Three-dimensional resistivity rapid imaging method for bench slope structure surface of open-pit mine

Abstract

This application discloses a rapid three-dimensional resistivity imaging method for the structural surfaces of open-pit mine bench slopes, belonging to the field of mine engineering safety monitoring technology. The method includes: using a multi-channel distributed resistivity imager to perform full-array four-electrode combined measurements on a two-dimensional gridded electrode array deployed on the open-pit mine bench slope, obtaining a full electric field observation dataset; based on the full electric field observation dataset, performing three-dimensional resistivity inversion to obtain a three-dimensional resistivity model characterizing the resistivity distribution of the rock and soil mass beneath the open-pit mine bench slope; and performing three-dimensional visualization imaging on at least one structural surface anomaly identified from the three-dimensional resistivity model, and the corresponding attitude parameters of each structural surface anomaly, to obtain a three-dimensional image of the structural surfaces characterizing the distribution features of the internal structural surfaces of the open-pit mine bench slope; wherein the attitude parameters include the dip parameter, dip angle parameter, and strike parameter of the structural surface anomaly. This application can significantly improve the effectiveness and accuracy of open-pit mine bench slope structural surface detection.

Description

Technical Field

[0001] This application relates to the field of mine engineering safety monitoring technology, and in particular to a rapid three-dimensional resistivity imaging method for the structural surface of an open-pit mine bench slope. Background Technology

[0002] Open-pit mine benches are the basic units of mine production, and the attitude, density, and spatial distribution of their structural surfaces (including weak structural surfaces such as joints, fissures, bedding, faults, and fracture zones) are key factors controlling slope stability. During open-pit mining, the degree of development of slope structural surfaces directly affects slope stability; areas with well-developed structural surfaces are highly susceptible to geological disasters such as landslides and collapses, seriously threatening safe mine production. Therefore, accurate and efficient detection and imaging of bench slope structural surfaces are of great significance for mine slope stability assessment and disaster early warning.

[0003] Existing methods for detecting structural surfaces on stepped slopes primarily employ geological compass measurements, core drilling, 3D laser scanning, and ground-penetrating radar. However, these methods all rely on the surface exposure of the structural surfaces in practical applications. For concealed structural surfaces within the slope that have not yet been exposed, these methods are completely ineffective. Yet, it is precisely these unexposed but developed structural surfaces that pose a potential threat to slope stability—once they reach a critical state, they may trigger sudden landslides without warning. This fundamental deficiency severely restricts the effectiveness of structural surface detection on mine slopes. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method for rapid three-dimensional resistivity imaging of the structural surface of an open-pit mine bench slope.

[0005] According to one aspect of this application, a rapid three-dimensional resistivity imaging method for the structural surface of an open-pit mine bench slope is provided, comprising: A multi-channel distributed resistivity imager was used to perform full array quad-electrode combination measurements on a two-dimensional gridded electrode array deployed on the slope of an open-pit mine bench, resulting in a full electric field observation dataset. Based on the full electric field observation dataset, a three-dimensional resistivity inversion was performed to obtain a three-dimensional resistivity model characterizing the resistivity distribution of the rock and soil mass below the slope of the open-pit mine bench. Three-dimensional visualization imaging is performed on at least one structural surface anomaly identified from the three-dimensional resistivity model, and the attitude parameters corresponding to each structural surface anomaly, to obtain a three-dimensional image of the structural surface that characterizes the distribution characteristics of the internal structural surfaces of the open-pit mine bench slope; wherein, the attitude parameters include the dip parameter, dip angle parameter and strike parameter of the structural surface anomaly.

[0006] Compared with existing technologies, this application has significant advantages: To address the problem that existing methods for detecting structural surfaces on mine bench slopes rely on the exposure of these surfaces at the surface, severely limiting the effectiveness of such detection, this application provides a rapid three-dimensional resistivity imaging method for open-pit mine bench slope structures. This method employs a multi-channel distributed resistivity imager to perform full-array quad-electrode combined measurements on a two-dimensional gridded electrode array deployed on the open-pit mine bench slope to obtain a full electric field observation dataset. This eliminates the dependence on surface exposure of the structural surfaces, instead relying on resistivity differences in the underground medium for detection, effectively identifying hidden structural surfaces within the slope. Furthermore, by analyzing the full resistivity of the entire array... The electric field observation dataset is used to perform three-dimensional resistivity inversion to obtain a three-dimensional resistivity model characterizing the resistivity distribution of the soil and rock mass beneath the slope. This enables spatial reconstruction from surface measurement data to the underground three-dimensional resistivity distribution, providing a three-dimensional data foundation for the identification of concealed structural surfaces. Based on this, three-dimensional visualization imaging is performed on the structural surface anomalies and their attitude parameters identified from the three-dimensional resistivity model to obtain three-dimensional images of structural surfaces characterizing the distribution features of internal structural surfaces on the slope. This transforms abstract three-dimensional resistivity data into intuitive spatial distribution images of structural surfaces, realizing a complete detection link from data acquisition to three-dimensional imaging that is independent of the structural surface exposure conditions.

[0007] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0008] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 The illustration shows a flowchart of a rapid three-dimensional resistivity imaging method for the slope structure of an open-pit mine bench, as provided in an embodiment of this application. Detailed Implementation

[0009] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0010] This embodiment provides a method for rapid three-dimensional resistivity imaging of the slope structure of an open-pit mine bench, such as... Figure 1 As shown, steps 110-130 are included. Specifically: S110: A multi-channel distributed resistivity imager is used to perform full array quad-electrode combination measurements on a two-dimensional gridded electrode array deployed on the slope of an open-pit mine bench, obtaining a full electric field observation dataset.

[0011] Here, the two-dimensional gridded electrode array is deployed in the following manner: according to a preset deployment method, multiple stainless steel needle-shaped electrodes are inserted into the soil of the open-pit mine bench slope; each stainless steel needle-shaped electrode has a unique number; the preset deployment method includes: deploying M electrode columns along the slope direction of the open-pit mine bench at a first electrode spacing, deploying N electrode columns along the slope inclination of the open-pit mine bench at a second electrode spacing, and inserting a stainless steel needle-shaped electrode at each intersection of the M electrode columns and the N electrode rows to form a two-dimensional gridded electrode array of M rows and N columns, where M≥10 and N≥6.

