding resistivity period change value is determined; based on the resistivity period change value and the corresponding resistivity, the resistivity period change rate of each monitoring point in each monitoring period is determined; the resistivity period change value and the resistivity period change rate are positively correlated, and the resistivity and the resistivity period change rate are negatively correlated.

[0035] In one specific implementation of this invention, the resistivity of each monitoring point in each monitoring period is subtracted from the resistivity of the previous monitoring period to determine the resistivity period change value for each monitoring period. For the first monitoring period, its corresponding resistivity period change value is set to 0 to avoid interruption of the calculation process due to the lack of prior historical reference data. Then, the resistivity period change rate is determined based on the ratio between the resistivity period change value and the corresponding resistivity of each monitoring point in each monitoring period. Here, by calculating the relative ratio rather than the absolute difference, the influence of inconsistent initial resistivity base values ​​of different lithologies in different underground layers is eliminated, making the dynamic evolution characteristics of different depths have uniform comparability. It should be noted that if the resistivity of each monitoring point in each monitoring period is 0, it indicates that the monitoring point is affected by extremely low resistivity or hardware failure, resulting in abnormal data. In this case, a preset minimal positive adjustment factor (e.g., 0.001) is directly added to the value to avoid the denominator being 0.

[0036] A negative correlation mapping is performed on the resistivity period change rate to determine the relative water level change at each monitoring point during each monitoring period. In one specific implementation of this invention, the corresponding relative water level change is determined based on the product of the resistivity period change rate at each monitoring point during each monitoring period and a preset electro-water coupling calibration coefficient; wherein the preset electro-water coupling calibration coefficient is negative.

[0037] In one specific implementation of this invention, the range of values ​​for the preset electro-water coupling calibration coefficient is set to... In this embodiment of the invention, the calibration coefficient is preferably set to -1.8, with the unit being meters (m). It can be adjusted automatically according to the rock porosity and groundwater mineralization in the specific implementation environment. When the rock porosity or groundwater mineralization in the detection area is small, the sensitivity of groundwater level rise and fall to overall resistivity change is weak. In this case, a calibration coefficient with a larger absolute value (such as -2.5) should be selected to amplify the actual water level change corresponding to electrical micro-disturbance. Conversely, if the karst in the detection area is highly developed, i.e., large porosity, or the water is rich in highly conductive ions, i.e., high mineralization, a small water level change can cause a drastic decrease in resistivity. In this case, a calibration coefficient with a smaller absolute value (such as -1.0) should be selected to ensure that the calculation results accurately match the actual hydrogeological response characteristics of the local area.

[0038] In karst aquifers within mining areas, a significant negative correlation exists between the resistivity of the medium and the water level. When the water level rises and the karst caves and fissures are filled more fully with water, the overall resistivity of the medium decreases due to the superior conductivity of water compared to the rock mass. In this case, the calculated resistivity change rate over time is negative. Conversely, when the water level drops and the rock mass is drained, the resistivity increases, and the change rate is positive. Therefore, by introducing an electro-hydraulic coupling calibration coefficient with a negative sign and length dimension for negative correlation mapping, the dimensionless electrical changes can be accurately converted into absolute length changes with practical hydrological significance. This allows the calculated positive values ​​to accurately represent rising water levels and negative values ​​to accurately represent falling water levels, thus objectively reconstructing the dynamic filling and draining process of groundwater within concealed water-conducting channels.

[0039] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the monitoring point clusters includes: Based on the overall magnitude of the resistivity period change rate of each monitoring point across all monitoring periods, the corresponding overall resistivity change rate is determined; in this embodiment of the invention, the mean of the resistivity period change rate of each monitoring point across all monitoring periods is taken as the corresponding overall resistivity change rate.

