Reconnaissance system and method for ecological restoration of acidic mine gushing water in karst region

Abstract

The system comprises an air-based remote sensing module, a space-based detection module, a ground investigation module and an in-well detection module, an air-space-ground-well integrated technology is adopted, and through progressive cooperative logic of air-based screening, space-based verification, ground testing and in-well scanning, the water inrush ecological restoration investigation system for the acid mine in the karst region is established. And accurate positioning of the hidden karst pipeline in the karst region is realized. According to the method, accurate positioning of the hidden karst pipeline in the karst region is achieved through the sky-sky-ground-well integrated technology, the exploration efficiency and precision are remarkably improved, and the cost is reduced. Compared with the prior art, the method has the advantages that the pollution source target region can be quickly locked, the groundwater dominant channel can be accurately identified, the ground and underground space correlation model is established, a more efficient and more scientific investigation means is provided for the acid mine gushing water ecological restoration in the karst region, and the precision and high efficiency of mine ecological restoration work are powerfully promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of mine ecological restoration, and relates to an ecological restoration and exploration system for acid mine water inrush in karst areas, and a method for ecological restoration and exploration for acid mine water inrush in karst areas. Background Art

[0002] Mine ecological restoration is a crucial task in promoting ecological civilization, and the management of acid mine drainage (AMD) is particularly critical. AMD, characterized by low pH and high heavy metal content, poses a serious threat to the fragile hydrogeological environment and ecosystems in karst regions. Traditional AMD management methods rely primarily on source control (such as blocking groundwater recharge) and end-of-pipe treatment (such as chemical neutralization). However, in historical mines in karst areas, traditional technologies face significant challenges due to the development of karst landforms, the interweaving of mine tunnels, the concealed pollution sources, and the complex underground space.

[0003] In the process of realizing the present invention, the inventors discovered that the prior art has at least one of the following technical problems: a) Traditional hydrogeological surveys rely on a single technical approach (such as surface drilling and geophysical exploration), making it difficult to integrate surface and underground, macroscopic and microscopic data, resulting in long survey cycles and high costs. b) Satellite remote sensing cannot penetrate vegetation-covered areas, and ground geophysical exploration is limited by the complexity of karst structures, making it difficult to accurately identify high-lying ground fissures, karst funnels, and other advantageous groundwater recharge channels; c) Manual mine reconnaissance is time-consuming and carries high safety risks. 3D laser scanning lacks synergy with surface data, resulting in large deviations in drilling positioning and a lack of spatial correlation support for remediation plans. d) Existing technologies lack a mechanism for dynamic backtracking of multi-period remote sensing images and cross-validation of multi-source data, making it difficult to identify the migration paths of pollution sources, which can easily lead to delayed or over-engineered control measures.

[0004] The above problems have seriously restricted the efficiency and effectiveness of AMD control in karst areas. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an innovative technology system that integrates multi-dimensional data, penetrates complex surface interference, and realizes ground-underground collaborative modeling, providing precise support for ecological restoration.

[0006] Through long-term exploration and experimentation, as well as numerous experiments and efforts, and continuous reform and innovation, the inventors have come up with a technical solution to solve the above technical problems. The present invention provides a system for ecological restoration and exploration of acid mine water inrush in karst areas. This system uses an integrated "air-space-ground-well" technology and a progressive collaborative logic of air-based screening, space-based verification, ground testing, and well scanning to achieve precise positioning of hidden karst pipelines in karst areas. The system includes the following modules: (a) Airborne remote sensing module, used to identify regional topography, surface water systems, and historical tailings piles using satellite remote sensing imagery, and to identify pollution source targets through dynamic analysis of multiple historical imagery periods; (b) Space-based detection module, equipped with drone-mounted LiDAR, uses multiple echo technology and filtering algorithms to penetrate vegetation interference, obtain real ground elevation data and drone LiDAR point cloud, and identify high-level ground fissures, karst funnels, and sinkholes in karst areas; (c) Ground survey module, combining high-density geophysical exploration, hydrogeological drilling, pumping tests, and tracer tests to obtain the temporal and spatial distribution characteristics of the mining area's geological structure, fracture network, and dominant groundwater pathways; (d) The in-well detection module acquires 3D point cloud data of goafs and caves through mine investigation and 3D laser scanning technology, and fuses it with the UAV LiDAR point cloud of the space-based detection module to establish a ground and underground space correlation model.

[0007] Compared with the prior art, the present invention has the following beneficial effects: This invention utilizes an integrated "air-sky-ground-well" technology to precisely locate hidden karst pipelines in karst areas, significantly improving survey efficiency and accuracy while reducing costs. Compared to existing technologies, this invention can rapidly locate pollution source targets, accurately identify dominant groundwater pathways, and establish a spatial correlation model between the surface and underground. This provides a more efficient and scientific survey method for the ecological restoration of acid mine water inrush in karst areas, significantly promoting the precision and efficiency of mine ecological restoration efforts.

[0008] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the airborne remote sensing module includes: Image pre-processing unit, used to perform radiation correction, geometric correction and cloud removal on satellite remote sensing images to improve image quality; The pollution source dynamic analysis unit compares multiple historical images, extracts the three-dimensional morphological parameters of the tailings pile based on the digital elevation model, analyzes its area, volume and sedimentation characteristics, and accurately locates the pollution source target area.

[0009] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The image preprocessing unit effectively improves the quality of satellite remote sensing images, providing a more accurate data basis for subsequent analysis; the pollution source dynamic analysis unit can accurately extract the three-dimensional morphological parameters of tailings piles through multi-period historical image comparison and digital elevation model, and conduct detailed analysis of their area, volume and sedimentation characteristics, thereby more accurately locking the pollution source target area, significantly improving the accuracy and efficiency of pollution source identification, and providing more reliable and detailed information support for the ecological restoration survey of acid mine water inrush in karst areas.

