A karst area acid mine water gushing ecological restoration survey system and method

Abstract

The application discloses a kind of karst region acid mine gushing water ecological restoration survey system and method, including air-based remote sensing module, space-based detection module, ground investigation module and in-hole detection module, adopt "air-space-ground-well" integrated technology, through air-based screening, space-based verification, ground test, in-hole scanning progressive type collaborative logic, realize the accurate positioning of hidden karst conduit in karst region.The application realizes the accurate positioning of hidden karst conduit in karst region by "air-space-ground-well" integrated technology, significantly improves the survey efficiency and accuracy, reduces the cost.Compared with prior art, the application can quickly lock the target area of pollution source, accurately identify the groundwater dominant channel, establish the correlation model of ground and underground space, provide more efficient and more scientific survey means for karst region acid mine gushing water ecological restoration, effectively promote the precision and efficiency of mine ecological restoration work.

Description

Technical Field

[0001] This invention belongs to the field of mine ecological restoration technology, and relates to an exploration system for ecological restoration of acid mine water inflow in karst areas, as well as a method for ecological restoration of acid mine water inflow in karst areas. Background Technology

[0002] Mine ecological restoration is an important task in practicing ecological civilization construction, and the treatment of acid mine water (AMD) is particularly critical. AMD is characterized by low pH and high heavy metal content, posing a serious threat to the fragile hydrogeological environment and ecosystem of karst regions. Traditional AMD treatment methods mainly rely on source control (such as blocking groundwater recharge) and end-of-pipe treatment (such as chemical neutralization). However, in historical legacy mines in karst regions, due to the development of karst landforms, interwoven mine tunnels, concealed pollution sources, and complex underground spatial forms, traditional technologies face severe challenges.

[0003] In the process of realizing this invention, the inventors discovered that at least one of the following technical problems exists in the prior art: a) Traditional hydrogeological surveys rely on single technical means (such as surface drilling and geophysical exploration), which makes it difficult to integrate surface and subsurface, macro and micro data, resulting in long exploration 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-level ground fissures, karst funnels, and other dominant groundwater recharge channels; c) Manual reconnaissance of mines is time-consuming and carries high safety risks. The lack of coordination between 3D laser scanning and surface data leads to large borehole positioning deviations and a lack of spatial correlation support for remediation plans. d) Existing technologies lack dynamic retrospective analysis of multi-period remote sensing images and cross-validation mechanisms for multi-source data, making it difficult to pinpoint the migration path of pollution sources and easily leading to delayed or overly engineered remediation measures.

[0004] The aforementioned problems severely restrict the efficiency and effectiveness of AMD remediation in karst regions. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide an innovative technical system that integrates multi-dimensional data, penetrates complex surface disturbances, and realizes ground-subsurface collaborative modeling, so as to provide precise support for ecological restoration.

[0006] Through long-term exploration and experimentation, and continuous reform and innovation, the inventors have developed a technical solution to address the aforementioned technical problems. This invention provides a system for ecological restoration of acidic mine water inrush in karst areas. It employs an integrated "air-space-ground-well" technology, using a progressive collaborative logic of air-based screening, space-based verification, ground testing, and well scanning to accurately locate hidden karst conduits in karst areas. The system includes the following modules: (a) The space-based remote sensing module is used to identify regional topography, surface water systems and historical tailings piles through satellite remote sensing images, and to locate pollution source target areas through dynamic analysis of multiple historical images; (b) Space-based detection module, equipped with UAV-borne lidar, uses multiple echo technology and filtering algorithms to penetrate vegetation interference, obtain real ground elevation data and UAV LiDAR point cloud, and identify high-level ground fissures, karst sinkholes and sinkholes in karst areas. (c) Ground survey module, which combines high-density geophysical exploration, hydrogeological drilling, pumping tests and tracer tests to obtain the spatiotemporal distribution characteristics of geological structure, fracture network and dominant groundwater channels in the mining area; (d) The well-in-the-hole detection module acquires three-dimensional point cloud data of the goaf and the interior of the karst cave through mine reconnaissance and three-dimensional laser scanning technology, and merges it with the UAV LiDAR point cloud of the space-based detection module to establish a ground-to-underground space correlation model.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention utilizes an integrated "air-space-ground-well" technology to achieve precise location of hidden karst conduits in karst regions, significantly improving exploration efficiency and accuracy while reducing costs. Compared to existing technologies, this invention can quickly pinpoint pollution source target areas, accurately identify dominant groundwater channels, and establish a correlation model between surface and underground spaces. It provides a more efficient and scientific exploration method for the ecological restoration of acid mine water inflows in karst regions, powerfully promoting the precision and efficiency of mine ecological restoration work.