[0040] Obtain the three-dimensional spatial coordinates of each monitoring point; perform cluster analysis based on the three-dimensional spatial coordinates of each monitoring point and the corresponding overall resistivity change rate to determine all monitoring point clusters. The three-dimensional spatial coordinates here correspond to the coordinates of each monitoring point in the three-dimensional coordinate system in step S101. In this embodiment of the invention, based on the three-dimensional spatial coordinates of each monitoring point, the overall resistivity change rate is introduced as a fourth dimension to construct a cluster vector for calculation. The cluster vector is expressed by the formula: ;in, For monitoring points Clustering vectors; For monitoring points The normalized value of the X-axis coordinate in three-dimensional space; For monitoring points The normalized value of the Y-axis coordinate in three-dimensional space; For monitoring points The normalized value of the Z-axis coordinate of the three-dimensional spatial coordinates; the normalization method used is maximum value normalization, and the corresponding maximum value is determined based on the maximum coordinate value of all monitoring points on the corresponding coordinate axis; For monitoring points The overall rate of change of resistivity. In this embodiment of the invention, cluster analysis is performed using the DBSCAN algorithm based on the clustering vectors of each monitoring point. The minimum number of points is set to 5 to 10 (preferably 5 in this embodiment of the invention), and the neighborhood radius is set to 0.05 to 0.3 (preferably 0.1 in this embodiment of the invention). These values ​​can be adjusted according to the specific implementation environment. The DBSCAN algorithm is a well-known technique in the art, and will not be further limited or described here.

[0041] Water-conducting channels or outflow sections in karst aquifers of mining areas are typically spatially continuous, and during dynamic evolution, monitoring points within the same connected body often exhibit similar electrical activity characteristics. By fusing geographical location information with electrical dynamic indicators (overall resistivity change rate) to construct a four-dimensional feature vector, the DBSCAN algorithm can accurately cluster spatially adjacent monitoring points with similar electrical change patterns into clusters. This method not only effectively characterizes the three-dimensional spatial outline of concealed water-conducting channels but also automatically filters out spatially scattered and isolated interference signal points through a density screening mechanism. This ensures that the subsequently extracted clusters have clear hydrogeological significance, providing reliable structural units for distinguishing between static background bodies and dynamic outflow sections.

[0042] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the intensity of fluctuation includes: Within each monitoring point cluster, the corresponding spatiotemporal activity factor is determined based on the mean of the resistivity period change rate of all monitoring points in each monitoring period. The degree of potential change is determined based on the overall magnitude of the spatiotemporal activity factor of each monitoring point cluster across all monitoring periods. In this embodiment of the invention, the mean of the absolute values ​​of the spatiotemporal activity factor of each monitoring point cluster across all monitoring periods is used as the corresponding degree of potential change.

[0043] Based on the discrete characteristics of the spatiotemporal activity factor of each monitoring point cluster under all monitoring periods, the corresponding potential fluctuation degree is determined; in this embodiment of the invention, the standard deviation of the spatiotemporal activity factor of each monitoring point cluster under all monitoring periods is used as the corresponding potential fluctuation degree. Based on the degree of potential fluctuation and the degree of potential change, the intensity of fluctuation of each monitoring point cluster is determined; the degree of potential fluctuation and the intensity of fluctuation are positively correlated, while the degree of potential change and the intensity of fluctuation are negatively correlated; in this embodiment of the invention, the ratio between the degree of potential fluctuation and the degree of potential change of each monitoring point cluster is normalized to determine the intensity of fluctuation; the normalization method here adopts minimum-maximum normalization, and the maximum and minimum values ​​in the minimum-maximum normalization process are selected from all monitoring point clusters.

[0044] It should be noted that for each cluster of monitoring points, if the corresponding potential change is 0, it means that the electrical characteristics of the area are completely static throughout all monitoring periods and do not have dynamic water flow characteristics. In this case, the fluctuation intensity of the cluster is directly determined to be 0 to avoid calculation crashes caused by a denominator of 0.