[0010] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the space-based detection module includes: Point cloud data processing unit, used to remove abnormal points and noise points in the collected LiDAR data and convert the data into the target coordinate system; The terrain modeling unit generates a digital elevation model based on ground point data and a digital surface model in combination with non-ground point data for three-dimensional terrain analysis of the mining area.

[0011] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The point cloud data processing unit effectively removes abnormal points and noise points in LiDAR data and converts them into the target coordinate system, improving the accuracy and usability of the data; the terrain modeling unit fully supports the three-dimensional terrain analysis of the mining area by generating digital elevation models and digital surface models, enhancing the understanding and grasp of the mining area's topography and landforms, providing more detailed and scientific data support for subsequent ecological restoration surveys, and improving the efficiency and quality of the survey.

[0012] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the ground survey module includes: The data collection and collation unit is used to collect geological and hydrogeological data of the survey area and verify the accuracy of the data through on-site surveys; The test program design and implementation unit obtains groundwater permeability and connectivity parameters based on high-density, hydrogeological drilling, pumping tests and tracer test programs.

[0013] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The data collection and compilation unit systematically integrates geological and hydrogeological data from the survey area and verifies its accuracy through on-site surveys, laying a solid foundation for subsequent work. The experimental design and implementation unit uses a variety of experimental methods to obtain key hydrogeological parameters, such as groundwater permeability and connectivity parameters. These data are crucial for understanding groundwater flow patterns and pollution migration pathways. Overall, this improvement significantly enhances the systematic and scientific nature of ground surveys, strengthens control over underground geological conditions in mining areas, provides reliable data support for the precise location of hidden karst conduits and the development of effective ecological restoration plans, and effectively improves the efficiency and quality of survey work.

[0014] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the well detection module includes: Scanning data processing unit, used to import the original point cloud data collected by the 3D laser scanner into professional software and realize multi-site cloud stitching through control point registration; The model optimization unit uses a curvature-based point cloud simplification algorithm and a smoothing filter algorithm to optimize the 3D model surface and adjust the texture to enhance the visualization effect.

[0015] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The scanning data processing unit effectively solves the problems of splicing and registering raw point cloud data, improving data integrity and accuracy. The model optimization unit optimizes the 3D model using advanced algorithms, improving not only the surface quality but also its visualization, making the underground spatial structure clearer and more intuitive. These improvements significantly enhance the efficiency and accuracy of well detection, providing more reliable and detailed underground spatial data support for the ecological restoration of acid mine water inrush in karst areas, and facilitating the development of more scientific and reasonable remediation plans.

[0016] The present invention also provides a method for ecological restoration and exploration of acid mine water inflow in karst areas, comprising the following steps: S1. Through satellite remote sensing image analysis, identify the terrain, water system and tailings pile target area of the work area based on remote sensing image interpretation algorithms, and determine the location of the pollution source by combining dynamic backtracking of multiple historical images; S2. Use UAV-mounted LiDAR to obtain high-precision terrain data and UAV LiDAR point clouds, remove vegetation interference through filtering algorithms, and identify hidden ground fissures, sinkholes, and karst funnels; S3. Conduct high-density ground geophysical exploration, hydrogeological drilling, and tracer testing to verify the connectivity of dominant groundwater channels and obtain hydrogeological parameters; S4. Obtain point cloud data of the goaf and karst caves through 3D laser scanning of the mine, and perform control point registration and spatial alignment with the drone LiDAR point cloud of the space-based detection module to establish a 3D spatial model of the ground and underground space in a unified coordinate system; S5. Generate a pollution source migration path analysis report based on multi-source fusion data, and determine the grouting curtain layout and drilling path through spatial analysis of the three-dimensional model to reduce treatment costs.

[0017] Compared with the prior art, the present invention has the following beneficial effects: Through the comprehensive application of the "air-space-ground-well" integrated technology, a full-scale survey from macro to micro, from surface to underground has been achieved, significantly improving the efficiency and accuracy of the survey and reducing costs. Specifically, satellite remote sensing image analysis can quickly identify terrain, water systems and tailings pile target areas, and dynamic backtracking of multiple historical images can accurately determine the location of pollution sources; the high-precision terrain data and point cloud obtained by the drone-mounted LiDAR effectively remove vegetation interference and clearly identify hidden ground fissures, sinkholes and karst funnels; ground high-density geophysical prospecting, hydrogeological drilling and tracer tests further verify the connectivity of the dominant groundwater channels and obtain key hydrogeological parameters; the fusion of mine three-dimensional laser scanning and space-based detection data establishes a three-dimensional spatial model of the ground and underground in a unified coordinate system, providing a scientific basis for the analysis of pollution source migration paths and the optimization of treatment plans. Finally, the spatial analysis of the three-dimensional model determines the location of the grouting curtain and the drilling path, significantly reducing the treatment cost and improving the scientific and economic nature of ecological restoration.

[0018] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the satellite remote sensing image analysis in step S1 includes: By comparing remote sensing images from different time periods, combined with terrain shadow analysis and DEM three-dimensional morphology extraction algorithm, the volume changes of tailings piles are quantified and the migration trend of pollution sources is determined.

[0019] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: By comparing remote sensing imagery from different time periods, combined with terrain shadow analysis and DEM three-dimensional morphology extraction algorithms, we can more accurately quantify the volume changes of tailings piles and clearly identify the migration trends of pollution sources. This improvement not only increases the accuracy of pollution source location but also provides a more scientific and dynamic basis for subsequent ecological restoration and management plans, helping to achieve precise management and rational resource allocation, thereby improving the efficiency and effectiveness of the entire ecological restoration project.