[0012] Here, the spacing between the first electrodes is 1m to 3m, the spacing between the second electrodes is 1m to 3m, the length of the stainless steel needle electrodes is 20cm to 40cm, and the insertion depth is not less than 2 / 3 of the electrode length, so as to ensure good electrical contact between the electrodes and the soil and rock and reduce the grounding resistance.

[0013] For example, in the actual layout of a bench slope in a large open-pit iron mine, the bench height is 12m, the slope angle is 65°, and the slope length is 150m. 30 electrode rows are arranged along the slope direction (X-direction) with a first electrode spacing of 2m, and 7 electrode rows are arranged along the slope dip direction (Y-direction) with a second electrode spacing of 2m. A 30cm long stainless steel needle electrode is inserted at each of the 30×7=210 intersection points to a depth of 20cm, forming a 30-row × 7-column two-dimensional gridded electrode array. Each stainless steel needle electrode in the two-dimensional gridded electrode array is assigned a unique number (e.g., by row or column). This unique number is associated with the electrode's spatial position in the two-dimensional gridded electrode array (i.e., the row and column coordinates of the electrode in the slope direction and dip direction), so that the electrode switching control module can automatically identify and select the corresponding electrode based on the number during subsequent data acquisition.

[0014] After the two-dimensional gridded electrode array was deployed, a multi-channel distributed resistivity imager was used to perform a full array quadrupole combination measurement, which included: (1) The electrode switching control module in the multi-channel distributed resistivity imager is used to select four-electrode combinations from the two-dimensional gridded electrode array according to the preset scanning sequence. Each four-electrode combination includes a first power supply electrode, a second power supply electrode, a first measurement electrode, and a second measurement electrode.

[0015] Here, quadrupole electrode combinations are selected sequentially from the two-dimensional gridded electrode array according to a preset scanning sequence, including: selecting all quadrupole electrode combinations that satisfy the preset device type geometric constraints in ascending order of the unique number corresponding to each stainless steel needle electrode from the two-dimensional gridded electrode array; wherein, the preset device type includes the Wenner quadrupole device type or the dipole-dipole device type; when the preset device type is the Wenner quadrupole device type, the first power supply electrode, the second power supply electrode, the first measuring electrode, and the second measuring electrode in each selected quadrupole electrode combination are arranged at equal intervals along the same straight line; when the preset device type is the dipole-dipole device type, the first power supply electrode and the second power supply electrode in each selected quadrupole electrode combination constitute a power supply dipole, and the first measuring electrode and the second measuring electrode constitute a measuring dipole, the power supply dipole and the measuring dipole are arranged along the same straight line, and the distance between the power supply dipole and the measuring dipole is n times the distance between the first power supply electrode and the second power supply electrode, where n is a preset positive integer (e.g., 2).

[0016] For example, in a 30-row × 7-column two-dimensional gridded electrode array, each stainless steel needle electrode is numbered using a row and column numbering method: starting from the electrode at the upper left corner of the slope (numbered 1,1), the electrodes are numbered sequentially from left to right along the direction of the slope as (1,1), (1,2), (1,3), ..., (1,30), and sequentially from top to bottom along the direction of the slope as (1,1), (2,1), (3,1), ..., (7,30). The unique number of each electrode is associated with its spatial position in the two-dimensional gridded electrode array.

[0017] Based on the aforementioned array of 210 electrodes, a multi-channel distributed resistivity imager was used to perform full array quadrupole combination measurements. The electrode switching control module in the multi-channel distributed resistivity imager sequentially selected all quadrupole electrode combinations that met the preset device type geometric constraints from the two-dimensional gridded electrode array, following the ascending order of the unique numbers corresponding to each stainless steel needle electrode (e.g., row-wise ascending, i.e., (1,1), (1,2), ..., (1,30), (2,1), (2,2), ...).

[0018] Taking the Wenner quadrupole device as an example, the electrode switching control module sequentially selects four electrodes that meet the equidistant spacing condition along the direction of orientation as a quadrupole combination. Specifically, for the first row of electrodes (numbered 1,1 to 1,30), with an electrode spacing of 2m, (1,1), (1,2), (1,3), and (1,4) are selected sequentially as the first group, (1,2), (1,3), (1,4), and (1,5) as the second group, and so on, for a total of 27 groups; then the same sliding selection is performed on the second row, the third row, and so on up to the seventh row. Similarly, the same sliding selection is performed along the orientation direction. After all traversals are completed, approximately 309 quadrupole electrode combinations are selected in the 30-row × 7-column two-dimensional gridded electrode array.

[0019] (2) For each quadrupole electrode combination, current is injected into the ground through the first power supply electrode and the second power supply electrode, and the potential difference is collected through the first measuring electrode and the second measuring electrode. The corresponding power supply current value and the measured potential difference value are recorded as a measurement record; wherein, each measurement record is associated with the spatial location information of a quadrupole electrode combination.

[0020] It should be noted that the supply current value and the measured potential difference value are physical quantities obtained under the same set of quadrupole electrode combinations and the same power supply acquisition sequence, and there is a one-to-one correspondence between them. That is, while injecting current into the ground through the currently selected first and second supply electrodes, the potential difference is acquired through the currently selected first and second measuring electrodes. The recorded supply current value and measured potential difference value are strictly synchronized in time and spatially associated with the same set of quadrupole electrode combinations, together forming a complete measurement record. Furthermore, the spatial location information of each quadrupole electrode combination includes the spatial coordinates of the current first supply electrode, the current second supply electrode, the current first measuring electrode, and the current second measuring electrode.

[0021] In other words, a single power supply-measurement process for the same set of quadrupole electrodes generates one and only one corresponding measurement record. The data items in this record (power supply current value, measured potential difference value, and spatial coordinates of the four electrodes) all originate from the same physical acquisition, are indivisible, and correspond one-to-one.

