Layer fracture dynamic monitoring method and system based on three-dimensional resistivity imaging

Abstract

The invention discloses a layer fracture dynamic monitoring method and system based on three-dimensional resistivity imaging, and relates to the technical field of layer fracture dynamic monitoring methods, a plurality of potential differences are collected according to electrode detection of an underground layer, and a three-dimensional resistivity distribution diagram is determined according to the plurality of potential differences and a three-dimensional model of the underground layer. And the three-dimensional resistivity distribution diagram is introduced, so that the monitoring accuracy of the layer body fractures is ensured. Therefore, according to dynamic monitoring of the position of the layer body fracture, a dynamic change diagram of the layer body fracture is acquired, and according to the dynamic change diagram of the layer body fracture, a danger coefficient of the layer body fracture is determined; according to the method, the dangerous event of the underground layer body is determined according to the positions of the plurality of layer body fractures, the danger coefficient of each layer body fracture and the form of the three-dimensional model of the underground layer body, and the real-time early warning of the layer body fractures is triggered, so that the dynamic monitoring of the dangerous event of the underground layer body is ensured, and the real-time early warning of the layer body fractures is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of methods for dynamic monitoring of layer cracks, and in particular to a method and system for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging. Background Art

[0002] With the development of science and technology, underground layers are often mentioned in underground engineering. Underground layers usually refer to continuous or layered structures of rocks, soils or other geological materials located below the surface of the earth. The formation of underground layers is due to various geological processes such as sedimentation, magma activity, and metamorphism. In the existing technology, underground layers gradually form layer cracks with the change of time. The layer cracks are generally discovered through electrode detection of the underground layer, and the corresponding potential difference is collected. The existence of the layer cracks is determined based on the comparison between the potential difference and the corresponding potential difference threshold. The monitoring accuracy of the layer cracks cannot be guaranteed, and the dynamic changes of the layer cracks cannot be monitored in real time. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a method and system for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging.

[0004] An embodiment of the present invention provides a method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging, comprising: Determine a three-dimensional model of the underground layer body according to the spatial position of the underground layer body and the distribution map of the underground layer body; Determining a plurality of electrode nodes of a plurality of underground layers based on traversing the three-dimensional model of the underground layer, and triggering electrode detection of the underground layer according to positions of the plurality of electrode nodes and the shape of the three-dimensional model of the underground layer; A plurality of potential differences are collected by electrode detection of the underground layer, and a three-dimensional resistivity distribution map is determined based on the plurality of potential differences and a three-dimensional model of the underground layer to determine corresponding layer fractures; According to the dynamic monitoring of the position of the layer crack, a dynamic change diagram of the layer crack is collected, and the risk factor of the layer crack is determined according to the dynamic change diagram of the layer crack; According to the positions of multiple layer cracks, the hazard coefficients of each layer crack and the morphology of the three-dimensional model of the underground layer, dangerous events of the underground layer are determined, and real-time early warning of the layer cracks is triggered.

[0005] An embodiment of the present invention provides a serial communication system based on a data buffer, which is applied to the above-mentioned method for dynamic monitoring of layer fractures based on three-dimensional resistivity imaging. The serial communication system based on a data buffer includes: A three-dimensional model module is used to determine a three-dimensional model of the underground layer according to the spatial position of the underground layer and the distribution map of the underground layer; an electrode detection module, configured to determine a plurality of electrode nodes of a plurality of underground layers based on traversal of the three-dimensional model of the underground layer, and trigger electrode detection of the underground layer according to positions of the plurality of electrode nodes and a shape of the three-dimensional model of the underground layer; A layer fracture module is used to collect multiple potential differences based on electrode detection of underground layers, and determine a three-dimensional resistivity distribution map based on the multiple potential differences and a three-dimensional model of the underground layers to determine the corresponding layer fractures; A dynamic monitoring module is used to collect a dynamic change diagram of the layer cracks based on the dynamic monitoring of the location of the layer cracks, and determine the risk factor of the layer cracks based on the dynamic change diagram of the layer cracks; The dangerous event module is used to determine dangerous events of underground layers according to the positions of multiple layer cracks, the dangerous coefficients of each layer crack and the shape of the three-dimensional model of the underground layer, and trigger real-time early warning of layer cracks.

[0006] Compared with the prior art, the present invention has the following beneficial effects: In an embodiment of the present invention, through the method in the embodiment of the present invention, the three-dimensional model of the underground layer body is determined according to the spatial position of the underground layer body and the distribution map of the underground layer body; based on the traversal of the three-dimensional model of the underground layer body, multiple electrode nodes of multiple underground layers are determined, and the electrode detection of the underground layer body is triggered according to the positions of the multiple electrode nodes and the shape of the three-dimensional model of the underground layer body; multiple potential differences are collected according to the electrode detection of the underground layer body, and the three-dimensional resistivity distribution map is determined according to the multiple potential differences and the three-dimensional model of the underground layer body to determine the corresponding layer cracks. The three-dimensional resistivity distribution map is introduced to ensure the monitoring accuracy of the layer cracks.

[0007] Therefore, according to the dynamic monitoring of the location of the layer cracks, the dynamic change diagram of the layer cracks is collected, and the hazard coefficient of the layer cracks is determined based on the dynamic change diagram of the layer cracks; according to the locations of multiple layer cracks, the hazard coefficient of each layer crack and the shape of the three-dimensional model of the underground layer, the dangerous events of the underground layer are determined, and the real-time early warning of the layer cracks is triggered. It is compatible with the dynamic change diagram of the layer cracks, and the dynamic changes of the layer cracks are monitored in real time, which ensures the dynamic monitoring of the dangerous events of the underground layer and realizes the real-time early warning of the layer cracks. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 3. It is a schematic flow chart of a method for dynamic monitoring of layer fractures based on three-dimensional resistivity imaging in an embodiment of the present invention; Figure 23. It is a flow chart of step S11 in the method for dynamic monitoring of layer fractures based on three-dimensional resistivity imaging in an embodiment of the present invention; Figure 3 3. It is a flow chart of step S12 in the method for dynamic monitoring of layer fractures based on three-dimensional resistivity imaging in an embodiment of the present invention; Figure 4 3. It is a flow chart of step S13 in the method for dynamic monitoring of layer fractures based on three-dimensional resistivity imaging in an embodiment of the present invention; Figure 5 3. It is a flow chart of step S14 in the method for dynamic monitoring of layer fractures based on three-dimensional resistivity imaging in an embodiment of the present invention; Figure 6 3. It is a flow chart of step S15 in the method for dynamic monitoring of layer fractures based on three-dimensional resistivity imaging in an embodiment of the present invention; Figure 7 It is a schematic diagram of the structure of a serial communication system based on a data buffer in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0010] See also Figures 1 to 7 A method for dynamically monitoring layer cracks based on three-dimensional resistivity imaging is applied to a serial communication scenario based on a data buffer. The method comprises: Step S11: determining a three-dimensional model of the underground layer according to the spatial position of the underground layer and the distribution map of the underground layer; Step S12: determining a plurality of electrode nodes of a plurality of underground layers based on traversing the three-dimensional model of the underground layer, and triggering electrode detection of the underground layer according to positions of the plurality of electrode nodes and the shape of the three-dimensional model of the underground layer; Step S13: collecting multiple potential differences based on electrode detection of the underground layer, and determining a three-dimensional resistivity distribution map based on the multiple potential differences and a three-dimensional model of the underground layer to determine corresponding layer fractures; Step S14: collecting a dynamic change diagram of the layer cracks based on the dynamic monitoring of the locations of the layer cracks, and determining a risk factor of the layer cracks based on the dynamic change diagram of the layer cracks; Step S15: determining a dangerous event of the underground layer according to the positions of the plurality of layer cracks, the risk factor of each layer crack and the shape of the three-dimensional model of the underground layer, and triggering a real-time early warning of the layer cracks; refer to Figure 2In step S11, a three-dimensional model of the underground layer is determined according to the spatial position of the underground layer and the distribution map of the underground layer; In the specific implementation process of the present invention, the specific steps are: S111: Detecting the position of the underground layer, collecting the spatial position of the underground layer, and determining the spatial area corresponding to the underground layer based on the spatial position and the shape of the underground layer; S112: collecting a database of underground layers, determining a distribution map of the underground layers based on the spatial positions of the underground layers and the database of the underground layers, and determining an area to be modeled based on the distribution map of the underground layers and the spatial area corresponding to the underground layers; S113: In the area to be modeled, a three-dimensional model of the underground layer body is determined based on the synthesis of the shape of the underground layer body and the real-time image of the underground layer body, an abnormal area is determined based on the detection of the three-dimensional model of the underground layer body, and a review of the abnormal area is triggered to improve the three-dimensional model of the underground layer body.