[0045] Real and active dynamic water-bearing zones typically exhibit dramatic jumps in electrical characteristics due to fluctuations in groundwater levels, characterized by highly uneven activity over time (large standard deviation). In contrast, some static low-resistivity bodies (such as stable ore bodies or perpetually saturated aquifers) may possess high initial electrical responses (large mean), but their changes over time are very stable. By introducing a coefficient of variation logic—that is, normalizing the degree of potential change (mean) using the degree of potential fluctuation (standard deviation)—interference caused by differences in the baseline electrical properties of the geological background can be effectively eliminated, thus accurately extracting dynamically active areas with "intermittent, sudden" water exchange characteristics. This step can eliminate static background bodies with obvious electrical characteristics but exhibiting "dead" behavior, significantly improving the targeting of underground water inflow channel identification.

[0046] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the water level change consistency coefficient includes: The relative water level changes of each monitoring point are arranged chronologically over all monitoring periods to determine the corresponding relative water level change sequence; the overall water level change is determined based on the total absolute value of the relative water level changes of each monitoring point over all monitoring periods; and the overall water level change is determined based on the mean of the absolute values ​​of the relative water level changes of each monitoring point over all monitoring periods.

[0047] Based on the sequence similarity characteristics of the relative water level change sequences among the monitoring points in each monitoring point cluster, the corresponding trend consistency is determined; in a specific implementation of this invention, the process of obtaining trend consistency in each monitoring point cluster includes: Each monitoring point is sequentially designated as the target point, and other monitoring points are designated as reference points. The DTW distance between the relative water level change sequence of the target point and the corresponding relative water level change sequence of each reference point is calculated using the Dynamic Time Warping (DTW) algorithm, and negative correlation mapping is performed to determine the sequence similarity between the target point and each corresponding reference point. The calculation process for sequence similarity is expressed by the following formula: ;in, For target point With the corresponding first Sequence similarity between reference points; For target point The relative water level change sequence and the corresponding first DTW distance between the relative water level change sequences of each reference point; The minimum-maximum normalization function is used, and the maximum and minimum values ​​used in the minimum-maximum normalization function are determined based on all DTW distances calculated in all monitoring point clusters. No further limitations or elaborations are made here.

[0048] The reference similarity of the target point is determined based on the overall magnitude of the sequence similarity between the target point and all corresponding reference points; in this embodiment of the invention, the corresponding reference similarity is determined based on the mean of the sequence similarity between the target point and all corresponding reference points.

[0049] The trend consistency is determined based on the overall magnitude of the reference similarity of all monitoring points in each monitoring point cluster. In this embodiment, the trend consistency is determined based on the mean of the reference similarity of all monitoring points in each monitoring point cluster. According to the principle of the Dynamic Time Warping (DTW) algorithm, the smaller the calculated DTW distance, the more matched the two time series are in terms of the evolutionary morphology and timing of water flow filling and draining, resulting in higher sequence similarity. Therefore, the larger the mean of the reference similarity of all monitoring points within a cluster, the more synchronized the groundwater flow response of each monitoring point in the region is in its temporal evolution trajectory, and the higher the consistency of the corresponding hydrological evolution trend.

[0050] Based on the relative dispersion of the overall water level change of all monitoring points in each monitoring point cluster, the corresponding degree of water level fluctuation is determined. Specifically, the degree of dispersion of water level change is determined based on the standard deviation of the overall water level change of all monitoring points in each monitoring point cluster. The standard value of water level change is determined based on the overall magnitude of the absolute value of the overall water level change of all monitoring points in each monitoring point cluster. In this embodiment of the invention, the standard value of water level change is determined by the mean of the absolute values ​​of the overall water level change of all monitoring points in each monitoring point cluster.

[0051] The dispersion of water level changes is standardized using a standard value for water level changes to determine the degree of water level fluctuation for each monitoring point cluster. In this embodiment of the invention, the ratio between the dispersion of water level changes and the standard value of water level changes is used as the degree of water level fluctuation for each monitoring point cluster.