[0008] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the space-based remote sensing module includes: The image preprocessing unit is used to perform radiometric correction, geometric correction, and cloud removal on satellite remote sensing images to improve image quality. The pollution source dynamic analysis unit, by comparing multiple historical images, extracts the three-dimensional morphological parameters of tailings piles based on digital elevation models, analyzes their area, volume and sedimentation characteristics, and accurately locates the target area of ​​pollution sources.

[0009] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: The image preprocessing unit effectively improved the quality of satellite remote sensing images, providing a more accurate data foundation for subsequent analysis. The pollution source dynamic analysis unit, through comparison of multiple historical images and digital elevation models, can accurately extract the three-dimensional morphological parameters of tailings piles and analyze their area, volume, and sedimentation characteristics in detail, thereby more accurately locating pollution source target areas. This significantly improves the accuracy and efficiency of pollution source identification and provides more reliable and detailed information support for the ecological restoration exploration of acid mine water inflow in karst areas.

[0010] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the space-based detection module includes: The point cloud data processing unit is used to remove outliers and noise points from the acquired LiDAR data and transform the data to the target coordinate system. The terrain modeling unit generates a digital elevation model based on ground point data and combines it with non-ground point data to generate a digital surface model for three-dimensional terrain analysis in mining areas.

[0011] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: The point cloud data processing unit effectively removes outliers and noise points from LiDAR data and transforms them to the target coordinate system, improving the accuracy and usability of the data. The terrain modeling unit generates digital elevation models and digital surface models, fully supporting the three-dimensional terrain analysis of the mining area, enhancing the understanding and grasp of the terrain and landforms of the mining area, providing more refined and scientific data support for subsequent ecological restoration exploration, and improving the efficiency and quality of exploration.

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

[0013] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: The data collection and processing unit systematically integrates geological and hydrogeological data of the survey area and verifies its accuracy through on-site reconnaissance, laying a solid foundation for subsequent work. The experimental design and implementation unit obtains key hydrogeological parameters, such as groundwater permeability and connectivity parameters, through various experimental methods. These data are crucial for understanding groundwater flow patterns and pollution migration pathways. Overall, this improvement significantly enhances the systematicness and scientific rigor of the surface survey, strengthens the control over the underground geological conditions of the mining area, provides reliable data support for accurately locating hidden karst conduits and developing effective ecological restoration plans, and effectively improves the efficiency and quality of exploration work.

[0014] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the well-drilling detection module includes: The scanning data processing unit is used to import the raw point cloud data collected by the 3D laser scanner into professional software and realize multi-point point cloud stitching through control point registration. The model optimization unit uses a curvature-based point cloud simplification algorithm and a smoothing filtering algorithm to optimize the surface of the 3D model and adjusts the texture to enhance the visualization effect.

[0015] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: The scanning data processing unit effectively solved the problems of stitching and registration of raw point cloud data, improving data integrity and accuracy. The model optimization unit optimized the 3D model through advanced algorithms, not only improving the surface quality of the model but also enhancing its visualization effect, making the underground space structure clearer and more intuitive. These improvements significantly improved the efficiency and accuracy of well exploration, providing more reliable and detailed underground space data support for the ecological restoration of acid mine water inflow in karst areas, and helping to formulate more scientific and reasonable remediation plans.

[0016] This invention also provides a method for ecological restoration exploration of acid mine water inrush in karst areas, comprising the following steps: S1. By analyzing satellite remote sensing images, the topography, water system and tailings pile target area of ​​the work area are identified based on remote sensing image interpretation algorithms, and the location of pollution sources is determined by dynamic retrospective analysis of multiple historical images. S2. Use UAV-borne LiDAR to acquire high-precision terrain data and UAV LiDAR point cloud, 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 tracing tests to verify the connectivity of dominant groundwater channels and obtain hydrogeological parameters. S4. Obtain point cloud data of the goaf and karst cave through three-dimensional laser scanning of the mine, and perform control point registration and spatial alignment with the UAV LiDAR point cloud of the space-based detection module to establish a three-dimensional spatial model of the ground and underground under 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 location and drilling path through spatial analysis of the three-dimensional model to reduce treatment costs.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: The integrated application of "air-space-ground-well" technology has enabled comprehensive exploration from macro to micro levels and from the surface to the subsurface, significantly improving exploration efficiency and accuracy while reducing costs. Specifically, satellite remote sensing image analysis can quickly identify terrain, water systems, and tailings pile target areas, and dynamic retrospective analysis of multi-period historical images accurately determines the location of pollution sources; high-precision terrain data and point clouds acquired by UAV-borne LiDAR effectively remove vegetation interference and clearly identify hidden ground fissures, sinkholes, and karst funnels; high-density ground geophysical exploration, hydrogeological drilling, and tracer experiments further verify the connectivity of dominant groundwater channels and obtain key hydrogeological parameters; the fusion of mine 3D laser scanning and space-based detection data has established a unified coordinate system for a 3D spatial model of the ground and subsurface, providing a scientific basis for pollution source migration path analysis and remediation scheme optimization. Finally, spatial analysis of the 3D model determines the location of the grouting curtain and the drilling path, significantly reducing remediation costs and improving the scientific and economic efficiency of ecological restoration.