[0020] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the filtering algorithm in step S2 is specifically: Based on multiple LiDAR echo signals, an adaptive threshold segmentation technique is used to distinguish vegetation from ground points, and the point cloud data is corrected through an iterative nearest point algorithm to generate a bare ground elevation model.

[0021] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The filtering algorithm, based on multiple LiDAR echo signals, utilizes adaptive threshold segmentation technology to effectively distinguish vegetation from ground points. It then uses an iterative nearest point algorithm to precisely correct the point cloud data, generating a high-precision bare ground elevation model. This improvement significantly enhances the accuracy and reliability of terrain data, enabling clearer identification of geological features such as hidden ground fissures, sinkholes, and karst funnels. This provides more precise terrain information for subsequent ecological restoration surveys, enhancing the scientific nature and feasibility of remediation plans.

[0022] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the control point registration in step S4 is specifically as follows: The coordinates of ground control points are collected by total station or GPS, and the 3D laser scanning point cloud in the mine is spatially aligned with the UAV LiDAR point cloud, with the error controlled within ±0.1m.

[0023] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: The high-precision control point registration method significantly improves the accuracy of the surface and underground spatial correlation model, ensuring the reliability of the three-dimensional spatial model. It also makes the relative position relationship between underground caves, goafs and surface topography more precise, providing a more scientific basis for subsequent remediation projects.

[0024] On the basis of the above technical solution, the present invention can also be improved as follows: Furthermore, the method for determining the grouting curtain placement position in step S5 is: Based on the analysis results of the direction of the cave pipeline and the pollution migration path in the three-dimensional spatial model, a grouting curtain was laid downstream of the pipeline to avoid full-area coverage treatment and shorten the treatment period.

[0025] Compared with the prior art, the beneficial effects of adopting the above further technical solution are: This precise positioning method not only reduces unnecessary engineering workload and resource waste, but also improves the pertinence and effectiveness of control measures, thereby more efficiently blocking the diffusion path of pollution sources and accelerating the ecological restoration process of acid mine water in karst areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 This is a pollution source distribution map for the ecological restoration survey of acid mine water in karst areas.

[0028] Figure 2 This is the overall boundary of sinkhole LSD01. Figure 2 In the figure, Figure A is an optical satellite image and Figure B is a digital elevation model.

[0029] Figure 3 It is the overall boundary of the ground fissure LF1. Figure 3 In the figure, Figure A is an optical satellite image and Figure B is a digital elevation model.

[0030] Figure 4 It is a 3D laser modeling diagram. DETAILED DESCRIPTION

[0031] The following describes the details in conjunction with specific embodiments.

[0032] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention.

[0033] In the present invention, unless otherwise specified, all equipment and raw materials can be purchased from the market or are commonly used in the industry. The methods in the following embodiments, unless otherwise specified, are all conventional methods in the art.

[0034] Example 1 This embodiment describes a specific implementation method of an acid mine water inrush ecological restoration and exploration system in a karst area.

[0035] This embodiment describes a system for ecological restoration and exploration of acid mine water inflow in karst areas. It employs integrated "air-space-ground-well" technology, using a progressive, coordinated approach consisting of airborne screening, space-based verification, ground-based testing, and in-well scanning to precisely locate hidden karst conduits in karst areas. The system includes an airborne remote sensing module, a space-based detection module, a ground-based survey module, and an in-well detection module.

[0036] The airborne remote sensing module uses satellite remote sensing imagery to identify basic information such as regional topography, surface water systems, physical geography, and hydrogeological conditions. Remote sensing imagery is also used to identify pollution sources such as historical tailings piles within the work area. By retrospectively investigating the dynamic changes in historical imagery of suspected tailings piles, the module can further identify and target these piles.

[0037] The airborne remote sensing module includes an image preprocessing unit and a pollution source dynamic analysis unit.

[0038] Image pre-processing unit, used to perform radiation correction, geometric correction and cloud removal on satellite remote sensing images to improve image quality; The pollution source dynamic analysis unit compares multiple historical images, extracts the three-dimensional morphological parameters of the tailings pile based on the digital elevation model, analyzes its area, volume and sedimentation characteristics, and accurately locates the pollution source target area.

[0039] The space-based detection module, equipped with drone-mounted laser radar (LiDAR), obtains the three-dimensional surface coordinates of the observation area, providing high-resolution, high-precision topographic imagery. It uses multiple echo technology to penetrate ground vegetation and employs filtering algorithms to remove surface vegetation, obtaining true ground elevation data and drone LiDAR point clouds to identify dominant groundwater recharge pathways such as high-lying ground fissures, karst funnels, and sinkholes in karst areas.

[0040] The space-based detection module includes a point cloud data processing unit and a terrain modeling unit.

[0041] Point cloud data processing unit, used to remove abnormal points and noise points in the collected LiDAR data and convert the data into the target coordinate system; The terrain modeling unit generates a digital elevation model based on ground point data and a digital surface model in combination with non-ground point data for three-dimensional terrain analysis of the mining area.

[0042] The ground survey module combines high-density geophysical exploration, hydrogeological drilling, pumping tests and tracer tests to obtain the mining area's geological structure, strata, fracture network, and the spatial distribution of historical tailings piles, as well as the hydrogeological conditions around the mine. It analyzes the development and changes of primary and artificial fractures and karst in the roof and floor of the mining area, and explores the temporal and spatial distribution characteristics of the dominant groundwater channels.