[0052] Simply relying on dispersion (standard deviation) cannot eliminate the dimensional influence caused by differences in the baseline of local water level changes at different burial depths or porosities. By standardizing the dispersion of water level changes using the standard value of water level changes, a dimensionless evaluation index similar to the coefficient of variation is constructed. This provides a unified evaluation scale for water level fluctuations within different clusters, reflecting the relatively disordered state of water level evolution within clusters more objectively and fairly. It should be noted that if the standard value of water level change is 0, it indicates that the electrical characteristics of all monitoring points within a cluster are completely static throughout the monitoring period, without any effective dynamic response to water level evolution. In this case, the water level fluctuation of that cluster is directly determined to be 0. Furthermore, since the overall change in water level represents the cumulative rise and fall of water level at each monitoring point throughout the entire monitoring period, the greater the dispersion of water level change determined by the standard deviation of the overall change in water level at all monitoring points in each cluster, the more significant the difference in the magnitude of water level change between each monitoring point within the cluster (i.e., extremely uneven spatial hydrodynamic distribution), and the worse the hydraulic connectivity and the higher the degree of water level fluctuation within the corresponding cluster.

[0053] Based on trend consistency and the degree of water level fluctuation, a water level change consistency coefficient is determined for each monitoring point cluster. Trend consistency is positively correlated with the water level change consistency coefficient, while the degree of water level fluctuation is negatively correlated with the water level change consistency coefficient. In a specific implementation of this invention, the process of obtaining the water level change consistency coefficient is expressed by the following formula: ;in, Clustering of monitoring points The consistency coefficient of water level changes; Clustering of monitoring points Consistency of trends; Clustering of monitoring points The degree of water level fluctuation; It is an exponential function with the natural constant as its base.

[0054] Real karst water-conducting channels exhibit high hydrodynamic connectivity. When water flows in or out, the water level fluctuations at monitoring points within the same channel should not only be similar in absolute magnitude but also highly synchronized in temporal evolution. By introducing dual constraints of trend consistency and relative water level fluctuation, random and chaotic water level abrupt changes caused by fault barriers, isolated local fissures, or geological noise can be effectively identified and eliminated, thereby accurately identifying a real underground water-conducting network with complete hydraulic connections.

[0055] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining dynamic moisture content confidence includes: The dynamic water content confidence of each monitoring point cluster is determined by weighted fusion of the fluctuation intensity and the water level change consistency coefficient. Specifically, the product of the fluctuation intensity and the preset first weight is used as the weighted fluctuation intensity; the product of the water level change consistency coefficient and the preset second weight is used as the weighted consistency coefficient; and the dynamic water content confidence of each monitoring point cluster is determined based on the sum of the weighted fluctuation intensity and the weighted consistency coefficient.

[0056] In one specific implementation of this invention, the sum of the preset first weight and the preset second weight is set to 1. Preferably, the preset first weight is set to 0.6 and the preset second weight is set to 0.4. These can be adjusted according to the specific implementation environment. The larger the preset first weight is, the more attention is paid to the intensity of the potential fluctuation. The larger the preset second weight is, the more attention is paid to the consistency coefficient of water level change, which represents the rise and fall of water level.

[0057] The dramatic electrical fluctuations ruled out interference from static geological bodies (such as metallic ore bodies), proving the existence of fluid exchange in the area. Meanwhile, the high consistency of water level changes ruled out the influence of isolated fractures or local noise, demonstrating that fluid exchange occurred within a connected water-conducting network. By weighted fusion of the fluctuation intensity and the water level change consistency coefficient, a dual cross-validation was achieved, from physical activity to hydrological connectivity. The resulting confidence level accurately quantifies the probability that the cluster belongs to a dynamically emerging section of a karst aquifer.