[0018] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the satellite remote sensing image analysis described in step S1 includes: By comparing remote sensing images from different time periods, combined with topographic shadow analysis and DEM three-dimensional morphology extraction algorithms, the volume change of tailings piles is quantified, and the migration trend of pollution sources is determined.

[0019] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: By comparing remote sensing images from different time periods, combined with topographic shadow analysis and DEM 3D morphology extraction algorithms, the volume changes of tailings piles can be quantified more accurately, and the migration trend of pollution sources can be clearly determined. This improvement not only enhances the accuracy of pollution source location but also provides a more scientific and dynamic basis for subsequent ecological restoration and remediation plans, helping to achieve precise governance and rational resource allocation, thereby improving the efficiency and effectiveness of the entire ecological restoration project.

[0020] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the filtering algorithm described in step S2 is as follows: 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 by an iterative nearest point algorithm to generate a bare land elevation model.

[0021] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: The filtering algorithm, based on multiple LiDAR echo signals, effectively distinguishes vegetation from ground points using adaptive threshold segmentation technology and precisely corrects point cloud data through an iterative nearest-point algorithm, thereby generating a high-precision bare land elevation model. This improvement significantly enhances the accuracy and reliability of topographic data, enabling clearer identification of geological features such as hidden ground fissures, sinkholes, and karst cones. This provides more accurate topographic information for subsequent ecological restoration surveys, enhancing the scientific validity and feasibility of remediation plans.

[0022] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the control point registration described in step S4 specifically involves: By collecting the coordinates of ground control points using a total station or GPS, the 3D laser scanning point cloud inside the mine is spatially aligned with the UAV LiDAR point cloud, with the error controlled within ±0.1m.

[0023] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: By employing a high-precision control point registration method, the accuracy of the ground-to-underground spatial correlation model was significantly improved, ensuring the reliability of the three-dimensional spatial model. This resulted in a more accurate determination of the relative positions of underground caves, mining subsidence areas, and surface topography, providing a more scientific basis for subsequent remediation projects.

[0024] Based on the above technical solution, the present invention can be further improved as follows: Furthermore, the method for determining the location of the grouting curtain in step S5 is as follows: Based on the analysis results of the karst cave pipeline route and pollution migration path in the three-dimensional spatial model, a grouting curtain is set up downstream of the pipeline to avoid full-area coverage treatment and shorten the treatment period.

[0025] Compared with the existing technology, the beneficial effects of adopting the above-mentioned further technical solution are as follows: This precise positioning method not only reduces unnecessary engineering work and resource waste, but also improves the pertinence and effectiveness of governance measures, thereby more efficiently blocking the diffusion path of pollution sources and accelerating the ecological restoration process of acid mine water inflow in karst areas. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a pollution source distribution map from an ecological restoration survey of acid mine water inflow in a karst region.

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

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

[0030] Figure 4 It is a 3D laser modeling diagram. Detailed Implementation

[0031] The following description is based on specific embodiments.

[0032] To make the objectives, 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 conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort 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 claimed invention, but merely to represent selected embodiments of the present invention.

[0033] In this invention, unless otherwise specified, all equipment and raw materials are available from the market or commonly used in the industry. Unless otherwise specified, the methods in the following embodiments are conventional methods in the art.

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

[0035] This embodiment describes an ecological restoration exploration system for acid mine water inrush in karst areas. It employs an integrated "air-space-ground-well" technology, utilizing a progressive collaborative logic of airborne screening, spaceborne verification, ground testing, and well-level scanning to achieve precise location of hidden karst conduits in karst regions. The system includes an airborne remote sensing module, a spaceborne detection module, a ground-level survey module, and a well-level detection module.

[0036] The space-based remote sensing module is used to identify basic information such as regional topography, surface water systems, natural geography, and hydrogeological conditions through satellite remote sensing imagery. Simultaneously, it uses remote sensing imagery to screen for pollution sources such as historical tailings piles within the work area. By retrospectively investigating the dynamic changes in historical images of suspected tailings piles, it further pinpoints the tailings piles.