[0011] In an embodiment of the present application, the position of the underground layer body is detected, and the spatial position of the underground layer body is collected. The spatial area corresponding to the underground layer body is determined based on the spatial position of the underground layer body and the shape of the underground layer body, which is compatible with the overall consideration of the spatial position of the underground layer body and the shape of the underground layer body, and ensures the accuracy of the spatial area corresponding to the underground layer body.

[0012] At this time, the position of the underground layer is detected. Based on the position detection, the spatial position information of the underground layer needs to be collected; this usually involves converting the exploration data into a geographic coordinate system and recording the longitude, latitude, altitude (or depth) and other information of each detection point; for methods such as seismic exploration and electromagnetic methods, it is also necessary to process and analyze information such as the time delay, amplitude and phase of the reflected wave or response signal to determine the specific position of the underground layer.

[0013] Optionally, in seismic exploration, the location information of each seismic wave excitation point and receiving point is recorded, and the time it takes for the seismic wave to propagate to different depths and reflect back is measured; by processing these time delay data, the depth and shape of the underground layer are calculated; similarly, in geological radar exploration, the propagation path of the radar wave and the location of the reflected signal are recorded to determine the specific location of the underground layer.

[0014] After collecting the spatial location information of the underground strata, it is necessary to determine the spatial areas to which they correspond based on the morphology of the underground strata (such as layered, blocky, folds, faults, etc.); this usually involves using a geographic information system (GIS) or three-dimensional modeling software to visualize the exploration data into three-dimensional geological models or geological profiles; through these models or profiles, the spatial distribution and morphological characteristics of the underground strata can be intuitively seen.

[0015] Optionally, after collecting data from seismic exploration and geological radar, GIS software will be used to convert these data into a three-dimensional geological model; through the model, the spatial distribution and morphological characteristics of aquifers, impermeable layers and potential hydrological channels can be seen; then, based on these characteristics, different spatial regions of the study area can be determined, such as deep aquifer areas, shallow aquifer areas, impermeable layer areas, etc., to provide a basis for subsequent geological analysis and water resources management.

[0016] Furthermore, a database of underground layers is collected, and a distribution map of the underground layers is determined based on the spatial position of the underground layers and the database of the underground layers. The area to be modeled is determined according to the distribution map of the underground layers and the spatial area corresponding to the underground layers. This is compatible with the overall consideration of the distribution map of the underground layers and the spatial area corresponding to the underground layers, ensuring the accuracy of the area to be modeled.

[0017] At this time, various data and information about the underground layer are collected to form a complete database; these data come from geological exploration reports, historical monitoring data, geological maps, remote sensing images, geophysical exploration results, etc.; the database should contain key information such as the location, morphology, lithology, thickness, occurrence, hydrogeological conditions, etc. of the underground layer; at this time, the geological exploration report provides a detailed description and classification of the underground layer, including lithology, structure, construction and other information; historical monitoring data records past monitoring results of the underground layer, such as water level changes, ground subsidence, etc.; geological maps show the spatial distribution and morphological characteristics of the underground layer; remote sensing images provide visual information of surface and shallow geological features, which helps to identify geological structures and lithology changes; geophysical exploration results such as seismic wave velocity, resistivity, magnetic susceptibility and other data can be used to infer the physical properties and structure of the underground layer.

[0018] Optionally, a geological exploration report for the area will be collected, which will describe in detail the type, thickness, and occurrence of the underground rock layers. Historical monitoring data will also be obtained to understand the changing trends of groundwater levels in the area over the past few decades. In addition, geological maps will be consulted to understand the spatial distribution and morphological characteristics of the underground layers. Finally, geophysical exploration, such as seismic exploration and electromagnetic exploration, will be carried out to obtain information on the physical properties and structure of the underground layers. All of these data will be integrated into a database to provide a basis for subsequent analysis and modeling.

[0019] After collecting a database of subsurface strata, it is necessary to use this data to determine the distribution map of the subsurface strata. This usually involves using a geographic information system (GIS) or geological modeling software to visualize the information in the database as two-dimensional or three-dimensional graphics. The distribution map should accurately reflect the spatial position, morphology, thickness, lithology and other characteristics of the subsurface strata. At this time, the geographic coordinate information and attribute data in the database are imported into the GIS software to generate a spatial distribution map of the subsurface strata. The geological modeling software is used to integrate the information in the database into a three-dimensional geological model to display the three-dimensional morphology and spatial relationships of the subsurface strata.

[0020] Optionally, after all relevant data are collected, GIS software will be used to visualize these data. A distribution map of underground rock formations will be drawn in GIS based on geological exploration reports and historical monitoring data, including the type, thickness, and occurrence of the rock formations. At the same time, geophysical exploration results (such as seismic wave velocity and resistivity data) will be superimposed on the distribution map to provide richer underground layer information. Through these visualization methods, the spatial distribution and morphological characteristics of the underground layers can be intuitively seen, providing a basis for subsequent analysis and modeling.

[0021] After determining the distribution map of the underground layers, the area to be modeled needs to be determined based on the distribution map and the spatial regions corresponding to the underground layers. This typically involves identifying key geological features, potential geological hazard areas, or areas with specific engineering requirements, and determining the spatial extent and boundaries of these areas. At this point, important geological structures, rock interfaces, and potential geological hazard areas are identified based on the distribution map. The stability of the underground layers and potential geological hazard risks, such as landslides, ground subsidence, and earthquakes, are analyzed. The spatial extent and boundaries of the area to be modeled are determined based on underground space planning or the needs of specific engineering projects.

[0022] Therefore, in the area to be modeled, the three-dimensional model of the underground layer is determined based on the synthesis of the morphology of the underground layer and the real-time image of the underground layer. The abnormal area is determined based on the detection of the three-dimensional model of the underground layer, and the review of the abnormal area is triggered to improve the three-dimensional model of the underground layer. The three-dimensional model of the underground layer is introduced to improve the underground layer.

[0023] At this point, the morphological data of the underground layer in the area to be modeled and real-time image data (such as geological radar images, seismic wave images, and drill core images) are used to construct a three-dimensional model of the underground layer. At this point, the morphological data and real-time image data are integrated into a unified data framework to ensure data accuracy and consistency. A three-dimensional model of the underground layer is constructed based on the integrated data using three-dimensional modeling software or a geographic information system (GIS). The model should accurately reflect the morphology, structure, thickness, lithology, and other characteristics of the underground layer. The accuracy of the model is verified by comparing the model with known geological information (such as geological exploration reports and historical monitoring data). Optionally, understand the distribution and stability of underground rock formations; the basic morphology and lithology of the underground strata have been determined through geological exploration, and real-time geological radar images and seismic wave images have been collected; next, use 3D modeling software to integrate these data into a model to construct a 3D stereoscopic model of the underground rock formations; this model shows the distribution, thickness, occurrence, and existing fault and fold structures of the rock formations; in order to verify the accuracy of the model, the model was compared with known geological exploration reports, and it was found that the model was consistent with the actual situation.

[0024] After constructing a three-dimensional model of the underground layer, model detection algorithms (such as outlier detection, pattern recognition, etc.) are used to identify abnormal areas in the model; these abnormal areas represent the complexity of the geological structure, potential geological hazards (such as landslides, ground subsidence, earthquakes, etc.) or other geological problems. At this time, outliers are identified by analyzing the attribute values ​​in the model (such as lithology, thickness, wave velocity, etc.), and these outliers indicate geological anomaly areas; machine learning algorithms or pattern recognition techniques are used to identify specific geological patterns or structures in the model, which are associated with geological hazards or geological problems; the results of outlier detection and pattern recognition are combined to comprehensively evaluate the stability and potential risks of the underground layer.

[0025] Optionally, a three-dimensional model detection algorithm is used to identify abnormal areas in the model. By analyzing the lithology distribution and thickness changes in the model, an abnormal area is found. The lithology of this area is significantly different from that of other areas and the thickness is thinner. At the same time, through pattern recognition technology, it is also found that there is an obvious fault structure in this area. Combining this information, it is judged that there is a risk of landslide in this area, and it is decided to conduct further exploration and verification.

[0026] After the abnormal area is identified, a review of the area needs to be triggered to obtain more detailed geological information and data. The review involves further geological exploration (such as drilling exploration, geophysical exploration, etc.), field investigation or laboratory analysis. Through the review, the geological characteristics and potential risks of the abnormal area can be more accurately understood, and the 3D model can be improved accordingly. At this time, a detailed review plan is formulated based on the characteristics and potential risks of the abnormal area, including exploration methods, exploration point layout, data analysis, etc. Field exploration and data analysis are carried out according to the review plan to obtain more detailed geological information and data. The review results are integrated into the 3D model, and the model is revised and improved to ensure its accuracy and reliability. Optionally, a detailed review plan was developed for the previously identified abnormal areas; drilling exploration was selected as the main exploration method, and multiple exploration points were arranged in the abnormal areas; more detailed core samples and groundwater level data were obtained through drilling exploration; at the same time, geophysical exploration (such as seismic exploration and electromagnetic exploration) was also carried out to obtain more geological information; through comprehensive analysis of these review results, the three-dimensional model was revised and improved to more accurately reflect the geological characteristics and potential risks of the abnormal areas; this provided a reliable geological basis for subsequent underground engineering design and construction.