[0058] Preferably, in some possible implementations of the embodiments of the present invention, the process of visually modeling the aquifer section of a karst aquifer in a mining area based on dynamic water content confidence includes: Monitoring points in clusters corresponding to dynamic water content confidence levels greater than or equal to a preset confidence threshold are designated as reliable points for the water-producing section. Visual modeling of the water-producing section of the karst aquifer in the mining area is performed based on all reliable points for the water-producing section. Specifically: the three-dimensional spatial coordinates of all reliable points for the water-producing section are extracted, along with the corresponding dynamic water content confidence level and overall water level change. Using the three-dimensional spatial coordinates as spatial indices, the reliable points for the water-producing section are remapped to the three-dimensional inversion grid cells divided in step S101, and the corresponding dynamic water content confidence level and overall water level change are assigned to the corresponding grid cells as hydrological evolution attributes. Blank grid cells without assigned values ​​are removed, and the outer boundary nodes of the remaining grid cells are extracted and reconstructed using a three-dimensional surface reconstruction algorithm (Marching, a method known to those skilled in the art, is used in this embodiment). The Cubes algorithm generates a physical contour model of the water outlet section that encloses the region. Inside the physical contour model of the water outlet section, a continuous field of hydrodynamic distribution is generated by three-dimensional spatial interpolation based on the overall change in water level of each grid cell. The corresponding rendering color or transparency parameter is mapped according to the dynamic water content confidence level of each grid cell, and three-dimensional voxel rendering is performed to generate a three-dimensional visualization model of the dynamic water outlet section of the karst aquifer in the mining area with hydrological evolution attributes.

[0059] This complete visualization modeling process not only eliminates non-aquifer, low-resistivity background bodies in spatial geometry and precisely defines the physical outline of the water-bearing section, but also deeply binds numerical attributes reflecting the dynamic laws of groundwater flow into the 3D model. This makes the final model not just a spatial shell, but a data complex with hydrological evolution information, resulting in higher accuracy in locating and constructing models of the water-bearing sections of karst aquifers in mining areas.

[0060] In summary, a visualization modeling method for the water-bearing section of karst aquifers in mining areas introduces dynamic monitoring characteristics by acquiring the resistivity of monitoring points over multiple monitoring periods and determining the relative change in water level based on the relative change in resistivity between adjacent periods. This overcomes the shortcomings of existing technologies that rely solely on single-period static electrical parameters and are easily interfered with by static geological bodies such as low-resistivity ore bodies. Furthermore, cluster analysis based on resistivity fluctuations and locations effectively delineates spatial regions with similar evolutionary characteristics. The method further combines the intensity of fluctuations at each monitoring point with the consistency coefficient of water level changes to comprehensively determine the dynamic water-bearing reliability. This deeply filters geological background noise from the dual perspectives of electrical temporal activity and the synergy of hydrological evolution trends, achieving accurate identification and extraction of real fluid movement dynamic signals. Finally, visualization modeling based on dynamic water-bearing reliability effectively eliminates interference from non-aquifer low-resistivity bodies, thereby improving the accuracy of locating and constructing models of the water-bearing section of karst aquifers in mining areas.

[0061] This invention also provides a visualization modeling system for the aquifer section of karst aquifers in mining areas. Please refer to [link / reference]. Figure 2 The diagram shows a structural diagram of a visualization modeling system for the water-bearing section of a karst aquifer in a mining area, provided by an embodiment of the present invention. The system includes: a data acquisition and preprocessing module 201, a parameter determination module 202, and a visualization modeling module 203.

[0062] The data acquisition and preprocessing module 201 is used to acquire the resistivity measured at each monitoring point in the karst aquifer of the mining area under test, and to determine the relative change in water level in each monitoring period based on the relative change in resistivity at each monitoring point in adjacent monitoring periods. The parameter determination module 202 is used to perform cluster analysis based on the resistivity fluctuations and corresponding locations of each monitoring point to determine all monitoring point clusters; within each monitoring point cluster, the corresponding fluctuation intensity is determined based on the temporal variation trend of the resistivity of each monitoring point; and the corresponding water level change consistency coefficient is determined based on the consistency of the variation trend of the relative water level change of each monitoring point. The visualization modeling module 203 is used to determine the dynamic water content confidence of each monitoring point cluster based on the degree of fluctuation and the consistency coefficient of water level change; and to perform visualization modeling of the water-bearing section of the karst aquifer in the mining area based on the dynamic water content confidence.