[0022] Building upon this foundation, the multi-channel distributed resistivity imager can simultaneously acquire the potential differences of multiple sets of measuring electrodes through multiple channels while being powered on. Specifically, during the injection of current into the ground using a selected set of power supply electrodes (the first and second power supply electrodes), the multi-channel distributed resistivity imager utilizes its multi-channel parallel acquisition architecture to simultaneously acquire the potential difference values ​​between multiple pairs of measuring electrodes through multiple receiving channels. Each pair of measuring electrodes corresponds to one channel, and each channel operates independently and in parallel. Thus, a single power supply can simultaneously acquire multiple potential difference values ​​corresponding to multiple pairs of measuring electrodes. Each set of potential difference values, along with the same power supply current value and the spatial location information of its corresponding quadrupole electrode combination, constitutes an independent measurement record. Consequently, before the electrode switching control module of the multi-channel distributed resistivity imager switches to the next quadrupole electrode combination according to a preset scanning sequence, multiple measurement records can be generated with a single power supply, significantly reducing the total number of power supply cycles and the total acquisition time, achieving rapid acquisition of full-array quadrupole combination measurements.

[0023] For example, taking a power supply acquisition in a 30-row × 7-column two-dimensional gridded electrode array as an example, assume that the electrode switching control module selects the current quadrupole electrode combination according to the preset scanning sequence: the first power supply electrode is the electrode numbered (3,5), the second power supply electrode is the electrode numbered (3,8) (the power supply dipole pitch is 6m), and the multi-channel distributed resistivity imager is configured with 8 parallel acquisition channels. While injecting current into the ground through the first power supply electrode (3,5) and the second power supply electrode (3,8) (for example, the power supply current value is 120mA), the 8 receiving channels of the multi-channel distributed resistivity imager simultaneously acquire the potential difference of 8 pairs of measuring electrodes. For example, channel 1 acquires the potential difference ΔV1 between measuring electrode pairs (3,9) and (3,10), channel 2 acquires the potential difference ΔV2 between measuring electrode pairs (3,10) and (3,11), ..., channel 8 acquires the potential difference ΔV8 between measuring electrode pairs (3,16) and (3,17).

[0024] All eight pairs of measuring electrodes are located in the same row (row 3) and on the same side of the power supply electrodes (3,8), thus obtaining eight sets of measurement data with different offsets simultaneously during a single power supply process. Although the eight measurement records share the same power supply current value of 120mA, they each correspond to different measuring electrode pairs and are associated with different spatial position information of quadrupole electrode combinations. Therefore, measurement record 1 includes: power supply current value of 120mA, potential difference value ΔV1, and spatial coordinates of quadrupole combinations (3,5), (3,8), (3,9), and (3,10); measurement record 8 includes: power supply current value of 120mA, potential difference value ΔV8, and spatial coordinates of quadrupole combinations (3,5), (3,8), (3,17), and (3,17).

[0025] After a power supply is completed, the electrode switching control module switches to the next set of quadrupole electrode combinations (for example, the next set of power supply electrode pairs are numbered (3,6) and (3,9)), and performs power supply and multi-channel parallel acquisition again. This cycle continues until all preset scan sequences have been traversed.

[0026] Those skilled in the art should understand that the parameters such as the 8-channel parallel acquisition, the specific selection method of the measurement electrode pairs, and the power supply current value in the above examples are merely illustrative and are not intended to limit the scope of protection of this invention. In practical applications, the number of parallel acquisition channels, the selection strategy of measurement electrode pairs, and the magnitude of the power supply current of a multi-channel distributed resistivity imager can all be flexibly configured according to the actual equipment and detection requirements.

[0027] (3) Traverse all quadrupole electrode combinations in the two-dimensional gridded electrode array to obtain multiple measurement records to form a full electric field observation dataset.

[0028] S120: Based on the full electric field observation dataset, perform three-dimensional resistivity inversion to obtain a three-dimensional resistivity model characterizing the resistivity distribution of the rock and soil mass below the slope of the open-pit mine bench.

[0029] Specifically, S120 includes S120-1 to S120-6: S120-1: Using the spatial position information of the quadrupole electrode combination associated with each measurement record, as well as the corresponding power supply current and measurement potential difference, the one-to-one corresponding measured apparent resistivity is calculated.

[0030] Here, apparent resistivity refers to the equivalent resistivity value calculated by substituting the measured current and voltage data in the field into a theoretical formula that assumes the underground space is a homogeneous, isotropic, semi-infinite space. The unit is usually ohm-meter (Ω·m).

[0031] In one possible implementation, the formula for calculating each measured apparent resistivity is: ; ; in, The geometric coefficients of the quadrupole electrode assembly are given. This represents the potential difference value acquired by the first and second measuring electrodes in this quadrupole electrode assembly. This refers to the power supply current value injected into the ground by the first and second power supply electrodes in this quadrupole electrode assembly. As the first power supply electrode, For the second power supply electrode, As the first measuring electrode, This is the second measuring electrode; , , , These represent the spatial distances between the two corresponding electrodes; when this quadrupole electrode assembly is a Wenner quadrupole device... , The electrode spacing; when the quadrupole electrode assembly is a dipole-dipole device. , For the power supply dipole pitch, It is a multiple of the dipole moment.

[0032] S120-2: The three-dimensional inversion space below the slope of the open-pit mine bench is meshed to obtain multiple three-dimensional mesh units, and an initial resistivity value is assigned to each three-dimensional mesh unit; wherein, the three-dimensional inversion space is a three-dimensional spatial region determined according to the layout range of the two-dimensional meshed electrode array and the preset detection depth.

[0033] Here, the preset detection depth ranges from 5 to 8 times the maximum electrode spacing of the two-dimensional gridded electrode array. Furthermore, the three-dimensional inversion space below the open-pit mine bench slope is meshed to obtain multiple three-dimensional mesh units. This includes: determining the meshing range of the three-dimensional inversion space in the strike direction and the dip direction based on the deployment range of the two-dimensional meshed electrode array in the strike and dip directions of the open-pit mine bench slope; using a preset multiple of the maximum electrode spacing of the two-dimensional meshed electrode array as the meshing range of the three-dimensional inversion space in the vertical direction; performing meshing in the strike direction with a first meshing interval, such that the number of strike meshes is twice the number of strike electrodes in the two-dimensional meshed electrode array; performing meshing in the dip direction with a second meshing interval, such that the number of dip meshes is twice the number of dip electrodes in the two-dimensional meshed electrode array; and performing equidistant meshing in the vertical direction with a third meshing interval, such that the number of vertical meshes is 1.5 times the larger of the number of strike electrodes and the number of dip electrodes; and the strike meshes, dip meshes, and vertical meshes together constitute multiple three-dimensional mesh units.