[0027] refer to Figure 3 In step S12, a plurality of electrode nodes of a plurality of underground layers are determined based on the traversal of the three-dimensional model of the underground layer, and electrode detection of the underground layer is triggered according to the positions of the plurality of electrode nodes and the shape of the three-dimensional model of the underground layer; In the specific implementation process of the present invention, the specific steps are: S121: Acquire a three-dimensional model of the underground layer, and determine multiple detection areas based on the traversal of the three-dimensional model of the underground layer. Two adjacent detection areas have corresponding layer boundary lines; S122: In each detection area, a corresponding electrode node is determined based on the spatial position, area, and shape of the detection area. The electrode node is used to supply a power supply to perform electrode detection on the underground layer. S123: Collect the shape of the three-dimensional model of the underground layer body, determine the electrode detection mode of the underground layer body according to the matching of the shape of the three-dimensional model of the underground layer body and the positions of multiple electrode nodes, and trigger multiple electrode nodes to perform electrode detection on the underground layer body along the electrode detection mode.

[0028] In an embodiment of the present application, a three-dimensional model of the underground layer is collected, and multiple detection areas are determined based on the traversal of the three-dimensional model of the underground layer. Two adjacent detection areas have corresponding layer boundary lines, which is compatible with the overall consideration of the traversal of the three-dimensional model of the underground layer and ensures the accuracy of multiple detection areas.

[0029] At this time, a three-dimensional model of the underground layer is collected. After obtaining the three-dimensional model of the underground layer, the model needs to be traversed, that is, the entire model needs to be searched and checked according to certain rules or algorithms; the purpose of the traversal is to divide the model into multiple detection areas, which can be regular (such as rectangles, squares, etc.) or irregular, depending on the actual shape and geological characteristics of the underground layer.

[0030] At this time, different traversal algorithms are selected, such as depth-first search (DFS), breadth-first search (BFS), etc., and the appropriate algorithm is selected according to the complexity and requirements of the model; based on the traversal results and the characteristics of the underground layers, the model is divided into multiple detection areas; these areas should cover the entire model, and there should be clear layer boundaries between adjacent areas; layer boundaries are usually geological feature lines, such as rock interfaces, faults, joints, etc.; these boundaries appear as obvious morphological changes or attribute differences in the three-dimensional model, and are an important basis for dividing the detection areas.

[0031] When dividing the detection area, it is necessary to ensure that there are clear layer boundaries between adjacent areas; these boundaries not only help to distinguish the geological characteristics of different areas, but also provide an important basis for subsequent geological analysis, resource assessment and disaster prediction; at this time, the boundaries are determined by morphological changes, attribute differences or geological characteristic lines in the three-dimensional model; the accuracy and reliability of the boundaries are verified through geological exploration data, historical monitoring data or geological maps.

[0032] Furthermore, in each detection area, the corresponding electrode node is determined based on the spatial position of the detection area, the regional area of ​​the detection area, and the regional morphology of the detection area. The electrode node is used to supply the power generator with electrode detection of the underground layer, which is compatible with the overall consideration of the spatial position of the detection area, the regional area of ​​the detection area, and the regional morphology of the detection area, thereby ensuring the accuracy of the corresponding electrode node.

[0033] At this time, the spatial position of the detection area is usually described by a geographic coordinate system (such as latitude and longitude coordinates, UTM coordinates, etc.); it is necessary to clarify the coordinates of the boundaries, center points or other key points of each detection area in order to subsequently determine the specific location of the electrode node; at this time, select a suitable geographic coordinate system to describe the location of the detection area; determine the geographic coordinates of the boundary lines, center points or other key points of the detection area.

[0034] The size of the detection area directly affects the number and distribution of electrode nodes. A larger detection area requires more electrode nodes to ensure comprehensiveness and accuracy of detection. At the same time, the distribution of electrode nodes should also take into account the shape and characteristics of the area to ensure that the current can be evenly distributed throughout the detection area. At this time, use GIS software or other tools to measure the area of ​​the detection area. Determine the required number of electrode nodes based on the area size. Generally, the number of electrode nodes is proportional to the area of ​​the detection area. According to the shape and characteristics of the area, reasonably distribute the electrode nodes to ensure uniform current distribution.

[0035] The shape of the detection area (such as rectangular, circular, irregular, etc.) has a significant impact on the arrangement of electrode nodes. Different electrode arrangements are required for areas of different shapes to ensure that the current can cover the entire detection area and avoid excessive concentration of current at edges or corners. At this time, the shape of the detection area is analyzed to determine whether it is regular and whether it contains complex boundaries or internal features. Based on the results of the morphological analysis, a suitable electrode arrangement is selected. For example, in areas of regular shapes, a uniform grid arrangement is used; in areas of irregular shapes, a more flexible arrangement is required.

[0036] After determining the spatial position, area, and shape of the detection area, begin to determine the corresponding electrode nodes; the location of the electrode nodes should be based on a comprehensive consideration of the above factors to ensure the effectiveness and accuracy of electrode detection; at this time, determine the specific location of each electrode node based on the spatial position, area, and shape of the detection area; mark the location of the electrode node in GIS software or other tools, and record its coordinates and other relevant information.

[0037] Therefore, the shape of the three-dimensional model of the underground layer body is collected, and the electrode detection mode of the underground layer body is determined according to the matching of the shape of the three-dimensional model of the underground layer body and the positions of multiple electrode nodes. Multiple electrode nodes are triggered to perform electrode detection on the underground layer body along the electrode detection mode, which is compatible with the overall consideration of the matching of the shape of the three-dimensional model of the underground layer body and the positions of multiple electrode nodes, thereby ensuring the accuracy of the electrode detection mode of the underground layer body.

[0038] At this time, the three-dimensional model usually contains key information such as the morphology, structure, lithology, thickness, etc. of the underground layer; this information is crucial for determining the electrode detection mode because it will affect the propagation path and distribution of the current in the underground layer; at this time, the three-dimensional model of the underground layer is obtained through technical means such as geological exploration, three-dimensional geological modeling or geographic information system (GIS); the three-dimensional model is subjected to morphological analysis to extract key morphological features, such as rock layer interfaces, faults, folds, etc.

[0039] The electrode detection mode refers to the arrangement of electrode nodes in the underground layer and the current application method; this step needs to be determined based on the three-dimensional model of the underground layer and the positions of multiple electrode nodes; at this time, the three-dimensional model of the underground layer is matched with the positions of the electrode nodes, and the current propagation path and distribution in the underground layer are analyzed; based on the matching results, the appropriate electrode detection mode is selected; common electrode detection modes include two-dimensional electrode arrays, three-dimensional electrode grids, radial electrode arrays, etc.; according to the specific characteristics of the underground layer and the exploration objectives, the detection mode is optimized to improve the accuracy and efficiency of detection.

[0040] After the electrode detection mode is determined, multiple electrode nodes need to be triggered along the mode for electrode detection; at this time, the electrode nodes are configured at the corresponding positions according to the detection mode and connected to the power supply machine and the data acquisition system; current is applied to the electrode nodes through the power supply machine to form an electric field; the current propagates in the underground layer and is affected by the morphology and structure of the underground layer; the data acquisition system is used to record the changes in current and voltage, as well as the relationship between these changes and the characteristics of the underground layer; the collected data is processed and analyzed to extract geological information of the underground layer, such as resistivity, polarizability, etc.

[0041] Specifically, assume that an underground mining area is being explored with the goal of determining the location, shape, and size of the ore body. A three-dimensional model of the underground layer has been acquired, and the locations of multiple electrode nodes have been determined. Next, the electrode detection mode is determined based on the three-dimensional model shape of the underground layer and the locations of the electrode nodes. By analyzing the three-dimensional model, it is found that the ore body is mainly located in a complex rock layer interface and has an irregular shape. Therefore, a three-dimensional electrode grid is selected as the detection mode to better cover the entire mining area and the location of the ore body.

[0042] After determining the detection pattern, electrode nodes were configured along the pattern and connected to the power supply and data acquisition system. Current was then applied to the electrode nodes, and changes in current and voltage were recorded. Data analysis revealed some abnormal areas of resistivity and polarizability that coincided with the location and morphology of the ore body. Finally, based on the data analysis results, the specific location, morphology, and scale of the ore body were determined. This information is of great significance for subsequent mineral development and resource assessment. This example illustrates the importance and application value of step S123 in actual exploration.

[0043] In one embodiment of the present application, an electrode node position matching table is collected, and the electrode node position matching table is shown in Table 1:

[0044] Assuming that the three-dimensional model of the underground layer shows the existence of an obvious rock interface, a two-dimensional electrode array is selected as the detection mode according to the electrode node position matching table, and the electrode nodes are arranged along the rock interface.