[0043] The ground investigation module includes a data collection and organization unit and a test plan design and implementation unit.

[0044] The data collection and collation unit is used to collect geological and hydrogeological data of the survey area and verify the accuracy of the data through on-site surveys; The test program design and implementation unit obtains groundwater permeability and connectivity parameters based on high-density, hydrogeological drilling, pumping tests and tracer test programs.

[0045] The in-well detection module combines mine reconnaissance with 3D laser scanning technology to investigate the geological and hydrogeological conditions within mines, goafs, and caves, as well as the acid mine water inflow (water inflow layer, water volume, internal morphology, etc.). Using a 3D laser scanner to obtain 3D point cloud data from goafs and caves, the module quickly grasps the topography, distribution of dangerous rock formations, and roof fluctuations of caves. It also quickly obtains cross-sectional and longitudinal information about goafs, caves, and mine tunnels. This information is then integrated with the drone LiDAR point cloud from the space-based detection module to determine the positional relationship between surface and underground topography, establishing a surface and underground spatial correlation model. This effectively reduces the probability of deviation from the surface drilling position into mine tunnels, goafs, and caves, improving work efficiency and saving project costs.

[0046] The well detection module includes a scanning data processing unit and a model optimization unit.

[0047] Scanning data processing unit, used to import the original point cloud data collected by the 3D laser scanner into professional software and realize multi-site cloud stitching through control point registration; The model optimization unit uses a curvature-based point cloud simplification algorithm and a smoothing filter algorithm to optimize the 3D model surface and adjust the texture to enhance the visualization effect.

[0048] Example 2 This embodiment provides a method for ecological restoration and exploration of acid mine water inflow in karst areas. It uses an integrated "air-space-ground-well" technology to achieve precise positioning of hidden karst pipelines in karst areas through a progressive collaborative logic of air-based screening, space-based verification, ground testing, and well scanning. The specific implementation steps are as follows: S1. Airborne remote sensing image analysis Airborne remote sensing images provide regional (10-100 km²) macro information, quickly identifying pollution sources (such as tailings pile target areas) and surface water distribution, such as Figure 1 shown.

[0049] S101. Data Source Selection: Based on the geographic location, scope, and resolution requirements of your work area, select appropriate remote sensing satellite data sources, such as Landsat 8 and Sentinel-2, to obtain high-resolution multispectral imagery. For example, for large karst areas, Landsat 8 imagery can be selected, as it offers wide coverage and moderate resolution, meeting the needs of macroscopic analysis.

[0050] S102, Data Preprocessing: Download the required data from the data service platform or satellite data provider and perform preprocessing, including radiation correction, geometric correction, cloud removal, etc., to ensure the accuracy and availability of the data.

[0051] Radiometric Correction: Perform radiometric correction on the image to eliminate the effects of atmospheric and sensor noise. This step can be performed using professional remote sensing image processing software, such as ENVI or ERDAS Imagine, using an atmospheric correction model (such as FLAASH).

[0052] Geometric correction: Geometric correction is performed on the image to ensure the geometric accuracy of the image. This step can use ground control points (GCPs) to perform geometric correction to ensure the accuracy of the image's geographic coordinates.

[0053] Cloud removal: Use cloud masking algorithms to remove clouds and cloud shadows from images to improve image usability. For example, the SCL (Scene Classification Layer) product in Sentinel-2 imagery can be used to identify and remove clouds and cloud shadows.

[0054] S103. Image enhancement: Improve the clarity and interpretability of remote sensing images through contrast enhancement and sharpening, making the features of land features more distinct. For example, histogram equalization and Laplace sharpening can be used to enhance image contrast and detail.

[0055] S104. Terrain and water system identification: Utilize terrain shadows and texture features in remote sensing images, combined with digital elevation model (DEM) data, to identify terrain features of the work area, such as mountains, hills, and plains; identify water systems such as rivers, lakes, and reservoirs based on water reflection characteristics, color, and shape.

[0056] Terrain feature recognition: By combining terrain shadow and texture features with DEM data, terrain features such as mountains, hills, and plains can be identified. Figure 2 and Figure 3 .

[0057] Water system identification: Rivers, lakes, reservoirs and other water systems can be identified based on the water's reflective characteristics (such as low reflectivity), color (such as blue or black) and shape (such as linear or surface).

[0058] S105. Locking the tailings pile target area: Compare multiple historical images, analyze the dynamic changes of the tailings pile, further determine the location of the tailings pile, and lock the pollution source target area.

[0059] Figure 1 The map shows the topography and water distribution of the work area, with the distribution of multiple acid mine water inflow points, tailings piles, and surface water bodies marked with different colors and symbols. For example, blue diamonds indicate acid mine water inflow points, pink areas indicate tailings piles, and cyan marks indicate surface water bodies.

[0060] Figure 2 Middle A (DOM) shows the morphology and extent of sinkhole LSD01 on the surface. Its boundary is outlined by a red dotted line, and the surrounding vegetation and terrain are rich in information. Figure 2 Middle B (DEM) shows the depth and slope characteristics of the sinkhole through light and dark changes and contour lines.

[0061] Figure 3 Figure A (DOM) shows the direction and distribution of the ground fissure LF1 on the surface. The fissure boundary is marked by a red dotted line, clearly showing the terrain and vegetation type that the fissure passes through. Figure 3 Medium B (DEM) uses the light and dark and height changes of the three-dimensional terrain to show the depth, width and elevation difference of the ground fissures with the surrounding terrain in detail.

[0062] S2. UAV-mounted LiDAR data acquisition and processing The drone-mounted LiDAR focuses on the mesoscale (1-10 km²) and identifies hidden channels such as high-lying ground fissures and karst funnels with centimeter-level resolution.