[0034] It should be noted that the aforementioned M electrode rows are arranged along the slope direction with a first electrode spacing, and N electrode rows are arranged along the slope dip with a second electrode spacing, forming a two-dimensional gridded electrode array of M rows and N columns. Therefore, the number of directional electrodes in the two-dimensional gridded electrode array is M, and the number of dip electrodes is N.

[0035] For example, based on the aforementioned 30-row × 7-column two-dimensional meshed electrode array (30 electrodes in the strike direction and 7 electrodes in the dip direction, with an electrode spacing of 2m), the process of meshing the three-dimensional inversion space below the slope of the open-pit mine bench is as follows: Direction of orientation: The two-dimensional gridded electrode array covers the direction from column 1 to column 30, with a length of (30-1)×2m=58m. To ensure inversion accuracy at the boundary, the electrode array is usually extended outward by a certain boundary distance (e.g., 5m~10m). In this example, the extension is 5m, so the orientation subdivision range is 68m. Subdivision is performed at the first subdivision interval, resulting in M×2=30×2=60 grids. Therefore, the actual subdivision interval in the orientation direction is 68m÷60≈1.13m.

[0036] Dipping direction: The length of the two-dimensional gridded electrode array in the dipping direction is (7-1)×2m=12m. Extending outward by 5m, the dipping subdivision range is 22m. Subdividing at the second subdivision interval, the number of dipping grids = N×2=7×2=14. Therefore, the actual subdivision interval in the dipping direction = 22m÷14≈1.57m.

[0037] Vertical direction: The vertical subdivision range is defined as a preset multiple of the maximum electrode spacing (2m). In this example, the preset detection depth range is 5 to 8 times the maximum electrode spacing; taking 6 times, the vertical subdivision range is 2m × 6 = 12m. Equal subdivision is performed at the third subdivision interval, resulting in a vertical grid number of grids = max(M, N) × 1.5 = 30 × 1.5 = 45. Therefore, the actual vertical subdivision interval is 12m ÷ 45 ≈ 0.27m.

[0038] Therefore, the total number of three-dimensional mesh elements is 60×14×45=37800, and an initial resistivity value is assigned to each three-dimensional mesh element through uniform assignment, hierarchical assignment, or interpolation assignment.

[0039] S120-3: In the current iteration, the finite element method or finite difference method is used to perform forward modeling of the current resistivity values ​​of multiple three-dimensional mesh elements, and the simulated apparent resistivity corresponding to each measurement record is calculated.

[0040] Specifically, based on the current iterative resistivity model, the three-dimensional Poisson equation is solved using the finite element method or the finite difference method to simulate the propagation and distribution of the electric field in the underground space after the power supply electrode injects current underground, obtaining the potential distribution at each node within the entire three-dimensional inversion space. This potential distribution is a global physical field containing potential information at any location in the three-dimensional inversion space.

[0041] Since each measurement record uniquely corresponds to a specific quadrupole electrode combination (including the specific spatial positions of the first power supply electrode A, the second power supply electrode B, the first measurement electrode M, and the second measurement electrode N), after the forward modeling is completed, for each measurement record, based on the spatial positions of the first measurement electrode M and the second measurement electrode N in the three-dimensional inversion space, the potential values ​​at the corresponding nodes are extracted from the global potential distribution. Then, combined with the power supply current value corresponding to the measurement record and the geometric coefficients of the quadrupole combination, the simulated apparent resistivity value corresponding to the measurement record is calculated. Since the spatial positions of the quadrupole electrode combinations corresponding to each measurement record are different, the calculated simulated apparent resistivity values ​​are naturally different as well. Therefore, the result of forward modeling is that for each measurement record, its corresponding simulated apparent resistivity value is calculated separately, forming a set of simulated apparent resistivity datasets that correspond one-to-one with the measured apparent resistivity.

[0042] S120-4: For each measurement record, calculate the fitting error using the simulated apparent resistivity and the measured apparent resistivity corresponding to the measurement record, and calculate the root mean square error in the current iteration process based on the fitting errors corresponding to all measurement records.

[0043] For each measurement record, the simulated apparent resistivity calculated by S120-3 is compared with the corresponding measured apparent resistivity. The difference between the two is the fitting error for that measurement record. The smaller the fitting error, the better the response of the current resistivity model under this four-electrode combination matches the measured data.

[0044] The root mean square error (RMS) for the current iteration is obtained by summing the squares of the fitting errors from all measurement records, averaging them, and then taking the square root. RMS is a single value that comprehensively measures how well the current resistivity model fits all measurement records—the smaller the RMS, the closer the current model is to the actual underground electrical structure.

[0045] S120-5: Using the least squares inversion algorithm, the current resistivity value of each three-dimensional grid cell is adjusted according to the root mean square error in the current iteration process, and the next round of iteration is continued until the iteration termination condition is met and the iteration stops.

[0046] Here, after obtaining the root mean square error (RMS) of the current iteration, the least squares inversion algorithm constructs an objective function consisting of a data fitting term and a model regularization term by calculating the sensitivity matrix (Jacobi matrix). It then solves for the minimum value of this objective function to obtain the correction amount (including adjustment direction and magnitude) for the resistivity value of each 3D grid cell. The current resistivity value is then added to the correction amount, completing the model update for this iteration. The updated resistivity model enters the next iteration, repeating S120-3 and S120-4 until the RMS drops below 5% or the number of iterations reaches the preset maximum value. At the end of the iteration, the adjustment amount of the resistivity value of each grid cell approaches zero, and the model tends to stabilize and converge. The 3D resistivity model at this point is the optimal inversion result.

[0047] S120-6: Obtain the current resistivity value of each three-dimensional mesh element after the iteration is completed, and arrange them according to their spatial position to obtain a three-dimensional resistivity model.

[0048] Here, arranging according to spatial location can be understood as organizing each three-dimensional mesh unit into a complete three-dimensional data volume according to its position in actual space. Since each three-dimensional mesh unit has a unique spatial location index, including its position in the strike direction, dip direction, and vertical direction, after iteration is complete and the resistivity values ​​of each mesh unit converge, all mesh units are rearranged according to the spatial order of the above three directions to form a three-dimensional array, thus obtaining the three-dimensional resistivity model. Furthermore, any position in this three-dimensional resistivity model corresponds one-to-one with the actual spatial coordinates in the three-dimensional inversion space. That is, the actual coordinate values ​​of that position in the strike direction, dip direction, and vertical direction can be calculated using the mesh indices in the three directions, thereby achieving the physical spatial locatability of the inversion results.