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

[0038] The image preprocessing unit is used to perform radiometric correction, geometric correction, and cloud removal on satellite remote sensing images to improve image quality. The pollution source dynamic analysis unit, by comparing multiple historical images, extracts the three-dimensional morphological parameters of tailings piles based on digital elevation models, analyzes their area, volume and sedimentation characteristics, and accurately locates the target area of ​​pollution sources.

[0039] The space-based detection module, equipped with an UAV-borne LiDAR, acquires the three-dimensional surface coordinates of the observation area, providing high-resolution, high-precision topographic images. Through multiple echo technology to penetrate ground vegetation, and using filtering algorithms to remove surface vegetation, it obtains true ground elevation data and UAV LiDAR point clouds, identifying dominant groundwater recharge channels such as high-level ground fissures, karst sinkholes, and sinkholes in karst regions.

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

[0041] The point cloud data processing unit is used to remove outliers and noise points from the acquired LiDAR data and transform the data to the target coordinate system. The terrain modeling unit generates a digital elevation model based on ground point data and combines it with non-ground point data to generate a digital surface model for three-dimensional terrain analysis in mining areas.

[0042] The ground survey module, combined with high-density geophysical exploration, hydrogeological drilling, pumping tests and tracer tests, obtains information on the geological structure, strata, fracture network and spatial distribution of historical tailings piles in the mining area, hydrogeological conditions around the mine, analyzes the development and variation of primary and anthropogenic fractures and karst in the roof and floor of the mining area, and explores the spatiotemporal distribution characteristics of dominant groundwater channels.

[0043] The ground survey module includes a data collection and processing unit and an experimental design and implementation unit.

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

[0045] The well-in-ground detection module combines mine reconnaissance with 3D laser scanning technology. It investigates the geological and hydrogeological conditions of mines, goafs, and karst caves, as well as the water inflow situation in acidic mines (water inflow strata, water volume, internal morphology, etc.). Using a 3D laser scanner, it acquires 3D point cloud data of the goaf and karst cave interiors, quickly understanding the cave topography, distribution of unstable rocks, and cave ceiling undulations. It rapidly obtains cross-sectional and longitudinal information of goafs, karst caves, and mines, and fuses this data with the UAV LiDAR point cloud from the space-based detection module to obtain the positional relationship between surface and underground topography, establishing a surface-to-underground spatial correlation model. This effectively reduces the probability of deviation from the drilling location in mine roadways, goafs, and karst caves, improving work efficiency and saving project costs.

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

[0047] The scanning data processing unit is used to import the raw point cloud data collected by the 3D laser scanner into professional software and realize multi-point point cloud stitching through control point registration. The model optimization unit uses a curvature-based point cloud simplification algorithm and a smoothing filtering algorithm to optimize the surface of the 3D model and adjusts the texture to enhance the visualization effect.

[0048] Example 2 This embodiment provides a method for ecological restoration exploration of acidic mine water inflow in karst areas. It employs an integrated "air-space-ground-well" technology, using a progressive collaborative logic of airborne screening, space-based verification, ground testing, and well scanning to achieve precise location of hidden karst conduits in karst areas. The specific implementation steps are as follows: S1, Space-based Remote Sensing Image Analysis Airborne remote sensing imagery provides regional (10-100 km²) macroscopic information, rapidly identifying pollution sources (such as tailings pile target areas) and the distribution of surface water systems, such as... Figure 1 As shown.

[0049] S101. Data Source Selection: Based on the geographical location, extent, and resolution requirements of the work area, select a suitable remote sensing satellite data source, such as Landsat 8 or Sentinel-2, to obtain high-resolution multispectral imagery. For example, for large karst areas, Landsat 8 imagery can be selected, as it has 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 radiometric correction, geometric correction, cloud removal, etc., to ensure the accuracy and usability of the data.

[0051] Radiometric correction: Radiometric correction is performed on the image to eliminate the effects of atmospheric and sensor noise. This step can be achieved using specialized remote sensing image processing software, such as ENVI or ERDAS Imagine, through atmospheric correction models (such as FLAASH).

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

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

[0054] S103. Image Enhancement Processing: Through contrast enhancement, sharpening, and other processing, the clarity and interpretability of remote sensing images are improved, making ground features more prominent. For example, histogram equalization and Laplacian sharpening can be used to enhance image contrast and detail.

[0055] S104. Topography and Water System Identification: Using the topographic shadows and texture features in remote sensing images, combined with digital elevation model (DEM) data, identify the topographic features of the work area, such as mountains, hills, and plains; and identify water systems such as rivers, lakes, and reservoirs based on the water body reflection characteristics, color, and shape.

[0056] Terrain Feature Recognition: By combining terrain shadows and texture features with DEM data, terrain features such as mountains, hills, and plains can be identified. See also Figure 2 and Figure 3 .