[0063] S201, LiDAR data acquisition: Mount the LiDAR on the drone and set appropriate flight parameters such as flight altitude, speed, and route planning. During the flight, the LiDAR device emits laser pulses and receives reflected signals to generate point cloud data.

[0064] Plan the drone's flight altitude (generally 100-300 meters), speed (generally 5-10 meters per second), and flight path based on the terrain and vegetation distribution in the work area. For example, in areas with dense vegetation, the flight altitude can be lowered to improve data resolution.

[0065] Launch a drone to collect LiDAR data and generate high-precision point cloud data. For example, using a high-precision LiDAR device such as the RIEGL VUX-1 UAV can obtain point cloud data with centimeter-level resolution.

[0066] S202, data preprocessing: import the collected LiDAR data into point cloud data processing software, remove abnormal points, noise points, etc., and convert the data to the required coordinate system.

[0067] Use professional point cloud data processing software, such as CloudCompare and LAS Tools, to import the collected LiDAR data.

[0068] Remove outliers and noise points from the data through statistical analysis and filtering algorithms. For example, use voxel-based filtering methods to remove isolated points and abnormal elevation points.

[0069] Convert the data to the required coordinate system, such as WGS-84, UTM, etc., to ensure the data's geographic coordinates are accurate.

[0070] S203. Point cloud classification: Identify ground points and non-ground points through algorithms to provide data support for subsequent 3D modeling.

[0071] Use machine learning or deep learning-based classification algorithms, such as random forests and convolutional neural networks, to classify point cloud data. For example, you can use the PointNet++ algorithm to perform semantic segmentation on point clouds to distinguish between ground points and non-ground points.

[0072] Generate classified point cloud data, including ground point cloud and non-ground point cloud, to provide a basis for subsequent terrain modeling and 3D modeling.

[0073] S204, Terrain Modeling: Generate a Digital Elevation Model (DEM) based on ground point data for terrain analysis; generate a Digital Surface Model (DSM) based on non-ground point data for building height analysis, vegetation coverage analysis, etc.

[0074] Generate a DEM using ground point cloud data. For example, you can use Kriging interpolation or radial basis function interpolation to generate a high-precision DEM.

[0075] Generate a DSM using non-ground point cloud data. For example, the DSM can be generated using the Thiessen polygon method or the nearest neighbor interpolation method.

[0076] Based on DEM and DSM, slope, aspect, terrain profile and other analyses are carried out to provide support for subsequent geological analysis.

[0077] S205. 3D modeling: Generate a 3D model based on point cloud data of buildings, vegetation, and mining areas to visually display the topography and distribution of land features in the mining area.

[0078] Use 3D modeling software, such as 3D Max and Blender, to convert point cloud data into a 3D model. For example, you can use MeshLab to reconstruct the surface of the point cloud and generate a 3D mesh model. Optimize the generated 3D model by simplifying the model, smoothing the surface, and adjusting the texture to improve model visualization and data processing efficiency.

[0079] S206, Data Fusion: Fuse LiDAR data with other remote sensing data (such as satellite remote sensing, drone photogrammetry, etc.) to improve data accuracy and reliability.

[0080] Use data fusion algorithms, such as PCA (Principal Component Analysis) and waveform matching, to fuse LiDAR data with other remote sensing data. For example, you can fuse a DEM generated by LiDAR with satellite remote sensing imagery to improve the accuracy of terrain information.

[0081] Generate fused multi-source data, improve the data resolution and information volume, and provide more comprehensive data support for subsequent analysis.

[0082] S207. Data analysis: Utilize the integrated mining area model to analyze the parameters of advantageous groundwater recharge channels such as high-level and hidden ground fissures, karst funnels, and sinkholes in the karst area, providing a basis for subsequent surveys.

[0083] Extract geological features such as ground fissures, karst funnels, and sinkholes from the fused data. For example, hidden ground fissures and karst funnels can be identified by analyzing the differences between DEM and DSM.

[0084] Calculate parameters of geological features, such as length, width, depth, and volume, to assess their impact on groundwater flow. For example, the connectivity and permeability of ground fissures can be analyzed to determine their potential as groundwater recharge pathways.

[0085] Generate detailed analysis reports, including the distribution of geological features, parameters and impacts on groundwater flow, to provide a scientific basis for subsequent ecological restoration surveys.

[0086] Furthermore, accurate identification of hidden groundwater recharge channels in karst regions, such as high-lying ground fissures, karst funnels, and sinkholes, can provide precise targets for upstream recharge cutoffs. Cutting off groundwater recharge upstream reduces the generation of acid mine water inrush at the source, providing important technical support for ecological restoration of mine water inrush in karst regions.

[0087] S3. Ground Survey The ground survey targeted key areas (0.1-1 km²) to characterize the fracture network with meter-level accuracy.

[0088] S301. Data collection and collation: Collect geological and hydrogeological data of the survey area, including geological maps, hydrogeological maps, etc., to provide basic information for field investigations.

[0089] Collect relevant information from relevant departments, geological survey reports, academic literature, etc. For example, you can obtain geological maps and hydrogeological maps from the local land and resources department.

[0090] The collected data will be digitized and a database or GIS layer will be established to facilitate subsequent analysis and query.

[0091] S302. On-site investigation: Conduct an on-site investigation of the work area to verify the accuracy of the collected data, observe the geological structure, fracture distribution, karst development, etc., and record relevant geological phenomena.

[0092] Prepare necessary tools, such as compass, geological hammer, GPS equipment, etc., to ensure the smooth progress of the on-site survey.