[0045] refer to Figure 4 In step S13, a plurality of potential differences are collected according to electrode detection of the underground layer, and a three-dimensional resistivity distribution map is determined according to the plurality of potential differences and a three-dimensional model of the underground layer to determine corresponding layer fractures; In the specific implementation process of the present invention, the specific steps are: S131: Real-time detection of the underground layer by electrode detection. The underground layer has multiple potentials under the action of electrode detection of multiple electrode nodes. The multiple potentials are distributed at different positions of the underground layer and form corresponding potential differences, so as to collect multiple potential differences in the underground layer. S132: mapping the multiple potential differences to corresponding positions of the underground layer to form a three-dimensional resistivity distribution map, and determining multiple fracture detection areas based on detection of the three-dimensional resistivity distribution map; S133: Determine the crack identification method of the crack detection area based on the relative positions of the multiple crack detection areas, the regional morphologies of the multiple crack detection areas and the previous data of the underground layer, and synchronously identify the multiple crack detection areas along the crack identification method to output the corresponding layer cracks. The layer cracks have different morphologies and present corresponding morphologies based on different potential differences.

[0046] In an embodiment of the present application, electrode detection of the underground layer is performed in real time. The underground layer has multiple potentials under the action of electrode detection of multiple electrode nodes. The multiple potentials are distributed at different positions of the underground layer and form corresponding potential differences. In order to collect multiple potential differences in the underground layer, the collection of multiple potential differences in the underground layer is introduced.

[0047] At this time, the electrode detection of the underground layer is carried out in real time. This step is the beginning of the whole process, which involves the use of multiple electrode nodes to perform real-time electrode detection on the underground layer. These electrode nodes are usually arranged on the ground or in the well to form an electric field and penetrate the underground layer. At this time, according to the characteristics of the underground layer and the exploration target, a suitable electrode node arrangement scheme is selected; this includes linear arrangement, grid arrangement or three-dimensional arrangement, etc.; by applying current or voltage to the electrode nodes, an electric field penetrating the underground layer is formed; and a data acquisition system is used to record the potential or current changes of the electrode nodes in real time.

[0048] Under the action of the electric field, different potentials will be formed at different locations in the underground layer; these potentials are determined by factors such as the conductivity, resistivity, thickness and morphology of the underground layer; at this time, the potential forms a complex distribution pattern in the underground layer, and this distribution pattern is closely related to the structural characteristics of the underground layer; by measuring the potential of the electrode nodes, the potential distribution of the underground layer can be indirectly understood.

[0049] Because the electric potential at different locations in the underground layer is different, a potential difference will be formed between adjacent electrode nodes. These potential differences are important data for electrode detection, as they reflect the electrical characteristics of the underground layer. At this time, by comparing the potential values ​​of adjacent electrode nodes, the potential difference between them is calculated; the calculated potential difference is recorded for subsequent data processing and interpretation.

[0050] During the entire electrode detection process, multiple potential difference data need to be collected; this data will be used to construct the resistivity distribution map of the underground layer or perform other geological interpretations; at this time, a data acquisition system is used to collect potential difference data in real time; the collected potential difference data is stored in a computer for subsequent processing and analysis.

[0051] Specifically, assume that an underground mining area is being explored with the goal of determining the location and morphology of the ore body. A series of electrode nodes have been arranged on the ground, and electrode detection has been carried out according to a predetermined plan. During the real-time detection process, the potential changes of each electrode node are recorded. Due to the presence of the underground ore body, it will disturb the electric field, resulting in an abnormal potential distribution at the electrode nodes above or near the ore body.

[0052] By comparing the potential values ​​of adjacent electrode nodes, the potential difference between them is calculated, and multiple such potential difference data are recorded; this data reflects the disturbance of the electric field by the underground ore body; next, this data will be used to construct the resistivity distribution map of the underground layer, and the location and shape of the ore body will be determined by analyzing the resistivity distribution map; this process will involve complex data processing and geological interpretation work, but the potential difference data collected in real time is the basis for these tasks.

[0053] Furthermore, multiple potential differences are mapped to corresponding positions of the underground layer, and a three-dimensional resistivity distribution map is formed. Multiple crack detection areas are determined based on the detection of the three-dimensional resistivity distribution map, which is compatible with the overall consideration of the detection of the three-dimensional resistivity distribution map and ensures the accuracy of multiple crack detection areas.

[0054] At this point, the multiple potential difference data collected previously are aligned with the specific locations of the underground layer; this is usually achieved through a geographic information system (GIS) or three-dimensional geological modeling software. At this point, the collected potential difference data are sorted to ensure that each data point contains corresponding location information (such as longitude, latitude, depth, etc.); using GIS or three-dimensional geological modeling software, each potential difference data point is aligned with the three-dimensional model of the underground layer; this is usually done by importing the potential difference data into the software and placing it at the corresponding position of the three-dimensional model according to the location information of the data point; the aligned data is verified to ensure that each potential difference data point is accurately placed at the corresponding position of the underground layer.

[0055] After completing the alignment of the potential difference data, the data is used to construct a 3D resistivity distribution map. At this point, an appropriate resistivity inversion algorithm is selected. These algorithms are usually based on geophysical principles, such as electromagnetic induction and resistivity imaging. The parameters of the inversion algorithm are set according to the characteristics of the underground layer and the exploration objectives. These parameters include the conductivity model, the number of iterations, the convergence conditions, etc. Next, the 3D resistivity distribution of the underground layer is calculated using the inversion algorithm and the potential difference data. This is usually an iterative process, and the parameters need to be continuously adjusted to obtain the resistivity distribution map that best matches the actual situation. Finally, the calculated 3D resistivity distribution is visualized to generate a 3D resistivity distribution map that is easy to understand and analyze.

[0056] After obtaining the three-dimensional resistivity distribution map, the fracture detection area is determined based on the resistivity anomaly areas in the map. At this time, the three-dimensional resistivity distribution map is carefully analyzed to identify the areas with abnormal resistivity. These abnormal areas usually manifest as sudden changes in resistivity or abnormal distribution. Based on the knowledge of geology and geophysics, it is judged whether these abnormal areas are fractures. This usually requires considering factors such as the lithology, structure, and stress state of the underground layer. Based on the judgment results, multiple fracture detection areas are delineated on the three-dimensional resistivity distribution map. These areas will serve as the focus of subsequent exploration and monitoring.

[0057] Specifically, assume that an underground mining area is being explored with the goal of determining whether there are potential leakage fractures around the mining area. Multiple potential difference data have been collected, aligned, and organized according to the steps in S131. A three-dimensional resistivity distribution map is constructed using this potential difference data. During the construction process, an appropriate resistivity inversion algorithm is selected and the corresponding parameters are set. After multiple iterations and adjustments, a relatively accurate three-dimensional resistivity distribution map is obtained.

[0058] After carefully analyzing the 3D resistivity distribution map, some areas with abnormal resistivity were discovered. These areas showed a sudden decrease or increase in resistivity, which was significantly different from the resistivity distribution of the surrounding areas. Based on the knowledge of geology and geophysics, it was determined that these abnormal areas were most likely caused by cracks. Therefore, multiple crack detection areas were delineated on the 3D resistivity distribution map and used as the focus of subsequent exploration and monitoring. Through this example, we can see the importance of step S132 in geological exploration. By aligning the potential difference data with the specific location of the underground layer and constructing a 3D resistivity distribution map, potential crack areas can be effectively identified, providing strong support for subsequent exploration and monitoring work.

[0059] Therefore, the crack identification method of the crack detection area is determined according to the relative positions of multiple crack detection areas, the regional morphology of multiple crack detection areas and the previous data of the underground layer, and the multiple crack detection areas are synchronously identified along the crack identification method to output the corresponding layer cracks. The layer cracks have different morphologies and present corresponding morphologies based on different potential differences. It is compatible with the overall consideration of the relative positions of multiple crack detection areas, the regional morphology of multiple crack detection areas and the previous data of the underground layer, ensuring the accuracy of the crack identification method of the crack detection area. At the same time, the three-dimensional resistivity distribution map is introduced to ensure the monitoring accuracy of the layer cracks.

[0060] At this time, the most appropriate fracture identification method is determined by comprehensively considering the relative positions, regional morphologies and previous data of multiple fracture detection areas in the underground layer. At this time, the relative position relationship of each fracture detection area in the underground layer is analyzed; this helps to understand the distribution pattern of fractures, such as whether they are linearly distributed, whether they are concentrated in a certain area, etc.; the morphology of each fracture detection area is evaluated; this includes the length, width, direction and other characteristics of the fracture; this information helps to determine the type of fracture (such as tension fracture, shear fracture, etc.) and cause; combined with previous data of the underground layer (such as geological exploration reports, drilling data, etc.), the fracture identification method is further verified and refined; previous data provides information on the lithology, structure, stress state and other aspects of the underground layer, which is crucial for accurate fracture identification.