[0063] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the visualization modeling system for the water-bearing section of a karst aquifer in a mining area and the visualization modeling method for the water-bearing section of a karst aquifer in a mining area provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0064] This invention also provides a computer device; please refer to [link / reference]. Figure 3 The diagram illustrates a computer device structure according to an embodiment of the present invention. The computer device includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. When the processor 302 executes the computer program 303, the computer device can execute any of the aforementioned visualization modeling methods for the water-bearing sections of karst aquifers in mining areas.

[0065] This invention also provides a computer program product that, when run on a computer device, enables the computer device to execute any of the aforementioned visualization modeling methods for the water-bearing sections of karst aquifers in mining areas.

[0066] This invention also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer device, the computer device can execute any of the aforementioned visualization modeling methods for the water-bearing sections of karst aquifers in mining areas.

[0067] In the embodiments provided by the present invention, it should be understood that the computer device, computer program product and computer-readable storage medium provided are all used to execute the corresponding methods provided above, and therefore the beneficial effects they can achieve can be referred to the beneficial effects of the methods provided above, which will not be repeated here.

[0068] 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.

[0069] 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 visually modeling the aquifer section of a karst aquifer in a mining area, characterized in that, The method includes: In the karst aquifer of the mining area to be tested, the resistivity of each monitoring point is obtained at each monitoring period; based on the relative change of resistivity of each monitoring point in adjacent monitoring periods, the relative change of water level in each monitoring period is determined. Cluster analysis was performed on the resistivity fluctuations and corresponding locations of each monitoring point to determine all monitoring point clusters. Within each monitoring point cluster, the degree of fluctuation was determined based on the temporal trend of resistivity changes at each monitoring point. The water level change consistency coefficient was determined based on the consistency of the relative water level changes at each monitoring point. Based on the degree of fluctuation and the consistency coefficient of water level change, the dynamic water content confidence of each monitoring point cluster is determined; based on the dynamic water content confidence, a visual model of the water-bearing section of the karst aquifer in the mining area is performed.

2. The method for visual modeling the aquifer section of a karst aquifer in a mining area according to claim 1, characterized in that, The process of obtaining the relative change in water level includes: Based on the relative deviation between the resistivity of each monitoring point in each monitoring period and the resistivity in the previous monitoring period, the corresponding resistivity period change value is determined; based on the resistivity period change value and the corresponding resistivity, the resistivity period change rate of each monitoring point in each monitoring period is determined; the resistivity period change value and the resistivity period change rate are positively correlated, and the resistivity and the resistivity period change rate are negatively correlated. By performing a negative correlation mapping on the resistivity period change rate, the relative water level change at each monitoring point in each monitoring period is determined.

3. The method for visual modeling the aquifer section of a karst aquifer in a mining area according to claim 2, characterized in that, The process of obtaining the clusters of monitoring points includes: Based on the overall magnitude of the resistivity period change rate at each monitoring point across all monitoring periods, determine the corresponding overall resistivity change rate. Obtain the three-dimensional spatial coordinates of each monitoring point; perform cluster analysis based on the three-dimensional spatial coordinates of each monitoring point and the corresponding overall resistivity change rate to determine all monitoring point clusters.

4. The visualization modeling method for the water-bearing section of a karst aquifer in a mining area according to claim 2, characterized in that, The process of obtaining the intensity of the fluctuation includes: Within each monitoring point cluster, the corresponding spatiotemporal activity factor is determined based on the mean of the resistivity period change rate of all monitoring points in each monitoring period. Based on the overall magnitude of the spatiotemporal activity factor of each monitoring point cluster across all monitoring periods, the corresponding potential change degree is determined. Based on the discrete characteristics of the spatiotemporal activity factor of each monitoring point cluster across all monitoring periods, the corresponding potential fluctuation degree is determined. Based on the degree of potential fluctuation and the degree of potential change, the degree of fluctuation intensity of each monitoring point cluster is determined; the degree of potential fluctuation and the degree of fluctuation intensity are positively correlated, and the degree of potential change and the degree of fluctuation intensity are negatively correlated.