[0093] Detailed observation of geological structures, such as faults and folds, as well as the distribution, density, direction, and dip of fractures. Also, record karst development, such as the location and size of caves and tiankengs.

[0094] Record geological phenomena observed on site and collect necessary rock and soil samples for subsequent laboratory analysis.

[0095] S303. Test plan determination: Based on the survey objectives and on-site conditions, determine the locations and plans for high-density geophysical exploration, hydrogeological drilling, pumping tests, tracer tests, etc. to obtain detailed hydrogeological parameters.

[0096] Determine the layout of survey lines and survey points, and select appropriate geophysical exploration methods, such as high-density resistivity method, seismic wave reflection method, etc.

[0097] Based on the geophysical exploration results, select the appropriate drilling location and determine the drilling depth and coring interval.

[0098] Design the well location, pumping volume and pumping time for the pumping test, and determine the water level observation points and observation frequency.

[0099] Select appropriate tracers and delivery points, and determine the arrangement of observation points and sampling frequency.

[0100] S304. Field investigation and testing: Conduct field investigation and various tests, including high-density geophysical prospecting, hydrogeological drilling, pumping tests, tracer tests, etc., to understand the geological and hydrogeological conditions, analyze the development and changes of primary and artificial fissures and karst in the roof and floor of the mining area, and explore the temporal and spatial distribution characteristics of the dominant groundwater channels.

[0101] Collect geophysical data according to the predetermined plan, record resistivity, seismic waves and other data, and analyze underground geological structures.

[0102] Conduct drilling operations, collect core samples, and record rock formation information and groundwater level changes.

[0103] Conduct pumping tests, monitor water level changes, and calculate parameters such as groundwater permeability coefficient.

[0104] Tracers are released to monitor their migration paths in groundwater and verify the connectivity of dominant groundwater channels.

[0105] S4. Well Detection In-well detection enables centimeter-level modeling of underground space at the microscopic scale (<100 m) and accurately locates the water-generating layer. Figure 4 A 3D laser modeling diagram is shown. The model uses varying colors and transparency to distinguish different areas and structures, visually demonstrating the complexity and connectivity of the underground space. This model is used to develop precise remediation plans, particularly providing critical support for grouting curtain placement and drilling path planning.

[0106] S401. Survey station planning: Plan survey station locations based on the internal structure of the mine to ensure that the scanning area is unobstructed and that there is sufficient overlap between adjacent survey stations to ensure data continuity and integrity.

[0107] Select appropriate measurement station locations based on the distribution of mine tunnels and caves. For example, set up measurement stations in the main tunnels and key caves of the mine to ensure coverage of all important geological structures.

[0108] Ensure that the scanning areas between adjacent measuring stations have sufficient overlap. The recommended overlap is set to 30%-50% to ensure the accuracy of data stitching.

[0109] S402. Control point acquisition: Use a total station or GPS to collect control point coordinates to ensure that the control points are evenly distributed and cover the entire scanning area, providing a basis for later data alignment.

[0110] Evenly distribute control points in the scanning area, for example, set one control point every 100 square meters to ensure that the entire scanning area is covered.

[0111] Use a total station or GPS device to accurately collect the coordinates of the control points to ensure the accuracy of the coordinate data.

[0112] S403. Equipment installation and fixation: Install the 3D laser scanner on a stable tripod to ensure that the equipment is stable and motionless during the scanning process; use a spirit level or electronic level to adjust the level of the equipment to ensure the correct instrument posture to obtain accurate scanning data.

[0113] S404. Equipment height measurement and calibration: Use a measuring rod or laser rangefinder to accurately measure the height from the center of the instrument to the ground mark, perform equipment calibration, and ensure the accuracy of the scanned data.

[0114] S405, Power-on Self-Test and System Initialization: Turn on the power and start the 3D laser scanner. Perform a self-test to automatically check system configuration, storage, and other information, confirm that all sensors are working properly, and ensure that the device is in good working condition.

[0115] S406, Parameter Settings and Mode Selection: Set the scanning resolution according to project requirements, such as 0.1° or 0.2°, to obtain point cloud data of varying accuracy. Select the appropriate scanning range and mode, such as panoramic or local scanning. Configure the data storage format (such as LAS or PLY) and storage path to ensure orderly data management and prepare for data collection.

[0116] S407, Data Collection and Quality Control: Start scanning, collect point cloud data, monitor data quality in real time, check point cloud density and integrity, and perform additional scans when necessary to ensure the collected data meets quality requirements. For areas with missing data or substandard quality, perform additional scans.

[0117] S408, Data import: Import the raw data collected by the scanner into professional 3D data processing software (such as CloudCompare, RIEGL RiSCAN PRO) to provide a basis for subsequent data processing and analysis.

[0118] S409, point cloud coarse registration: Perform preliminary registration of the scanned data from different sites automatically or manually to ensure the continuity and integrity of the data and provide initial data for accurate registration.

[0119] S410, precise point cloud registration: By inputting the coordinates of registration markers such as the registration target ball and reflector, the overall coordinate system of the point cloud is established to improve the registration accuracy and ensure the accuracy of the data.

[0120] S411, Point Cloud Cleaning: Use filtering algorithms to remove unnecessary noise, background, or human shadows to ensure a clean and accurate point cloud. The cleaning process includes removing redundant data and filling in missing areas to improve data quality and usability. For missing data areas, data is filled in to ensure the integrity of the point cloud.

[0121] S412, Point cloud modeling: Convert point cloud data into 3D models as needed, e.g. Figure 4 The three-dimensional model shown intuitively displays the internal structure and morphology of the mine, goaf and cave.