[0061] After comprehensively analyzing the relative positions, regional morphology and previous data of multiple fracture detection areas, one or more fracture identification methods are determined; these methods include geological radar detection, seismic exploration, transient electromagnetic method, and borehole coring. At this time, the appropriate fracture identification method is selected based on the characteristics of the fracture and the exploration objectives. For example, for linearly distributed fractures, geological radar detection is more effective; while for deep fractures, seismic exploration is more appropriate. A detailed exploration plan is formulated, including the layout of exploration points, the setting of exploration parameters, and the methods of data acquisition and processing.

[0062] After determining the fracture identification methods, it is necessary to simultaneously identify multiple fracture detection areas along these methods; this usually involves field exploration and data collection. At this time, according to the exploration plan, field exploration is carried out in multiple fracture detection areas; this includes steps such as arranging exploration points, installing exploration equipment, and collecting data; ensuring that data is collected simultaneously in multiple fracture detection areas; this helps to compare the characteristics and distribution patterns of fractures in different areas, thereby improving the accuracy of identification.

[0063] After completing field exploration and data collection, the collected data needs to be processed and analyzed to output the corresponding layer fracture information; at this time, the collected data is preprocessed, such as denoising, filtering, correction, etc., to improve the data quality; the processed data is used to identify fractures using appropriate algorithms or models; this involves image recognition, machine learning and other technologies; the identified fracture information is output in a visual manner, such as a three-dimensional fracture distribution map, fracture parameter table, etc.; this information is used for subsequent geological analysis, resource assessment, disaster prediction and other aspects.

[0064] It is understood that an underground mining area is being explored with the goal of determining whether there are potential cracks around the mining area to ensure construction safety and mining area stability; multiple crack detection areas have been identified in accordance with the steps of S132, and crack identification is ready.

[0065] The relative positions and regional morphologies of these fracture detection areas were analyzed; it was found that the fractures were mainly distributed linearly and concentrated in certain areas around the mining area; combined with previous geological exploration reports, it was determined that these fractures were caused by geological tectonic activities; geological radar detection was selected as the fracture identification method; geological radar detection has the advantages of high efficiency, non-destructiveness, and high resolution, and is suitable for detecting shallow underground fractures; a detailed exploration plan was formulated, including the layout of exploration points, the setting of radar parameters, and the methods of data acquisition and processing.

[0066] Multiple fracture detection areas were identified simultaneously using geological radar detection. During the on-site exploration process, multiple exploration points were arranged and geological radar equipment was installed. Radar images of each exploration point were obtained through data acquisition and processing. Fractures were identified on the radar images. Image recognition algorithms were used to identify fracture features in the radar images, and parameters such as the length, width, and direction of the fractures were calculated. The identified fracture information was output in the form of a three-dimensional fracture distribution map, providing an important reference for subsequent mine design and construction.

[0067] In one embodiment of the present application, a crack detection matching table is collected, and the crack detection matching table is shown in Table 2:

[0068] This crack detection matching table shows the crack identification method, location, shape, length and width of each crack detection area, providing an important reference for subsequent underground engineering design and construction. refer to Figure 5 In step S14, a dynamic change diagram of the layer cracks is collected based on the dynamic monitoring of the location of the layer cracks, and a risk factor of the layer cracks is determined based on the dynamic change diagram of the layer cracks; In the specific implementation process of the present invention, the specific steps are: S141: Collect the location of the stratum cracks and dynamically monitor the location of the stratum cracks. Based on the dynamic monitoring of the location of the stratum cracks, multiple change nodes of the stratum cracks are determined. Based on the synthesis of the multiple change nodes of the stratum cracks, a dynamic change map of the stratum cracks is determined. S142: Determine the current form of the layer crack according to the dynamic change diagram of the layer crack, and determine a first sub-risk factor based on the current form of the layer crack and the duration of the change of the layer crack; determine a second sub-risk factor based on the current form of the layer crack and the position of the layer crack; S143: Collect the hazard level mapping relationship, and determine the hazard level of the layer crack based on the current shape of the layer crack and the hazard level mapping relationship, and determine the hazard coefficient of the layer crack according to the hazard level of the layer crack, the first sub-hazard coefficient and the second sub-hazard coefficient.

[0069] In an embodiment of the present application, the locations of the stratum fractures are collected, and the locations of the stratum fractures are dynamically monitored. Based on the dynamic monitoring of the locations of the stratum fractures, multiple change nodes of the stratum fractures are determined. Based on the synthesis of the multiple change nodes of the stratum fractures, a dynamic change graph of the stratum fractures is determined, which is compatible with the overall consideration of the synthesis of the multiple change nodes of the stratum fractures, and ensures the accuracy of the dynamic change graph of the stratum fractures.

[0070] At this time, the location of the layer cracks is collected. At the same time, geologists will use geological exploration equipment (such as geological radar, seismic exploration instruments, transient electromagnetic instruments, etc.) to explore the underground layer to determine the initial location of the layer cracks. During the exploration process, the coordinate information (longitude, latitude, depth) of the cracks, as well as the morphological characteristics of the cracks (such as length, width, direction, occurrence, etc.) will be recorded in detail. This information is the basis for subsequent analysis and monitoring. The collected crack location information will be marked on the geological map to form a preliminary crack distribution map. This helps to intuitively understand the location and distribution pattern of cracks in the underground layer.

[0071] After the location of the fracture is determined, monitoring equipment such as displacement sensors, stress sensors, tilt sensors, etc. will be arranged near the fracture or at key locations; these devices can monitor the deformation and stress state of the fracture and the surrounding strata in real time; at the same time, the monitoring equipment will automatically collect data at a certain frequency (such as every hour, every day, etc.) and transmit the data to the data center or remote monitoring system; the data can be viewed and analyzed in real time through the Internet or dedicated software; the collected data will be processed and analyzed to assess the changing trends and risks of the fracture; this includes calculating indicators such as the displacement of the fracture, the stress change rate, and the deformation rate.

[0072] Based on the changing trends of the monitoring data, the time points when the crack positions change significantly are identified, namely the change nodes; these nodes usually correspond to events such as sudden expansion of the cracks, accelerated displacement or sharp changes in stress state; for each identified change node, key information such as the time of occurrence, position change, stress change, etc. will be recorded; this information is crucial for the subsequent synthesis of dynamic change maps.

[0073] Optionally, in underground ore body projects, three key change nodes were identified through monitoring data; the first node occurred on the third day after the start of monitoring, when the displacement of the crack suddenly increased; the second node occurred on the fifth day, when the stress state of the crack changed significantly; the third node occurred on the seventh day, when the expansion trend of the crack became more obvious; the key information of these nodes was recorded in detail.

[0074] All identified change nodes and their key information are integrated to form a complete data set; this data set contains all important change information of the crack from its initial state to its current state; based on the integrated data set, a professional geographic information system (GIS) or mapping software will be used to synthesize a dynamic change diagram of the layer cracks; this diagram usually includes information such as the initial position of the crack, the position of the change node, the displacement, the stress state, etc., and displays the dynamic change process of the crack in the form of a time series; finally, the synthesized dynamic change diagram will be analyzed to evaluate the change trend of the cracks, potential risks and the impact on the safety of underground engineering.

[0075] Optionally, based on the three identified change nodes and their key information, a dynamic change map of the layer cracks was synthesized using GIS software; this map clearly shows the dynamic change process of the cracks from the initial state to the seventh day, including the expansion trend of the cracks, the displacement and the change of the stress state; by analyzing this map, the impact of the cracks on the safety of the underground layer was evaluated, and corresponding response measures were formulated.

[0076] Furthermore, the current form of the layer cracks is determined according to the dynamic change diagram of the layer cracks, and the first sub-risk coefficient is determined based on the current form of the layer cracks and the duration of the change of the layer cracks; the second sub-risk coefficient is determined based on the current form of the layer cracks and the position of the layer cracks, which is compatible with the overall consideration of the current form of the layer cracks and the position of the layer cracks, and ensures the accuracy of the second sub-risk coefficient.

[0077] At this time, the dynamic change diagram of the layer cracks is analyzed. The diagram usually contains all important change information of the cracks from the initial state to the current state, such as position, length, width, direction, etc.; by analyzing the dynamic change diagram, the current morphology of the layer cracks is identified, including whether it continues to expand, whether new branches appear, whether it intersects with other cracks, etc.; these morphological changes are crucial for assessing the danger of the cracks; after identifying the current morphology, the morphological characteristics of the cracks need to be recorded in detail for subsequent hazard factor calculation and risk assessment.

[0078] Calculate the time it takes for the layer cracks to change from the initial state to the current morphology, which is usually obtained by analyzing the time nodes on the dynamic change diagram; next, it is necessary to evaluate the morphological change rate of the cracks, that is, the change in characteristics such as crack length and width per unit time; this is calculated by comparing the crack morphology at different time points; based on the morphological change rate and change time of the cracks, use a predetermined hazard coefficient calculation formula or evaluation model to determine the first sub-hazard coefficient; generally, the faster the morphological change and the longer the change time, the higher the first sub-hazard coefficient.