[0049] S130: Perform three-dimensional visualization imaging on at least one structural surface anomaly identified from the three-dimensional resistivity model, and the corresponding attitude parameters of each structural surface anomaly, to obtain a three-dimensional image of the structural surface that characterizes the distribution characteristics of the internal structural surfaces of the open-pit mine bench slope; wherein, the attitude parameters include the dip parameter, dip angle parameter and strike parameter of the structural surface anomaly.

[0050] Here, at least one structural surface anomalous body is identified in the following way: 1) Obtain the background resistivity value; Here, the background resistivity value is calculated as follows: a resistivity histogram is plotted using the resistivity values ​​of each three-dimensional grid cell in the three-dimensional resistivity model; the range of resistivity values ​​with the highest frequency in the resistivity histogram is taken as the main peak range, and the three-dimensional grid cells whose resistivity values ​​fall within the main peak range and are located within the shallow preset depth range of the three-dimensional resistivity model are determined as background grid cells; where the shallow preset depth range refers to the three-dimensional grid cells in the three-dimensional resistivity model from 0 to D meters below the surface of the slope, where D is less than the vertical subdivision range of the three-dimensional inversion space; the average resistivity of all background grid cells is calculated as the background resistivity value.

[0051] For example, in the aforementioned 30-row × 7-column two-dimensional gridded electrode array (with an electrode spacing of 2m), the vertical subdivision range of the three-dimensional inversion space is 12m (6 times the maximum electrode spacing), with 45 vertical grids and a vertical grid spacing of approximately 0.27m. Here, the shallow preset depth range D can be taken as twice the maximum electrode spacing, i.e., D = 4m. Then, the number of grid layers corresponding to the shallow preset depth range is approximately 4m ÷ 0.27m ≈ 15 layers, which is the 1st to 15th three-dimensional grid unit from the slope surface.

[0052] Furthermore, assuming that the bar chart with the highest height in the resistivity histogram is located in the resistivity range of 200 Ω·m to 350 Ω·m, indicating that the number of three-dimensional grid cells with resistivity values ​​falling within this range is the largest, this range is the main peak range. Further, three-dimensional grid cells with resistivity values ​​falling within this main peak range and located within the aforementioned shallow preset depth range (0 to 4 m depth from the slope surface downwards) are defined as background grid cells, and the average resistivity of all background grid cells is calculated as the background resistivity value.

[0053] After obtaining the background resistivity value, a resistivity threshold can be set based on this background resistivity value to identify low-resistivity and high-resistivity structural surface anomalies, thereby performing preliminary screening of each mesh cell in the three-dimensional resistivity model.

[0054] 2) Determine the first resistivity threshold and the second resistivity threshold based on the background resistivity value, wherein the first resistivity threshold is less than the second resistivity threshold.

[0055] For example, the first resistivity threshold is 70% of the background resistivity value, and the second resistivity threshold is 150% of the background resistivity value.

[0056] The low-resistivity and high-resistivity continuous regions identified by the absolute resistivity threshold reflect the main body of the anomalous body, but their boundaries may be inaccurate. To accurately define the spatial boundaries of the structural anomalous body, it is necessary to further utilize the spatial rate of change of resistivity—that is, the resistivity gradient—to identify the location of the electrical abrupt change interface.

[0057] 3) Traverse each three-dimensional grid cell in the three-dimensional resistivity model, mark the three-dimensional grid cells with resistivity values ​​less than the first resistivity threshold as low-resistivity candidate cells, and merge adjacent low-resistivity candidate cells into a low-resistivity continuous region; and mark the three-dimensional grid cells with resistivity values ​​greater than the second resistivity threshold as high-resistivity candidate cells, and merge adjacent high-resistivity candidate cells into a high-resistivity continuous region.

[0058] Low-resistivity candidate cells can be understood as: grid cells that may contain low-resistivity anomalies (such as water-bearing fissures or muddy-filled fracture zones), but have not yet been verified for spatial connectivity. Isolated low-resistivity candidate cells (e.g., only one or two grid cells are marked, but the surrounding area is normal background value) are usually regarded as noise or local electrical disturbances and are not sufficient to constitute a structural surface anomaly; only when multiple low-resistivity candidate cells are spatially adjacent to each other and form a continuous area of ​​a certain size do they have geological significance and be identified as a low-resistivity structural surface anomaly.

[0059] High-resistivity candidate cells can be understood as grid cells that may contain high-resistivity anomalies (such as tensional fractures or dry fracture zones), but which are candidate sets that have not yet been verified for spatial connectivity. Isolated high-resistivity candidate cells (e.g., only one or two grid cells are marked, but the surrounding area is normal background) are usually considered noise or local electrical disturbances and are not sufficient to constitute a structural anomaly. Only when multiple high-resistivity candidate cells are spatially adjacent and form a continuous region of a certain size do they have geological significance and are identified as high-resistivity structural anomalies.

[0060] It should be noted that "adjacent" means that two 3D mesh elements have a contact relationship with a shared surface (coplanar) in 3D space. That is, the two elements are adjacent in one of the directions of orientation, dip, and vertical, and the index difference in that direction is 1, while the indices in the other two directions are the same.

[0061] 4) Based on the resistivity gradient components of each three-dimensional mesh element in the directional, dip, and vertical directions in the three-dimensional resistivity model, calculate the gradient magnitude of each three-dimensional mesh element.

[0062] Thus, low-resistivity and high-resistivity continuous regions were identified using resistivity thresholds, and boundary continuous regions were identified using resistivity gradients. These three types of continuous regions reflect the spatial distribution of underground electrical anomalies from different perspectives, and they need to be spatially overlaid for analysis to comprehensively determine the precise extent of the structural anomaly.

[0063] 5) Mark 3D mesh cells with gradient modulus greater than the preset gradient threshold as boundary candidate cells, and merge adjacent boundary candidate cells into a continuous boundary region.