[0122] S413, Data Optimization: Further optimize the point cloud or model, including simplifying the point cloud density, smoothing the model surface, adjusting the color and texture, etc., to improve the model visualization effect and data processing efficiency.

[0123] S414, Data Output: Export the final processed point cloud data or 3D model to the required format, such as .PLY, .LAS, .E57, .OBJ, .FBX, etc., for subsequent analysis, display or engineering application.

[0124] S5. Generate a pollution source migration path analysis report based on multi-source fusion data, and determine the grouting curtain layout and drilling path through spatial analysis of the three-dimensional model to reduce treatment costs.

[0125] Data fusion: Fuse airborne remote sensing data, space-based LiDAR data, ground survey data, and in-well detection data to build a unified multi-source database to enable data sharing and collaborative analysis.

[0126] Construction of a four-dimensional hydrogeological model: Integrating multi-dimensional data such as time (historical evolution), space (surface-underground), and physical properties (electricity / permeability), a four-dimensional hydrogeological model is constructed to comprehensively display the hydrogeological characteristics and dynamic changes of the mining area.

[0127] Pollution source migration path analysis: Based on the four-dimensional hydrogeological model, the migration path and diffusion trend of pollution sources are analyzed to provide a scientific basis for the formulation of treatment plans.

[0128] Optimization of treatment plans: Based on the results of the pollution source migration path analysis and combined with the three-dimensional spatial model, the grouting curtain layout location and drilling path are determined to avoid full-area coverage treatment, shorten the treatment period, and reduce treatment costs.

[0129] This embodiment uses the integrated "air-space-ground-well" technology to form a progressive exploration logic of "regional screening → key area intensification → target point detailed investigation", avoiding the waste of resources of the traditional method of "looking for a needle in a haystack", and realizing the efficient, accurate and scientific ecological restoration exploration of acid mine water inrush in karst areas, providing strong technical support for mine ecological restoration.

[0130] The "air-space-ground-well" integrated technology of the present invention significantly shortens the survey cycle and increases the speed of locking the pollution source. Specifically, this technology shortens the time required by traditional methods from several months or even years to several weeks, and the survey cycle is shortened by 40%. At the same time, through the fusion and analysis of multi-source data, the location of the pollution source can be quickly and accurately identified, and the locking speed is increased by 60%. For example, in the satellite remote sensing stage, the use of high-resolution images and multi-period image comparison can quickly identify the terrain, water system and tailings pile target areas, and combined with the dynamic backtracking of historical images, the location of the pollution source can be quickly determined. The introduction of UAV LiDAR further accelerates the data acquisition process. Its centimeter-level resolution can quickly identify geological features such as hidden ground fissures, sinkholes and karst funnels, providing precise guidance for subsequent ground surveys and well detection, avoiding the "needle in a haystack" search in traditional methods, and greatly improving work efficiency.

[0131] This integrated technology achieves over 90% accuracy in identifying dominant channels for hidden karst groundwater recharge, while reducing the borehole deviation rate to 5%. By integrating multi-dimensional data from satellite remote sensing, drone LiDAR, ground geophysical exploration, and in-well exploration, a high-precision 3D geological model is constructed. For example, satellite remote sensing provides macroscopic topographic and water system information, drone LiDAR penetrates vegetation to obtain ground elevation data, ground geophysical exploration and drilling provide detailed geological structures and hydrogeological parameters, and in-well exploration provides precise 3D point cloud data of the underground space. This fusion of data enables more accurate identification of hidden karst conduits, clearly depicting their location, morphology, and connectivity. During drilling operations, the high-precision 3D model enables precise planning of drilling paths, effectively reducing drilling deviation rates and ensuring accurate target drilling, thereby improving the relevance and effectiveness of remediation measures.

[0132] This technology optimizes resource allocation by reducing repetitive survey steps, resulting in a 50% reduction in overall costs. Traditional survey methods often require multiple trips back and forth between different survey stages, involving a significant amount of repetitive work, such as multiple ground surveys and drilling, resulting in a waste of manpower, material resources, and time. Integrated technology, however, utilizes multi-source data to obtain comprehensive geological information in one go, avoiding repeated surveys. For example, during the satellite remote sensing and drone LiDAR phases, most topographic, geological, and hydrological information can be acquired, providing precise guidance for subsequent ground surveys and in-well detection, reducing unnecessary field work. Furthermore, high-precision 3D models and data analysis provide a scientific basis for the development of remediation plans, avoiding over-engineering and waste of resources, and further reducing costs.

[0133] The integrated "air-space-ground-well" technology provides comprehensive surface-to-subsurface data, covering everything from macro-regional topography and surface water systems to micro-level information on underground fissures and caves. Satellite remote sensing provides fundamental information on large-scale topography, surface water systems, and historical tailings piles. UAV LiDAR identifies mesoscale geological features such as hidden ground fissures, karst funnels, and sinkholes. Ground surveys, using high-density geophysical exploration, hydrogeological drilling, pumping tests, and tracer tests, reveal the spatial and temporal distribution of the mining area's geological structure, fracture networks, and dominant groundwater pathways. In-well exploration, using 3D laser scanning technology, generates detailed 3D point cloud data of goafs and cave interiors. This data, integrated with space-based exploration data, establishes a spatial correlation model between the surface and underground. This multi-dimensional, comprehensive data provides a scientific and comprehensive basis for the development of remediation plans, making remediation measures more precise and effective, and enhancing the scientific nature and feasibility of the entire ecological restoration project.

[0134] In the description of the present invention, it should be understood that "-" and "~" represent a range between two values, and the range includes the endpoints. For example, "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0135] In the description of the present invention, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0136] In the description of the invention, the numerical values of time, temperature, ratio and mass involved may be based on actual measurements, standard parameters of equipment, simplified rounding results, or within an acceptable error range, ensuring the practicality and repeatability of the invention.