[0057] Water system identification: Based on the water body's reflectivity (such as low reflectivity), color (such as blue or black), and shape (such as linear or planar), water systems such as rivers, lakes, and reservoirs can be identified.

[0058] S105. Tailings pile target area identification: By comparing historical images from multiple periods and analyzing the dynamic changes of the tailings pile, the location of the tailings pile can be further determined, and the pollution source target area can be identified.

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

[0060] Figure 2 The DOM (Domain A) shows the shape and extent of the sinkhole LSD01 on the surface, with its boundaries outlined by red dotted lines, and rich information on the surrounding vegetation and topography. Figure 2 The DEM (Digital Image Center B) shows the depth and slope characteristics of the sinkhole through variations in light and shadow and contour lines.

[0061] Figure 3 The middle section (DOM) shows the direction and distribution of ground fissure LF1 on the surface, with the fissure boundaries marked by red dashed lines, clearly showing the terrain and vegetation types that the fissure traverses. Figure 3 The DEM (Digital Elevation Model) in the middle B (Digital Elevation Model) uses the light and shadow and elevation changes of the three-dimensional terrain to present in detail the depth, width and elevation difference of the ground fissures with the surrounding terrain.

[0062] S2, UAV-borne LiDAR data acquisition and processing By using UAV-borne LiDAR to focus on the mesoscale (1-10 km²), hidden channels such as high-level ground fissures and karst sinkholes can be identified with centimeter-level resolution.

[0063] S201, LiDAR Data Acquisition: The LiDAR is mounted on the UAV, and appropriate parameters such as flight altitude, speed and flight path are set. During the flight, the LiDAR device emits laser pulses and receives reflected signals to generate point cloud data.

[0064] Based on the terrain and vegetation distribution of the work area, plan the UAV's flight altitude (generally 100-300 meters), speed (generally 5-10 meters per second), and flight path. For example, in densely vegetated areas, the flight altitude can be appropriately reduced to improve data resolution.

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

[0066] S202. Data preprocessing: Import the collected LiDAR data into point cloud data processing software, remove outliers and noise points, 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] Outliers and noise points in the data are removed through statistical analysis and filtering algorithms. For example, a voxel-based filtering method is used to remove isolated points and points with abnormal elevations.

[0069] Convert the data to the required coordinate system, such as WGS-84 or UTM, to ensure the accuracy of the data's geographic coordinates.

[0070] S203, Point Cloud Classification: This algorithm identifies ground points and non-ground points to provide data support for subsequent 3D modeling.

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

[0072] The system generates classified point cloud data, including ground point clouds and non-ground point clouds, which provides a foundation for subsequent terrain modeling and 3D modeling.

[0073] S204. Terrain Modeling: Generate a digital elevation model (DEM) from ground point data for terrain analysis; generate a digital surface model (DSM) from non-ground point data for building height analysis, vegetation cover analysis, etc.

[0074] DEMs can be generated using ground point cloud data. For example, high-precision DEMs can be generated using Kriging interpolation or radial basis function interpolation.

[0075] DSMs can be generated using non-terrestrial point cloud data. For example, the Thiessen polygon method or nearest neighbor interpolation method can be used to generate DSMs.

[0076] Based on DEM and DSM, slope, aspect, and topographic profile analyses are performed to support subsequent geological analyses.

[0077] S205. 3D Modeling: Generate a 3D model based on point cloud data of buildings, vegetation, and mining areas to intuitively display the topography and landforms of the mining area.

[0078] Use 3D modeling software, such as 3D Max and Blender, to convert point cloud data into 3D models. For example, MeshLab can be used to reconstruct the surface of point clouds and generate 3D mesh models. The generated 3D models are then optimized, including simplifying the model, smoothing surfaces, and adjusting textures, to improve the model's visualization and data processing efficiency.

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

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

[0081] The generated multi-source data improves the resolution and information content of the data, providing more comprehensive data support for subsequent analysis.

[0082] S207. Data Analysis: Using the integrated mining area model, analyze the parameters of dominant groundwater recharge channels such as high-level, concealed ground fissures, karst sinkholes, and sinkholes in karst areas to provide a basis for subsequent exploration.

[0083] Geological features, such as ground fissures, karst sinkholes, and sinkholes, can be extracted from the fused data. For example, hidden ground fissures and karst sinkholes can be identified by analyzing the differences between the 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, analyze the connectivity and permeability of ground fissures to determine their potential as groundwater recharge channels.

[0085] Detailed analysis reports are generated, including the distribution and parameters of geological features and their impact on groundwater flow, providing a scientific basis for subsequent ecological restoration investigations.