[0064] Here, the specific value of the preset gradient threshold can be preset according to the actual geological conditions and the required detection accuracy. When the gradient modulus of a certain grid cell is greater than the preset gradient threshold, it indicates that the resistivity at that location has changed drastically in space, corresponding to the physical interface between the intact rock mass and the structural plane. This grid cell is marked as a candidate boundary cell.

[0065] 6) Perform spatial overlay analysis on the low-resistivity continuous region, the high-resistivity continuous region, and the boundary continuous region. Use the boundary continuous region as the structural interface to correct the spatial range of the low-resistivity continuous region and the high-resistivity continuous region, and obtain multiple corrected continuous regions.

[0066] Low-resistivity / high-resistivity continuous regions are identified using absolute resistivity thresholds (70% / 150%), which effectively delineate the main body of anomalies. However, their boundary locations may be inaccurate—at the edge of anomalies, resistivity values ​​often lie in the transition zone between background and anomaly values, and threshold judgment may easily include or exclude some transition zone mesh cells. Boundary continuous regions, on the other hand, are identified using resistivity gradients (spatial rate of change), which can accurately locate the interface of electrical abrupt changes, but they do not contain information about the interior of the anomaly. Therefore, combining the two—using the location of the boundary continuous regions as a benchmark to correct the boundaries of low-resistivity / high-resistivity continuous regions—allows the finally identified structural anomalies to both cover the complete anomaly region and have precise boundary locations.

[0067] Here, the spatial extent of the low-resistivity continuous region and the high-resistivity continuous region can be modified by using the boundary continuous region through clipping, filling and separation, forming several independent modified continuous regions, ensuring that each modified continuous region is spatially continuous and has clear boundaries.

[0068] 7) Treat each corrected continuous region as a structural surface anomalous body.

[0069] After identifying at least one structural plane anomaly, in order to quantitatively describe the spatial distribution direction of each structural plane anomaly, it is necessary to further calculate the attitude parameters of each anomaly, including dip, dip angle and strike.

[0070] Correspondingly, after obtaining at least one structural surface anomaly, the attitude parameters corresponding to each structural surface anomaly are calculated as follows: extract the spatial position information of all three-dimensional mesh elements in the structural surface anomaly; use the least squares method to fit the spatial position information of all three-dimensional mesh elements in the structural surface anomaly into a plane equation representing the spatial plane; calculate the normal vector of the plane equation, and use the projection direction of the normal vector of the plane equation onto the horizontal plane as the dip parameter of the structural surface anomaly, the angle between the normal vector of the plane equation and the vertical direction as the dip angle parameter of the structural surface anomaly, and the direction perpendicular to the dip parameter as the orientation parameter of the structural surface anomaly.

[0071] It should be understood that the above-mentioned least squares plane fitting and attitude calculation methods are common knowledge in this field and will not be elaborated here.

[0072] After obtaining the spatial location and orientation parameters of each structural surface anomaly, the above information can be visualized on a three-dimensional resistivity model to intuitively show the spatial distribution characteristics of each structural surface anomaly within the stepped slope.

[0073] Three-dimensional visualization imaging is performed on at least one structural surface anomaly identified in a three-dimensional resistivity model, along with the corresponding attitude parameters of each anomaly, to obtain a three-dimensional image of the structural surface representing the distribution characteristics of the internal structural surfaces of the open-pit mine bench slope. This includes: using the three-dimensional resistivity model as the imaging data volume; annotating the spatial location and corresponding attitude parameters of the identified at least one structural surface anomaly on the three-dimensional resistivity model to obtain an annotated three-dimensional resistivity model; using isosurface extraction or volume rendering techniques to perform three-dimensional visualization rendering on the annotated three-dimensional resistivity model to generate three-dimensional spatial morphology images of each structural surface anomaly; and displaying the spatial morphology of each structural surface anomaly through cross-sectioning, transparency adjustment, or color mark mapping, and outputting it as a three-dimensional image of the structural surface.

[0074] It should be understood that the above-mentioned 3D visualization imaging process includes three progressively advancing steps: First, using the three-dimensional resistivity model as the imaging data volume means using the entire three-dimensional resistivity model as the data basis for visualization, rather than just displaying the anomalies independently. Identified structural anomalies and their attitude parameters are labeled on this data volume, so that the final image can display both the spatial morphology and attitude information of the anomalies, as well as their relative positional relationship with the surrounding rock mass (background resistivity distribution), facilitating engineers' intuitive judgment of the geological significance of the structural surfaces.

[0075] Secondly, isosurface extraction or volume rendering techniques are used to visualize and render the labeled 3D resistivity model. Isosurface extraction extracts surfaces with the same resistivity value from the data volume by setting a resistivity threshold, which is suitable for highlighting the boundaries and shapes of anomalies. Volume rendering, on the other hand, assigns color and opacity to each voxel to directly display the resistivity distribution inside the entire data volume, which is suitable for comprehensively observing the spatial distribution of anomalies in the surrounding rock. Either technique can be used, or they can be used in combination.

[0076] Finally, the rendering results can be interactively displayed and observed through methods such as cross-sectioning, transparency adjustment, and color-mark mapping. Cross-sectioning allows the model to be cut from any direction, observing the internal morphology of anomalies on a specific section; transparency adjustment achieves a perspective effect by adjusting the transparency of the anomaly or surrounding rock, facilitating simultaneous observation of structural plane distribution at multiple depths; color-mark mapping uses different colors to represent different resistivity value ranges, intuitively distinguishing anomalies from the surrounding rock. These three methods can be used individually or in combination to achieve comprehensive observation of the spatial morphology of structural plane anomalies from multiple angles and levels.

[0077] After the above visualization imaging process is completed, the final output is a three-dimensional image of the structural surface. This image integrates multi-dimensional information such as the background resistivity of the surrounding rock, the spatial morphology of the anomaly, and the occurrence parameters, and can be directly used for mine slope stability analysis and structural surface investigation.

[0078] The following is a detailed description with examples.

[0079] For example, an experiment was conducted on the slope of a bench in a large open-pit iron mine. The bench is 12m high, the slope angle is 65°, the slope length is 150m, the lithology is magnetite quartzite, the integrity is relatively good, and joints and fissures are locally developed.