[0137] In the description of the present invention, the term "about" or "approximately" is used to express the approximate value of a numerical value or range, allowing a certain error to ensure the flexibility and practicality of the description while remaining within an acceptable error range, with the maximum error range not exceeding 10% of the corresponding numerical value or numerical range.

[0138] The above are merely preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be construed as limiting the present invention, and the scope of protection of the present invention should be determined by the scope defined in the claims. Persons skilled in the art will appreciate that improvements and modifications may be made without departing from the spirit and scope of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A karst area acid mine water ecological restoration and exploration system, characterized by: Adopting the integrated "air-space-ground-well" technology, through the progressive collaborative logic of air-based screening, space-based verification, ground testing, and well scanning, the system can accurately locate hidden karst pipelines in karst areas. It includes the following modules: (a) Airborne remote sensing module, used to identify regional topography, surface water systems, and historical tailings piles using satellite remote sensing imagery, and to identify pollution source targets through dynamic analysis of multiple historical images; (b) Space-based detection module, equipped with drone-mounted LiDAR, uses multiple echo technology and filtering algorithms to penetrate vegetation interference, obtain real ground elevation data and drone LiDAR point cloud, and identify high-level ground fissures, karst funnels, and sinkholes in karst areas; (c) Ground survey module, combining high-density geophysical exploration, hydrogeological drilling, pumping tests, and tracer tests to obtain the temporal and spatial distribution characteristics of the mining area's geological structure, fracture network, and dominant groundwater pathways; (d) The in-well detection module acquires 3D point cloud data of goafs and caves through mine investigation and 3D laser scanning technology, and fuses it with the UAV LiDAR point cloud of the space-based detection module to establish a ground and underground space correlation model.

2. The survey system according to claim 1, characterized in that: The air-based remote sensing module includes: Image pre-processing unit, used to perform radiation correction, geometric correction and cloud removal on satellite remote sensing images to improve image quality; The pollution source dynamic analysis unit compares multiple historical images, extracts the three-dimensional morphological parameters of the tailings pile based on the digital elevation model, analyzes its area, volume and sedimentation characteristics, and accurately locates the pollution source target area.

3. The survey system according to claim 1, characterized in that The space-based detection module includes: Point cloud data processing unit, used to remove abnormal points and noise points in the collected LiDAR data and convert the data into the target coordinate system; The terrain modeling unit generates a digital elevation model based on ground point data and a digital surface model in combination with non-ground point data for three-dimensional terrain analysis of the mining area.

4. The survey system according to claim 1, characterized in that: The ground survey module includes: The data collection and collation unit is used to collect geological and hydrogeological data of the survey area and verify the accuracy of the data through on-site surveys; The test program design and implementation unit obtains groundwater permeability and connectivity parameters based on high-density, hydrogeological drilling, pumping tests and tracer test programs.

5. The survey system according to claim 1, characterized in that: The well detection module includes: Scanning data processing unit, used to import the original point cloud data collected by the 3D laser scanner into professional software and realize multi-site cloud stitching through control point registration; The model optimization unit uses a curvature-based point cloud simplification algorithm and a smoothing filter algorithm to optimize the 3D model surface and adjust the texture to enhance the visualization effect.

6. A method for ecological restoration and exploration of acid mine water inflow in karst areas, characterized in that: The following steps are involved: S1. Through satellite remote sensing image analysis, identify the terrain, water system and tailings pile target area of the work area based on remote sensing image interpretation algorithms, and determine the location of the pollution source by combining dynamic backtracking of multiple historical images; S2. Use UAV-mounted LiDAR to obtain high-precision terrain data and UAV LiDAR point clouds, remove vegetation interference through filtering algorithms, and identify hidden ground fissures, sinkholes, and karst funnels; S3. Conduct high-density ground geophysical exploration, hydrogeological drilling, and tracer testing to verify the connectivity of dominant groundwater channels and obtain hydrogeological parameters; S4. Obtain point cloud data of the goaf and karst caves through 3D laser scanning of the mine, and perform control point registration and spatial alignment with the drone LiDAR point cloud of the space-based detection module to establish a 3D spatial model of the ground and underground space in a unified coordinate system; S5. Generate a pollution source migration path analysis report based on multi-source fusion data, and determine the grouting curtain layout and drilling path through spatial analysis of the three-dimensional model to reduce treatment costs.

7. The method according to claim 6, characterized in that The satellite remote sensing image analysis in step S1 includes: By comparing remote sensing images from different time periods, combined with terrain shadow analysis and DEM three-dimensional morphology extraction algorithm, the volume changes of tailings piles are quantified and the migration trend of pollution sources is determined.

8. The method according to claim 6, characterized in that The filtering algorithm in step S2 is specifically: Based on multiple LiDAR echo signals, an adaptive threshold segmentation technique is used to distinguish vegetation from ground points, and the point cloud data is corrected through an iterative nearest point algorithm to generate a bare ground elevation model.

9. The method according to claim 6, characterized in that The control point registration in step S4 is specifically as follows: The coordinates of ground control points are collected by total station or GPS, and the 3D laser scanning point cloud in the mine is spatially aligned with the UAV LiDAR point cloud, with the error controlled within ±0.1m.

10. The method according to claim 6, characterized in that The method for determining the grouting curtain placement position in step S5 is: Based on the analysis results of the direction of the cave pipeline and the pollution migration path in the three-dimensional spatial model, a grouting curtain was laid downstream of the pipeline to avoid full-area coverage treatment and shorten the treatment period.

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