[0079] Optionally, in an underground ore body project, it is calculated that the time it takes for layer fractures to change from an initial state to a current morphology is one month (approximately 30 days); by comparing the fracture morphology at different time points, it is assessed that the rate of change of the fracture morphology is approximately 0.67 meters per day (20 meters / 30 days); based on this information, a predetermined hazard factor calculation formula is used to obtain a first sub-hazard factor of 0.75 (assuming the value is between 0 and 1, with higher values ​​indicating greater hazard).

[0080] It is necessary to evaluate the impact of the current location of layer cracks on the safety of underground projects. This usually requires considering the distance and direction between the cracks and key structures (such as underground pipelines, support structures, etc.), as well as whether the cracks are located on potential slip surfaces or weak zones. In addition, it is also necessary to analyze whether the current morphology of the cracks is complex, such as whether multiple branches appear, whether cross structures are formed, etc. The more complex the morphology, the greater the impact of the cracks on the safety of underground projects. Based on the evaluation results of the positional impact and morphological complexity, a predetermined hazard factor calculation formula or evaluation model is used to determine the second sub-hazard factor.

[0081] Optionally, the assessment found that the current position of the layer crack is close to the layer wall (about 5 meters), and the crack direction is roughly parallel to the layer axis; in addition, the crack also forms a "T"-shaped cross structure with a relatively complex shape; based on this information, using the predetermined hazard factor calculation formula, the second sub-hazard factor is obtained to be 0.80 (also assuming that the value is between 0-1); in summary, through the detailed analysis and calculation of step S142, the first sub-hazard factor and the second sub-hazard factor of the layer crack are obtained, and these coefficients will be used for subsequent comprehensive hazard factor calculation and risk assessment.

[0082] Therefore, the hazard level mapping relationship is collected, and the hazard level of the layer cracks is determined based on the current morphology of the layer cracks and the hazard level mapping relationship. The hazard coefficient of the layer cracks is determined according to the hazard level of the layer cracks, the first sub-hazard coefficient and the second sub-hazard coefficient. This is compatible with the overall consideration of the hazard level of the layer cracks, the first sub-hazard coefficient and the second sub-hazard coefficient, and ensures the accuracy of the hazard coefficient of the layer cracks.

[0083] At this time, relevant data on the historical hazards of layer cracks are collected, including the morphological characteristics of the cracks (such as length, width, direction, branching, etc.), location information (distance from key structures, groundwater level, etc.), and records of geological disasters or engineering accidents caused by these cracks; based on the collected historical data, it is necessary to establish a mapping relationship between the morphological characteristics of the layer cracks and the hazard level; this usually involves statistical analysis of the data to determine which morphological characteristics are associated with higher hazards.

[0084] After the mapping relationship is established, it is necessary to use a portion of independent historical data to verify its accuracy; this is done by comparing the hazard level predicted by the mapping relationship with the hazard level of actual geological disasters or engineering accidents.

[0085] Specifically, in a certain area, historical data on geological disasters caused by layer cracks in the past ten years were collected; by analyzing these data, it was found that cracks with a length of more than 30 meters, a width of more than 0.5 meters, and a distance of less than 10 meters from key structures (such as underground layers) are more likely to cause geological disasters; based on this information, a mapping relationship between crack morphological characteristics and hazard levels was established, and the hazard levels were divided into three levels: low, medium, and high.

[0086] It is necessary to evaluate the current morphology of the layer cracks, including length, width, direction, branching, etc.; determine the hazard level of the layer cracks based on the current morphology obtained from the evaluation and the previously established hazard level mapping relationship; in a specific project in the above-mentioned area, it was found that a layer crack currently has a length of 40 meters and a width of 0.6 meters, and is 8 meters away from an important layer; according to the previously established hazard level mapping relationship, this crack was judged to be at a high risk level.

[0087] The hazard coefficient of the layer cracks is determined according to the hazard level of the layer cracks, the first sub-hazard coefficient and the second sub-hazard coefficient, which is compatible with the matching of the hazard level of the layer cracks, the first sub-hazard coefficient and the second sub-hazard coefficient, and reflects the influence of multiple factors such as crack morphology, change rate and position on the hazard; at this time, the layer cracks have been determined to be at a high risk level, the first sub-hazard coefficient is 0.75 (calculated based on the morphological change rate and change duration of the cracks), and the second sub-hazard coefficient is 0.80 (calculated based on the location and morphological complexity assessment of the cracks), so the comprehensive hazard coefficient of the crack is calculated to be 0.85; this means that the crack is very dangerous and emergency measures need to be taken to deal with it.

[0088] In one embodiment of the present application, a danger level matching table is collected, and the danger level matching table is shown in Table 3:

[0089] The hazard level of the layer cracks is determined based on their current morphology (such as length, width, distance from key structures, etc.) and the above-mentioned matching table. At the same time, the hazard level of the layer cracks, the first sub-hazard coefficient and the second sub-hazard coefficient are comprehensively considered to determine their comprehensive hazard coefficient. This usually involves assigning a weight to each factor and calculating scores based on the values ​​of these factors. Finally, the weighted sum of these scores is used to obtain the comprehensive hazard coefficient.

[0090] The weight distribution table is shown in Table 4:

[0091] Comprehensive risk factor = risk level score × risk level weight + first sub-risk factor × first sub-risk factor weight + second sub-risk factor × second sub-risk factor weight = 2 × 0.5 + 0.75 × 0.3 + 0.6 × 0.2 = 1 + 0.225 + 0.12 = 1.345.

[0092] refer to Figure 6 In step S15, a dangerous event of the underground layer is determined according to the positions of the plurality of layer cracks, the risk coefficient of each layer crack and the shape of the three-dimensional model of the underground layer, and a real-time early warning of the layer crack is triggered; In the specific implementation process of the present invention, the specific steps are: S151: monitoring the locations of multiple layer cracks in real time, determining multiple environmental parameters based on the environmental detection of the locations of the multiple layer cracks, and determining the environmental type of the underground layer based on the multiple environmental parameters and the underground layer; S152: Determine a first risk factor based on the type of the underground layer environment and the risk coefficient of each layer crack, and determine a second risk factor of the underground layer based on the risk coefficient of each layer crack and the shape of the three-dimensional model of the underground layer; S153: Determine the dangerous events of the underground layer according to the mapping relationship between the first dangerous factor, the second dangerous factor and the dangerous events, and trigger a corresponding early warning signal based on the dangerous events of the underground layer. The early warning signal is used to achieve real-time early warning of layer cracks.

[0093] In an embodiment of the present application, the positions of multiple layer cracks are monitored in real time, multiple environmental parameters are determined based on environmental detection of the positions of multiple layer cracks, and the environmental type of the underground layer is determined based on the multiple environmental parameters and the underground layer. This combines the overall consideration of multiple environmental parameters and the underground layer to ensure the accuracy of the environmental type of the underground layer.

[0094] At this time, advanced monitoring technologies and equipment are used to track and record the location information of multiple layer cracks in real time; these technologies include but are not limited to geological radar, fiber optic sensors, GPS positioning systems, etc.; through continuous data collection, the changes in the crack position over time are obtained, so as to understand the activity status and trend of the cracks. At this time, the propagation characteristics of electromagnetic waves in the stratum are used to detect the location and shape of underground cracks by transmitting and receiving reflected waves; by arranging an underground fiber optic network, the sensitivity of optical fiber to temperature and strain is used to monitor the expansion and activity of cracks; for cracks on the surface or near the surface, a GPS positioning system is used to accurately measure their position coordinates.

[0095] After determining the location of the layer cracks, further environmental testing is required to obtain multiple environmental parameters related to the crack location; these parameters include groundwater level, soil moisture, temperature, gas composition (such as oxygen, carbon dioxide, methane, etc.), geological structure characteristics (such as rock layer inclination, fault distribution), etc.; these parameters are crucial for understanding the causes of crack formation, evaluating their impact on underground engineering safety, and predicting their future development trends; at this time, use water level meters or water level sensors to monitor changes in groundwater levels to understand whether the cracks are related to groundwater activity; use hygrometers and thermometers to measure the humidity and temperature of the soil around the cracks to evaluate their impact on crack activity; use gas detectors to detect the gas composition around the cracks to determine whether there is any harmful gas leakage or gas generated by geological activity; through geological exploration and geological mapping, understand the geological structure characteristics of the area where the cracks are located, such as rock layer inclination, fault distribution, etc.