5. The method for visual modeling the aquifer section of a karst aquifer in a mining area according to claim 1, characterized in that, The process of obtaining the water level change consistency coefficient includes: The relative water level changes of each monitoring point in all monitoring periods are arranged in chronological order to determine the corresponding relative water level change sequence; the overall water level change is determined based on the total absolute value of the relative water level changes of each monitoring point in all monitoring periods. Based on the sequence similarity characteristics of the relative water level change sequences among the monitoring points in each cluster, the corresponding trend consistency is determined. The degree of water level fluctuation is determined based on the relative dispersion of the overall water level change of all monitoring points in each monitoring point cluster. Based on the trend consistency and the degree of water level fluctuation, a water level change consistency coefficient is determined for each monitoring point cluster. The trend consistency is positively correlated with the water level change consistency coefficient, and the degree of water level fluctuation is negatively correlated with the water level change consistency coefficient.

6. The method for visual modeling the aquifer section of a karst aquifer in a mining area according to claim 5, characterized in that, The process of obtaining the degree of water level fluctuation includes: The degree of dispersion of water level changes is determined based on the standard deviation of the overall water level change of all monitoring points in each monitoring point cluster. The standard value of water level change is determined based on the overall magnitude of the absolute value of the overall water level change of all monitoring points in each monitoring point cluster. The dispersion of water level changes is standardized using the standard value of water level change to determine the degree of water level fluctuation for each monitoring point cluster.

7. The method for visual modeling the aquifer section of a karst aquifer in a mining area according to claim 1, characterized in that, The process of obtaining the dynamic water content reliability includes: The dynamic water content reliability of each monitoring point cluster is determined by weighted fusion of the fluctuation intensity and the water level change consistency coefficient.

8. The method for visual modeling the aquifer section of a karst aquifer in a mining area according to claim 1, characterized in that, The process of visually modeling the aquifer section of a karst aquifer in a mining area based on the dynamic water content confidence includes: Monitoring points in the cluster corresponding to dynamic water content confidence levels greater than or equal to the preset confidence threshold are taken as confidence points of the water outlet section; and a visual model of the water outlet section of the karst aquifer in the mining area is performed based on all confidence points of the water outlet section.

9. A method for visually modeling the aquifer section of a karst aquifer in a mining area according to claim 5, characterized in that, The process of obtaining the trend consistency includes: Within each monitoring point cluster, each monitoring point is sequentially designated as the target point, and the other monitoring points outside the target points are designated as reference points. The DTW distance between the relative water level change sequence of the target point and the relative water level change sequence of each corresponding reference point is calculated based on the dynamic time warping algorithm, and negative correlation mapping is performed to determine the sequence similarity between the target point and each corresponding reference point. Based on the overall magnitude of the sequence similarity between the target point and all corresponding reference points, the reference similarity of the target point is determined. Based on the overall magnitude of the reference similarity of all monitoring points in each monitoring point cluster, the corresponding trend consistency is determined.

10. A visualization modeling system for the aquifer section of a karst aquifer in a mining area, characterized in that, The system includes: The data acquisition and preprocessing module is used to acquire the resistivity measured at each monitoring point in the karst aquifer of the mining area under test, and to determine the relative change in water level in each monitoring period based on the relative change in resistivity at each monitoring point in adjacent monitoring periods. The parameter determination module is used to perform cluster analysis based on the resistivity fluctuations and corresponding locations of each monitoring point to determine all monitoring point clusters; within each monitoring point cluster, the module determines the degree of fluctuation based on the temporal trend of resistivity changes at each monitoring point; and determines the water level change consistency coefficient based on the consistency of the relative water level changes at each monitoring point. The visualization modeling module is used to determine the dynamic water content reliability of each monitoring point cluster based on the intensity of the fluctuation and the consistency coefficient of water level change; and to perform visualization modeling of the water-bearing section of the karst aquifer in the mining area based on the dynamic water content reliability.

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