[0086] Furthermore, accurately identifying hidden groundwater recharge channels in karst regions, such as high-level ground fissures, karst sinkholes, and sinkholes, can provide precise targets for cutting off upstream recharge. Cutting off groundwater recharge upstream reduces the generation of acidic mine water inflows at the source, providing important technical support for the ecological restoration of mine water inflows in karst regions.

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

[0088] S301. Data Collection and Organization: Collect geological and hydrogeological data of the survey area, including geological maps and hydrogeological maps, to provide basic information for field investigation.

[0089] Collect relevant data from relevant departments, geological survey reports, academic literature, and other sources. For example, geological maps and hydrogeological maps can be obtained from the local land and resources department.

[0090] The collected data is digitized to create a database or GIS layer, which facilitates subsequent analysis and querying.

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

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

[0093] Observe the geological structures in detail, such as faults and folds, as well as the distribution, density, orientation, and dip angle of fissures. At the same time, record the development of karst, such as the location and scale of caves and sinkholes.

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

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

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

[0097] Based on the geophysical exploration results, select a suitable drilling location and determine the drilling depth and core sampling 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 layout of observation points and sampling frequency.

[0100] S304. Field Investigation and Testing: Conduct field investigations and various tests, including high-density geophysical exploration, hydrogeological drilling, pumping tests, and tracer tests, to understand the geological and hydrogeological conditions, analyze the development and changes of primary and man-made fissures and karst in the top and bottom plates of the mining area, and explore the spatiotemporal distribution characteristics of dominant groundwater channels.

[0101] Geophysical data was collected according to the predetermined plan, and data such as resistivity and seismic waves were recorded to analyze the underground geological structure.

[0102] Drilling operations were conducted to collect rock core samples and record information on rock strata and changes in groundwater levels.

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

[0104] Tracers were deployed and their migration paths in groundwater were monitored to verify the connectivity of dominant groundwater channels.

[0105] S4, Well Exploration Well-drilling enables centimeter-level modeling of underground spaces at the microscale (<100 m) and precise location of water-bearing layers. Figure 4 A 3D laser model is shown. The model uses different colors and transparency to distinguish different areas and structures, visually representing the complexity and connectivity of underground space. This model is used to develop precise remediation plans, providing crucial support, particularly in grouting curtain layout and borehole path planning.

[0106] S401. Station Planning: Based on the internal structure of the mine, plan the location of the stations to ensure that the scanning area is unobstructed and that there is sufficient overlap between adjacent stations to ensure the continuity and integrity of the data.

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

[0108] Ensure sufficient overlap between the scanning areas of adjacent stations; a 30%-50% overlap is recommended to guarantee the accuracy of data stitching.

[0109] S402. Control point acquisition: Use a total station or GPS to acquire the coordinates of control points, ensuring that the control points are evenly distributed and cover the entire scanning area, providing a basis for subsequent data registration.

[0110] Distribute control points evenly within the scanning area, for example, one control point per 100 square meters, to ensure coverage of the entire scanning area.

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

[0112] S403. Equipment Setup and Fixing: Mount the 3D laser scanner on a sturdy tripod to ensure the equipment remains stable during the scanning process; use a bubble level or electronic level to adjust the equipment level to ensure the instrument is in the correct orientation and 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 instrument center to the ground marker, and perform equipment calibration to ensure the accuracy of the scanned data.

[0114] S405. Power-on self-test and system initialization: Connect the power supply and start the 3D laser scanner. A self-test program will run, automatically checking system configuration, storage, and other information to confirm that all sensors are functioning correctly and to ensure the equipment is in good working order.

[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 with different precision. Select the appropriate scanning range and mode, such as panoramic scanning or partial scanning. Configure the data storage format (such as LAS, PLY) and storage path to ensure orderly data management and prepare for data acquisition.

[0116] S407. Data Acquisition and Quality Control: Initiate scanning, acquire point cloud data, monitor data quality in real time, check point cloud density and integrity, and perform supplementary scanning as necessary to ensure that the acquired data meets quality requirements. Perform supplementary scanning for areas with missing or substandard data.

[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 foundation for subsequent data processing and analysis.

[0118] S409. Point Cloud Coarse Registration: Perform preliminary registration of scan data from different sites using automatic or manual methods to ensure data continuity and integrity, providing initial data for accurate registration.

[0119] S410 Point Cloud Precise Registration: By inputting the coordinates of registration markers such as the registration target sphere and reflector, a global coordinate system for the point cloud is established, improving registration accuracy and ensuring data accuracy.

[0120] S411. Point Cloud Cleaning: Using filtering algorithms, unwanted noise, background, or human figures are filtered out to ensure the cleanliness and accuracy of the point cloud. The cleaning process includes deleting redundant data and filling in missing areas to improve data quality and usability. For missing data areas, data imputation is performed to ensure the integrity of the point cloud.