[0096] After obtaining multiple environmental parameters, it is necessary to combine the geological characteristics and engineering background of the underground layer and apply professional knowledge in fields such as geology and environmental science to determine the environmental type of the underground layer; these environmental types include karst environment, weak interlayer environment, high stress environment, aquifer environment, etc.; the determination of environmental type helps to better understand the formation mechanism of cracks, evaluate their impact on the safety of underground projects, and formulate corresponding response measures; at this time, if the groundwater level fluctuates greatly, the soil moisture is high, and there are caves or underground rivers and other characteristics, it is a karst environment; if the crack is located between two layers of hard rock and there are weak interlayers (such as mudstone, shale, etc.) between the rock layers, it is a weak interlayer environment; if the underground layer is in a high tectonic stress area and the crack morphology is complex and the expansion speed is fast, it is a high stress environment; if the groundwater level is stable and the soil moisture around the crack is high and the water content is large, it is an aquifer environment.

[0097] Specifically, geological radar and fiber optic sensors were used to monitor the locations of multiple layer cracks around the layer in real time. Through continuous data collection and analysis, they found that a crack located near the side wall of the layer was gradually expanding. In order to gain a deeper understanding of the formation mechanism of this crack and its impact on the safety of the layer, further environmental testing was carried out. They found that the groundwater level in the area was high and fluctuated greatly, the soil moisture was high and there were cave characteristics. Based on these environmental parameters and the geological characteristics of the underground layer, it was judged that the area belonged to a karst environment. Therefore, they formulated corresponding response measures to strengthen the support and waterproofing work of the layer to ensure construction safety.

[0098] Furthermore, the first risk factor is determined based on the environmental type of the underground layer and the risk coefficient of each layer crack, and the second risk factor of the underground layer is determined according to the risk coefficient of each layer crack and the morphology of the three-dimensional model of the underground layer. This is compatible with the overall consideration of the risk coefficient of each layer crack and the morphology of the three-dimensional model of the underground layer, ensuring the accuracy of the second risk factor of the underground layer.

[0099] At this time, the first risk factor is comprehensively evaluated and determined based on the environmental type of the underground layer (such as karst environment, weak interlayer environment, high stress environment, etc.) and the previously calculated risk factors of each layer crack; the first risk factor mainly reflects the degree of influence of the environmental type on the development of layer cracks and the occurrence of potential geological disasters; at this time, during specific operations, a risk assessment matrix or model will be formulated based on the characteristics of the environmental type and the risk factors of the layer cracks; in this matrix or model, different combinations of environmental types and risk factors will correspond to different risk levels or first risk factor values; by consulting this matrix or model, the first risk factor of the current underground layer can be quickly determined.

[0100] Optionally, first develop a risk assessment matrix based on historical data and expert experience; the horizontal axis of the matrix represents the type of environment, and the vertical axis represents the range of hazard factors of layer cracks; each intersection corresponds to a risk level or a first hazard factor value; after determining the environmental type of the current underground layer and the hazard factors of each layer crack, consult the risk assessment matrix to find the corresponding risk level or first hazard factor value.

[0101] The second risk factor is determined by taking into account the risk coefficient of each layer crack and the three-dimensional model morphology of the underground layer (such as stratum dip, fold morphology, fault distribution, etc.); the second risk factor mainly evaluates the structural stability of the underground layer and the direct threat of layer cracks to the safety of underground projects; in specific operations, three-dimensional geological modeling software will be used to construct a three-dimensional model of the underground layer, and the position and risk coefficient of each layer crack will be marked in the model; then, by analyzing the morphological characteristics of the model and the distribution of cracks, the structural stability of the underground layer and the potential geological disaster risk are evaluated, thereby determining the second risk factor.

[0102] At this time, three-dimensional geological modeling software is used to construct a three-dimensional model of the underground layer based on geological exploration data and geological mapping results; the location and hazard coefficient of the cracks in each layer are marked in the model, and the distribution, morphological characteristics and relationship of the cracks with the structure of the underground layer are analyzed; based on the analysis results of the cracks and the structural characteristics of the underground layer, the potential geological disaster risks are evaluated and the second risk factor is determined.

[0103] Specifically, in a certain underground layer project, it has been determined that the underground layer belongs to a karst environment, and the risk factors of the cracks in each layer have been calculated; next, they follow the steps of S152 to determine the first risk factor and the second risk factor.

[0104] Determination of the first risk factor: After consulting the previously developed risk assessment matrix, they found that the risk level corresponding to the combination of the hazard factors of the karst environment and the current layer fractures was "high"; therefore, they determined the first risk factor to be "high karst activity risk."

[0105] Determination of the second risk factor: A three-dimensional model of the underground layer was constructed using three-dimensional geological modeling software, and the location and risk factor of each layer crack were marked in the model; by analyzing the model, they found that the layer passed through a karst development area, and the strata in this area had a large dip angle and multiple faults; in addition, some layer cracks intersected with faults, forming potential slip surfaces; based on these analysis results, it was assessed that the area had a high risk of stratum structural instability; therefore, they determined the second risk factor to be "stratum structural instability risk"; in summary, through the steps of S152, the environmental type of the underground layer, the risk factor of the layer cracks and the three-dimensional model morphology of the underground layer were comprehensively evaluated, and the first and second risk factors were determined, which provided an important reference basis for the subsequent underground engineering design and construction.

[0106] Therefore, the dangerous events of the underground layer are determined according to the mapping relationship between the first dangerous factor, the second dangerous factor and the dangerous event, and the corresponding early warning signal is triggered based on the dangerous events of the underground layer. The early warning signal is used to realize real-time early warning of layer cracks, and is compatible with the overall consideration of the mapping relationship between the first dangerous factor, the second dangerous factor and the dangerous event, ensuring the accuracy of the dangerous events of the underground layer. At the same time, it is compatible with the dynamic change diagram of the layer cracks, and the dynamic changes of the layer cracks are monitored in real time, ensuring the dynamic monitoring of the dangerous events of the underground layer and realizing real-time early warning of the layer cracks.

[0107] At this time, the hazardous events occurring in the underground layer are determined by combining the previously determined first hazard factor (such as high karst activity risk), the second hazard factor (such as the risk of unstable stratum structure) and a pre-established hazardous event mapping relationship; the hazardous event mapping relationship is a mapping table or model developed based on historical data, expert experience and scientific analysis, which is used to match hazardous factors with specific hazardous events; during specific operations, the hazardous event mapping relationship will be consulted to find the hazardous event corresponding to the current combination of the first hazard factor and the second hazard factor; this hazardous event is layer collapse, ground subsidence, groundwater inrush, rock fracture, etc., which depends on the geological characteristics of the underground layer, the engineering background and the type of hazardous factor.

[0108] At this time, a hazardous event mapping relationship table is developed based on historical data; the table lists different hazardous factor combinations and corresponding hazardous events; after determining the first hazardous factor and the second hazardous factor of the current underground layer, the hazardous event mapping relationship table is consulted to find the corresponding hazardous event.

[0109] Once a dangerous event in the underground layer is determined, the corresponding early warning signal needs to be triggered immediately; the early warning signal is in various forms such as sound and light alarms, text message notifications, and remote monitoring platform alarms, depending on the actual situation at the project site and the configuration of the early warning system; the purpose of the early warning signal is to promptly notify relevant personnel to pay attention to dangerous situations and take necessary response measures to avoid or reduce potential geological disasters or engineering accidents; at this time, an early warning system is set up at the project site or remote monitoring center, including alarm devices, communication equipment, etc.; after the dangerous event is determined, the corresponding early warning signal is triggered through the early warning system; for example, if the dangerous event is the risk of layer collapse, the sound and light alarm and text message notification are triggered to notify the construction personnel in the layer to evacuate immediately.

[0110] Specifically, in a certain underground layer construction project, the first risk factor has been determined to be "high karst activity risk" and the second risk factor is "stratum structure instability risk"; next, they follow the steps of S153 to determine the dangerous events and trigger the early warning signal.

[0111] After consulting the hazardous event mapping relationship table, they found that the hazardous event corresponding to the combination of "high karst activity risk" and "stratum structural instability risk" is "layer collapse risk"; therefore, they determined that the hazardous event currently occurring in the underground layer is layer collapse; at the same time, after determining the hazardous event, they immediately triggered the early warning signal; they issued an emergency alarm through the sound and light alarm device in the layer, and sent an emergency evacuation notice to the layer construction personnel through the SMS notification system; at the same time, the remote monitoring center also received the alarm information, and immediately activated the emergency plan and dispatched a rescue team to the scene.

[0112] In one embodiment of the present application, a dangerous event matching table is collected, and the dangerous event matching table is shown in Table 5:

[0113] In this example, if the first hazard factor is "high karst activity risk" and the second hazard factor is "stratum structure instability risk", then according to the hazard event matching table, the hazard event of the underground layer is determined to be "layer collapse risk".

[0114] See also Figure 7 , Figure 7 : is a schematic diagram of the structure of a serial communication system based on a data buffer in an embodiment of the present invention; the serial communication system based on a data buffer includes: A three-dimensional model module 21 is used to determine a three-dimensional model of the underground layer according to the spatial position of the underground layer and the distribution map of the underground layer; an electrode detection module 22 for determining a plurality of electrode nodes of a plurality of underground layers based on traversing the three-dimensional model of the underground layer, and triggering electrode detection of the underground layer according to positions of the plurality of electrode nodes and a shape of the three-dimensional model of the underground layer; A layer fracture module 23 is configured to collect multiple potential differences based on electrode detection of the underground layer, and determine a three-dimensional resistivity distribution map based on the multiple potential differences and a three-dimensional model of the underground layer to determine corresponding layer fractures; The dynamic monitoring module 24 is used to collect a dynamic change diagram of the layer cracks based on the dynamic monitoring of the location of the layer cracks, and determine the risk factor of the layer cracks based on the dynamic change diagram of the layer cracks; The dangerous event module 25 is used to determine dangerous events of the underground layer according to the positions of multiple layer cracks, the dangerous coefficients of each layer crack and the shape of the three-dimensional model of the underground layer, and trigger real-time warning of the layer cracks.

[0115] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging, characterized in that: include: Determine a three-dimensional model of the underground layer body according to the spatial position of the underground layer body and the distribution map of the underground layer body; Determining a plurality of electrode nodes of a plurality of underground layers based on traversing the three-dimensional model of the underground layer, and triggering electrode detection of the underground layer according to positions of the plurality of electrode nodes and the shape of the three-dimensional model of the underground layer; A plurality of potential differences are collected by electrode detection of the underground layer, and a three-dimensional resistivity distribution map is determined based on the plurality of potential differences and a three-dimensional model of the underground layer to determine corresponding layer fractures; According to the dynamic monitoring of the position of the layer crack, a dynamic change diagram of the layer crack is collected, and the risk factor of the layer crack is determined according to the dynamic change diagram of the layer crack; According to the positions of multiple layer cracks, the hazard coefficients of each layer crack and the morphology of the three-dimensional model of the underground layer, dangerous events of the underground layer are determined, and real-time early warning of the layer cracks is triggered.

2. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 1, characterized in that: Determining the three-dimensional model of the underground layer according to the spatial position of the underground layer and the distribution map of the underground layer includes: Performing position detection on the underground layer body and collecting the spatial position of the underground layer body, and determining the spatial area corresponding to the underground layer body based on the spatial position of the underground layer body and the shape of the underground layer body; Collecting a database of underground layers, determining a distribution map of the underground layers based on the spatial position of the underground layers and the database of the underground layers, and determining an area to be modeled based on the distribution map of the underground layers and the spatial area corresponding to the underground layers; In the area to be modeled, the three-dimensional model of the underground layer is determined based on the synthesis of the morphology of the underground layer and the real-time image of the underground layer. The abnormal area is determined based on the detection of the three-dimensional model of the underground layer, and the review of the abnormal area is triggered to improve the three-dimensional model of the underground layer.

3. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 1, characterized in that: The method of determining a plurality of electrode nodes of a plurality of underground layers based on traversing the three-dimensional model of the underground layer, and triggering electrode detection of the underground layer according to positions of the plurality of electrode nodes and the shape of the three-dimensional model of the underground layer, includes: Collecting a three-dimensional model of the underground layer, and determining a plurality of detection areas according to the traversal of the three-dimensional model of the underground layer, wherein two adjacent detection areas have corresponding layer boundary lines; In each detection area, a corresponding electrode node is determined based on the spatial position, area and shape of the detection area, and the electrode node is used to supply a power supply to perform electrode detection on the underground layer; The shape of the three-dimensional model of the underground layer is collected, and the electrode detection mode of the underground layer is determined based on the matching of the shape of the three-dimensional model of the underground layer and the positions of multiple electrode nodes. Multiple electrode nodes are triggered along the electrode detection mode to perform electrode detection on the underground layer.

4. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 1, characterized in that: The method of collecting multiple potential differences based on electrode detection of the underground layer, and determining a three-dimensional resistivity distribution map based on the multiple potential differences and a three-dimensional model of the underground layer to determine corresponding layer fractures includes: Real-time detection of underground layer body by electrode detection. Under the action of electrode detection of multiple electrode nodes, the underground layer body has multiple potentials. The multiple potentials are distributed at different positions of the underground layer body and form corresponding potential differences to collect multiple potential differences in the underground layer body.

5. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 4, characterized in that: The method further includes collecting a plurality of potential differences according to electrode detection of the underground layer, and determining a three-dimensional resistivity distribution map according to the plurality of potential differences and a three-dimensional model of the underground layer to determine corresponding layer fractures. The multiple potential differences are mapped to corresponding positions of the underground layer to form a three-dimensional resistivity distribution map, and multiple fracture detection areas are determined based on the detection of the three-dimensional resistivity distribution map; The crack identification method of the crack detection area is determined based on the relative positions of the multiple crack detection areas, the regional morphology of the multiple crack detection areas and the previous data of the underground layer, and the multiple crack detection areas are synchronously identified along the crack identification method to output the corresponding layer cracks. The layer cracks have different morphologies and present corresponding morphologies based on different potential differences.

6. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 1, characterized in that: The method of collecting a dynamic change diagram of the layer cracks based on the dynamic monitoring of the location of the layer cracks and determining the risk factor of the layer cracks based on the dynamic change diagram of the layer cracks includes: The locations of the stratum fractures are collected and dynamically monitored. Based on the dynamic monitoring of the locations of the stratum fractures, multiple change nodes of the stratum fractures are determined. Based on the synthesis of the multiple change nodes of the stratum fractures, a dynamic change diagram of the stratum fractures is determined.

7. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 6, characterized in that: The method of collecting a dynamic change diagram of the layer cracks based on the dynamic monitoring of the location of the layer cracks and determining the risk factor of the layer cracks based on the dynamic change diagram of the layer cracks also includes: Determine the current form of the layer cracks according to the dynamic change diagram of the layer cracks, and determine the first sub-risk factor based on the current form of the layer cracks and the duration of the change of the layer cracks; determine the second sub-risk factor based on the current form of the layer cracks and the position of the layer cracks; The hazard level mapping relationship is collected, and the hazard level of the layer cracks is determined based on the current form of the layer cracks and the hazard level mapping relationship, and the hazard coefficient of the layer cracks is determined according to the hazard level of the layer cracks, the first sub-hazard coefficient and the second sub-hazard coefficient.

8. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 1, characterized in that: The method of determining a dangerous event of an underground layer according to the positions of multiple layer cracks, the risk coefficient of each layer crack, and the shape of a three-dimensional model of the underground layer, and triggering a real-time early warning of the layer cracks, includes: Real-time monitoring of the locations of multiple layer cracks is performed, multiple environmental parameters are determined based on the environmental detection of the locations of the multiple layer cracks, and the type of environment of the underground layer is determined based on the multiple environmental parameters and the underground layer; The first risk factor is determined based on the environmental type of the underground layer and the risk coefficient of each layer crack, and the second risk factor of the underground layer is determined based on the risk coefficient of each layer crack and the shape of the three-dimensional model of the underground layer.

9. The method for dynamic monitoring of layer cracks based on three-dimensional resistivity imaging according to claim 8, characterized in that: The method of determining a dangerous event of the underground layer according to the positions of the plurality of layer cracks, the risk factor of each layer crack and the shape of the three-dimensional model of the underground layer, and triggering a real-time early warning of the layer cracks, further includes: The dangerous events of the underground layer are determined according to the mapping relationship between the first dangerous factor, the second dangerous factor and the dangerous events, and a corresponding early warning signal is triggered based on the dangerous events of the underground layer. The early warning signal is used to achieve real-time early warning of layer cracks.

10. A serial communication system based on a data buffer, characterized in that: The serial communication system based on the data buffer is applied to the dynamic monitoring method of layer fractures based on three-dimensional resistivity imaging as described in any one of claims 1 to 9, and the serial communication system based on the data buffer includes: A three-dimensional model module, used for determining a three-dimensional model of the underground layer according to the spatial position of the underground layer and the distribution map of the underground layer; an electrode detection module, configured to determine a plurality of electrode nodes of a plurality of underground layers based on traversal of the three-dimensional model of the underground layer, and trigger electrode detection of the underground layer according to positions of the plurality of electrode nodes and a shape of the three-dimensional model of the underground layer; A layer fracture module is used to collect multiple potential differences based on electrode detection of underground layers, and determine a three-dimensional resistivity distribution map based on the multiple potential differences and a three-dimensional model of the underground layers to determine the corresponding layer fractures; A dynamic monitoring module is used to collect a dynamic change diagram of the layer cracks based on the dynamic monitoring of the location of the layer cracks, and determine the risk factor of the layer cracks based on the dynamic change diagram of the layer cracks; The dangerous event module is used to determine dangerous events of underground layers according to the positions of multiple layer cracks, the dangerous coefficients of each layer crack and the shape of the three-dimensional model of the underground layer, and trigger real-time early warning of layer cracks.

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