[0121] S412, Point Cloud Modeling: Convert point cloud data into a 3D model as needed, for example... Figure 4 The three-dimensional model shown intuitively displays the internal structure and morphology of mines, goafs, and karst caves.

[0122] S413. Data Optimization: Further optimize the point cloud or model, including simplifying the point cloud density, smoothing the model surface, and adjusting colors and textures, to improve the model's visualization 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 applications.

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

[0125] Data fusion: Integrating airborne remote sensing data, space-based LiDAR data, ground survey data, and well-drilled exploration data to construct a unified multi-source database, enabling data sharing and collaborative analysis.

[0126] Construction of a four-dimensional hydrogeological model: Integrating multi-dimensional data such as time (historical evolution), space (surface-subsurface), and physical properties (electrical / permeable properties), 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 a 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] Treatment plan optimization: Based on the analysis results of pollution source migration paths and combined with the three-dimensional spatial model, the location of the grouting curtain and the drilling path are determined to avoid full-area coverage treatment, shorten the treatment period, and reduce treatment costs.

[0129] This embodiment utilizes an integrated "air-space-ground-well" technology to form a progressive exploration logic of "regional screening → key area densification → target point fine investigation," avoiding the resource waste of traditional methods that are like "finding a needle in a haystack." It achieves efficient, accurate, and scientific exploration for the ecological restoration of acid mine water inflow in karst areas, providing strong technical support for mine ecological restoration.

[0130] This invention's integrated "air-space-ground-well" technology significantly shortens the exploration cycle and improves the speed of pollution source location. Specifically, this technology reduces the time required by traditional methods (months or even years) to weeks, shortening the exploration cycle by 40%. Simultaneously, through the fusion and analysis of multi-source data, it quickly and accurately identifies pollution source locations, improving the location speed by 60%. For example, in the satellite remote sensing phase, by utilizing high-resolution imagery and comparing multiple image periods, it is possible to quickly identify topography, water systems, and tailings pile target areas. Combined with dynamic retrospective analysis of historical imagery, the location of pollution sources can be rapidly determined. The introduction of UAV LiDAR further accelerates the data acquisition process. Its centimeter-level resolution can quickly identify geological features such as concealed ground fissures, sinkholes, and karst cones, providing precise guidance for subsequent ground surveys and well exploration, avoiding the "needle in a haystack" search of traditional methods, and significantly improving work efficiency.

[0131] The integrated technology achieved an accuracy rate of over 90% in identifying dominant channels for hidden karst groundwater recharge, while reducing the borehole deviation rate to 5%. By fusing multi-dimensional data from satellite remote sensing, UAV LiDAR, ground geophysical exploration, and well drilling, a high-precision three-dimensional geological model was constructed. For example, satellite remote sensing provided macroscopic topographic and drainage information, UAV LiDAR penetrated vegetation to obtain actual ground elevation data, ground geophysical exploration and drilling obtained detailed geological structures and hydrogeological parameters, and well drilling provided precise three-dimensional point cloud data of the underground space. This data fusion made the identification of hidden karst conduits more accurate, clearly depicting their location, shape, and connectivity. During drilling operations, based on the high-precision three-dimensional model, the drilling path could be precisely planned, effectively reducing the borehole deviation rate, ensuring accurate target hits, and improving the targeting and effectiveness of remediation measures.

[0132] This technology optimizes resource allocation by reducing redundant exploration steps, resulting in a 50% reduction in overall costs. Traditional exploration methods often require multiple trips to different exploration stages, involving a large amount of repetitive work, such as multiple ground surveys and drilling operations, leading to a waste of manpower, materials, and time. In contrast, the integrated technology, through the collaborative application of multi-source data, acquires comprehensive geological information in one go, avoiding redundant exploration. For example, during the satellite remote sensing and UAV LiDAR stages, most of the topographic, geological, and hydrological information can be acquired, providing precise guidance for subsequent ground surveys and well exploration, reducing unnecessary fieldwork. Furthermore, high-precision 3D models and data analysis provide a scientific basis for the development of remediation plans, avoiding over-engineering and resource waste, further reducing costs.

[0133] The integrated "air-space-ground-well" technology provides comprehensive data support across the entire surface and subsurface, encompassing information from macroscopic regional topography and surface water systems to microscopic underground fissures and caves. Satellite remote sensing provides basic information on large-scale topography, surface water systems, and historical tailings piles, while UAV LiDAR identifies hidden geological features such as ground fissures, karst sinkholes, and sinkholes at the mesoscopic scale. Ground surveys, through high-density geophysical exploration, hydrogeological drilling, pumping tests, and tracer experiments, obtain spatiotemporal distribution characteristics of the mining area's geological structure, fissure network, and dominant groundwater channels. Well exploration, using 3D laser scanning technology, acquires detailed 3D point cloud data of the mined-out areas and caverns, which is then fused with space-based data to establish a model linking the surface and subsurface spaces. This multi-dimensional and comprehensive data provides a scientific and comprehensive basis for the formulation of remediation plans, making remediation measures more precise and effective, and enhancing the scientific validity and feasibility of the entire ecological restoration project.

[0134] In the description of this invention, it should be understood that "-" and "~" represent a range between two values, and this 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 this invention, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone.

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

[0137] In the description of this invention, the terms “about” or “approximately” are used to express approximate values ​​or ranges, allowing for a certain degree of error to ensure the flexibility and practicality of the description, while remaining within an acceptable range of error, with the maximum error not exceeding 10% of the corresponding value or range.

[0138] The above are merely preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be considered as limitations on the present invention, and the scope of protection of the present invention should be determined by the scope defined in the claims. For those skilled in the art, several improvements and modifications can be made without departing from the spirit and scope of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A system for ecological restoration of acidic mine water inflow in karst areas, characterized in that, Employing an integrated "air-space-ground-well" technology, and through a progressive and collaborative logic of airborne screening, space-based verification, ground testing, and well scanning, the system achieves precise location of hidden karst conduits in karst regions. This includes the following modules: (a) The space-based remote sensing module is used to identify regional topography, surface water systems and historical tailings piles through satellite remote sensing images, and to locate pollution source target areas through dynamic analysis of multiple historical images; (b) Space-based detection module, equipped with UAV-borne lidar, uses multiple echo technology and filtering algorithms to penetrate vegetation interference, obtain real ground elevation data and UAV LiDAR point cloud, and identify high-level ground fissures, karst sinkholes and sinkholes in karst areas. (c) Ground survey module, which combines high-density geophysical exploration, hydrogeological drilling, pumping tests and tracer tests to obtain the spatiotemporal distribution characteristics of geological structure, fracture network and dominant groundwater channels in the mining area; (d) The well-in-the-hole detection module acquires three-dimensional point cloud data of the goaf and the interior of the karst cave through mine reconnaissance and three-dimensional laser scanning technology, and merges it with the UAV LiDAR point cloud of the space-based detection module to establish a ground-to-underground space correlation model.

2. The exploration system according to claim 1, characterized in that, The space-based remote sensing module includes: The image preprocessing unit is used to perform radiometric correction, geometric correction, and cloud removal on satellite remote sensing images to improve image quality. The pollution source dynamic analysis unit, by comparing multiple historical images, extracts the three-dimensional morphological parameters of tailings piles based on digital elevation models, analyzes their area, volume and sedimentation characteristics, and accurately locates the target area of ​​pollution sources.

3. The exploration system according to claim 1, characterized in that, The space-based detection module includes: The point cloud data processing unit is used to remove outliers and noise points from the acquired LiDAR data and transform the data to the target coordinate system. The terrain modeling unit generates a digital elevation model based on ground point data and combines it with non-ground point data to generate a digital surface model for three-dimensional terrain analysis in mining areas.

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

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

6. A method for ecological restoration exploration of acidic mine water inflow in karst areas, characterized in that, Includes the following steps: S1. By analyzing satellite remote sensing images, the topography, water system and tailings pile target area of ​​the work area are identified based on remote sensing image interpretation algorithms, and the location of pollution sources is determined by dynamic retrospective analysis of multiple historical images. S2. Use UAV-borne LiDAR to acquire high-precision terrain data and UAV LiDAR point cloud, 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 tracing tests to verify the connectivity of dominant groundwater channels and obtain hydrogeological parameters. S4. Obtain point cloud data of the goaf and karst cave through three-dimensional laser scanning of the mine, and perform control point registration and spatial alignment with the UAV LiDAR point cloud of the space-based detection module to establish a three-dimensional spatial model of the ground and underground under 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 location 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 topographic shadow analysis and DEM three-dimensional morphology extraction algorithms, the volume change of tailings piles is quantified, and the migration trend of pollution sources is determined.

8. The method according to claim 6, characterized in that, The filtering algorithm described in step S2 is as follows: 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 by an iterative nearest point algorithm to generate a bare land elevation model.

9. The method according to claim 6, characterized in that, The control point registration in step S4 specifically involves: By collecting the coordinates of ground control points using a total station or GPS, the 3D laser scanning point cloud inside 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 location of the grouting curtain in step S5 is as follows: Based on the analysis results of the karst cave pipeline route and pollution migration path in the three-dimensional spatial model, a grouting curtain is set up downstream of the pipeline to avoid full-area coverage treatment and shorten the treatment period.

Images