[0080] Here, 30 electrodes were arranged at 2m intervals along the slope direction and 7 electrodes at 2m intervals along the slope dip direction, forming a grid array of 30×7=210 electrodes. The electrodes were stainless steel needle-shaped electrodes, 30cm long, with an insertion depth of 20cm. Each electrode had a unique number associated with its spatial location. A distributed high-density resistivity imager (128 channels with automatic switching) and a Wenner quadrupole device were used, with a power supply voltage of 180V and a power supply current of 50mA~200mA. Quadrupole electrode combinations satisfying the Wenner geometric constraints were selected sequentially according to the electrode numbering. Each group was acquired in 1s, with approximately 3800 combinations in the entire array, taking approximately 25 minutes in total to obtain the full electric field observation dataset. A finite element method 3D inversion software was used, with the mesh divided into strike 60, dip 20, and vertical 18. Eight least-squares inversion iterations were performed, resulting in a final RMS of 3.2%, yielding a 3D resistivity model.

[0081] Based on a three-dimensional resistivity model, the average resistivity of shallow intact rock mass (200 Ω·m ~ 350 Ω·m) is used as the background value. 70% and 150% of the background value are used as low-resistivity and high-resistivity thresholds, respectively. Candidate low-resistivity / high-resistivity units are identified and merged into continuous regions. Simultaneously, the resistivity gradient modulus is calculated, and regions exceeding a preset threshold are designated as boundary continuous regions. The low-resistivity / high-resistivity continuous regions are overlaid with the boundary continuous regions for analysis. The boundary continuous regions are used to correct the anomaly range, resulting in multiple structural anomalies. For the identified anomalies, their spatial coordinates are extracted from their mesh elements. The least squares method is used to fit the plane equations, and the normal vector is calculated. The horizontal projection direction of the normal vector is the dip direction, the angle between the normal vector and the vertical direction is the dip angle, and the direction perpendicular to the dip direction is the strike direction.

[0082] The identification results show that: in the middle part of the slope (20m~35m in the X direction, 6m~9m in the Y direction), there is a low-resistivity anomaly zone (45Ω·m~80Ω·m, anomaly amplitude 60%~75%), with a dip of 270° and a dip angle of 58°, which is identified as a bedding fracture zone; in the upper part of the slope (0m~3m in the Y direction, 40m~50m in the X direction), there is a high-resistivity anomaly zone (>500Ω·m), which is identified as a tension fracture.

[0083] Finally, two verification boreholes (15m deep) were drilled in the low resistivity anomaly area. Core sampling revealed core fragmentation (RQD = 15%~25%), well-developed fractures, and clay infill, consistent with the detection results. The spatial location and attitude parameters of the structural anomaly were marked on a three-dimensional resistivity model. Isosurface extraction or volume rendering techniques were used for three-dimensional visualization rendering, displaying the anomaly as a plate-like distribution in a semi-transparent manner, generating a three-dimensional image of the structural surface.

[0084] Compared with existing technologies, this application has significant advantages: To address the problem that existing methods for detecting structural surfaces on mine bench slopes rely on the exposure of these surfaces at the surface, severely limiting the effectiveness of such detection, this application provides a rapid three-dimensional resistivity imaging method for open-pit mine bench slope structures. This method employs a multi-channel distributed resistivity imager to perform full-array quad-electrode measurements on a two-dimensional gridded electrode array deployed on the open-pit mine bench slope to obtain a full electric field observation dataset. This eliminates the dependence on surface exposure of the structural surfaces, instead relying on resistivity differences in the underground medium for detection, effectively identifying hidden structural surfaces within the slope. Furthermore, by performing three-dimensional resistivity inversion on the full electric field observation dataset, a three-dimensional resistivity model characterizing the resistivity distribution of the soil and rock beneath the slope is obtained. This study provides a three-dimensional data foundation for identifying concealed structural surfaces by reconstructing the spatial distribution of three-dimensional resistivity from surface measurement data. Based on this, the study quantifies structural surface identification and attitude extraction by identifying structural surface anomalies and calculating their attitude parameters from the three-dimensional resistivity model, avoiding the subjectivity of relying on human experience in traditional methods. Finally, the study obtains three-dimensional images of structural surfaces characterizing the distribution features of internal structural surfaces by performing three-dimensional visualization imaging of the identified structural surface anomalies and their attitude parameters. This transforms abstract three-dimensional resistivity data into intuitive spatial distribution images of structural surfaces, achieving a complete detection chain from data acquisition to three-dimensional imaging that is independent of structural surface exposure conditions, significantly improving the effectiveness and accuracy of open-pit mine bench slope structural surface detection.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible 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.

[0086] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A rapid three-dimensional resistivity imaging method for the structural surface of an open-pit mine bench slope, characterized in that, include: A multi-channel distributed resistivity imager was used to perform full array quad-electrode combination measurements on a two-dimensional gridded electrode array deployed on the slope of an open-pit mine bench, resulting in a full electric field observation dataset. Based on the full electric field observation dataset, a three-dimensional resistivity inversion was performed to obtain a three-dimensional resistivity model characterizing the resistivity distribution of the rock and soil mass below the slope of the open-pit mine bench. Three-dimensional visualization imaging is performed on at least one structural surface anomaly identified from the three-dimensional resistivity model, and the attitude parameters corresponding to each structural surface anomaly, to obtain a three-dimensional image of the structural surface that characterizes the distribution characteristics of the internal structural surfaces of the open-pit mine bench slope; wherein, the attitude parameters include the dip parameter, dip angle parameter and strike parameter of the structural surface anomaly.

2. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 1, characterized in that, The two-dimensional gridded electrode array is deployed in the following manner: According to the preset layout, multiple stainless steel needle electrodes are inserted into the soil on the slope of the open-pit mine bench; each stainless steel needle electrode has a unique number. The preset layout method includes: laying out M electrode rows at a first electrode spacing along the slope of the open-pit mine bench, laying out N electrode rows at a second electrode spacing along the inclination of the slope of the open-pit mine bench, and inserting a stainless steel needle electrode at each intersection of the M electrode rows and the N electrode rows to form the two-dimensional gridded electrode array of M rows and N columns.

3. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 2, characterized in that, The full array quadrupole combined measurement includes: Using the electrode switching control module in the multi-channel distributed resistivity imager, four-electrode combinations are selected sequentially from the two-dimensional gridded electrode array according to a preset scanning sequence. Each four-electrode combination includes a first power supply electrode, a second power supply electrode, a first measurement electrode, and a second measurement electrode. For each quadrupole electrode combination, current is injected into the ground through the first power supply electrode and the second power supply electrode, and the potential difference is collected through the first measuring electrode and the second measuring electrode. The corresponding power supply current value and the measured potential difference value are recorded as a measurement record. Each measurement record is associated with the spatial location information of a quadrupole electrode combination. By traversing all quadrupole electrode combinations in the two-dimensional gridded electrode array, multiple measurement records are obtained to form the full electric field observation dataset.

4. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 3, characterized in that, The step of sequentially selecting quadrupole electrode combinations from the two-dimensional gridded electrode array according to a preset scanning sequence includes: According to the ascending order of the unique number corresponding to each stainless steel needle electrode, all quadrupole electrode combinations that meet the preset device type geometric constraints are selected sequentially from the two-dimensional gridded electrode array. The preset device type includes either the Winner quadrupole device type or the dipole-dipole device type; When the preset device type is the Winner quadrupole device type, the first power supply electrode, the second power supply electrode, the first measuring electrode, and the second measuring electrode in each selected quadrupole electrode combination are arranged at equal intervals along the same straight line; When the preset device type is the dipole-dipole device type, the first power supply electrode and the second power supply electrode in each selected quadrupole electrode combination constitute a power supply dipole, and the first measuring electrode and the second measuring electrode constitute a measuring dipole. The power supply dipole and the measuring dipole are arranged along the same straight line, and the distance between the power supply dipole and the measuring dipole is n times the distance between the first power supply electrode and the second power supply electrode, where n is a preset positive integer.

5. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 3, characterized in that, Based on the full electric field observation dataset, a three-dimensional resistivity inversion is performed to obtain a three-dimensional resistivity model characterizing the resistivity distribution of the soil and rock mass beneath the open-pit mine bench slope, including: Using the spatial position information of the quadrupole electrode combination associated with each measurement record, as well as the corresponding power supply current and measurement potential difference, the measured apparent resistivity is calculated one by one. The three-dimensional inversion space below the slope of the open-pit mine bench is meshed to obtain multiple three-dimensional mesh units, and an initial resistivity value is assigned to each three-dimensional mesh unit; wherein, the three-dimensional inversion space is a three-dimensional spatial region determined according to the deployment range of the two-dimensional meshed electrode array and the preset detection depth; In the current iteration, the finite element method or finite difference method is used to perform forward modeling on the current resistivity values ​​of the multiple three-dimensional mesh elements, and the simulated apparent resistivity corresponding to each measurement record is calculated. For each measurement record, the fitting error is calculated using the simulated apparent resistivity and the measured apparent resistivity corresponding to the measurement record, and the root mean square error in the current iteration process is calculated based on the fitting errors corresponding to all measurement records. The least squares inversion algorithm is used to adjust the current resistivity value of each three-dimensional grid cell according to the root mean square error in the current iteration process, and continue to the next round of iteration until the iteration termination condition is met and the iteration stops. After the iteration is completed, the current resistivity value of each three-dimensional mesh cell is obtained and arranged according to its spatial position to obtain the three-dimensional resistivity model.

6. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 5, characterized in that, The at least one structural surface anomalous body is identified through the following methods: Obtain the background resistivity value; A first resistivity threshold and a second resistivity threshold are determined based on the background resistivity value, wherein the first resistivity threshold is less than the second resistivity threshold; Traverse each three-dimensional grid cell in the three-dimensional resistivity model, mark the three-dimensional grid cells with resistivity values ​​less than the first resistivity threshold as low-resistivity candidate cells, and merge adjacent low-resistivity candidate cells into a low-resistivity continuous region; and mark the three-dimensional grid cells with resistivity values ​​greater than the second resistivity threshold as high-resistivity candidate cells, and merge adjacent high-resistivity candidate cells into a high-resistivity continuous region. Based on the resistivity gradient components of each three-dimensional grid cell in the three-dimensional resistivity model in the directional, dip, and vertical directions, the gradient magnitude corresponding to each three-dimensional grid cell is calculated. Three-dimensional mesh elements with gradient magnitude greater than a preset gradient threshold are marked as boundary candidate elements, and adjacent boundary candidate elements are merged into a continuous boundary region. The low-resistivity continuous region, the high-resistivity continuous region, and the boundary continuous region are spatially superimposed and analyzed. The spatial range of the low-resistivity continuous region and the high-resistivity continuous region is corrected by using the boundary continuous region as the structural surface interface, resulting in multiple corrected continuous regions. Each corrected contiguous region is treated as a structural surface anomalous body.

7. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 6, characterized in that, The background resistivity value was obtained by calculating the following method: Using the resistivity values ​​of each three-dimensional mesh element in the three-dimensional resistivity model, a resistivity histogram is plotted. The range of resistivity values ​​with the highest frequency in the resistivity histogram is taken as the main peak range, and the three-dimensional grid cells whose resistivity values ​​fall within the main peak range and are located within the shallow preset depth range of the three-dimensional resistivity model are determined as background grid cells; wherein, the shallow preset depth range refers to the three-dimensional grid cells in the three-dimensional resistivity model from 0 to D meters below the surface of the slope, where D is less than the vertical partitioning range of the three-dimensional inversion space; Calculate the average resistivity of all background grid cells to obtain the background resistivity value.

8. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 5, characterized in that, The three-dimensional inversion space below the slope of the open-pit mine bench is meshed to obtain multiple three-dimensional mesh elements, including: Based on the deployment range of the two-dimensional gridded electrode array in the strike direction and the dip direction of the open-pit mine bench, the subdivision range of the three-dimensional inversion space in the strike direction and the subdivision range in the dip direction are determined. The maximum electrode spacing of the two-dimensional gridded electrode array is taken as the vertical division range of the three-dimensional inversion space; The first subdivision interval is used to subdivide within the subdivision range of the orientation direction, such that the number of orientation grids is twice the number of orientation electrodes of the two-dimensional gridded electrode array; The array is divided within the dipping range of the inclined direction at a second dipping interval, such that the number of inclined grids is twice the number of inclined electrodes of the two-dimensional gridded electrode array. The vertical direction is divided into equal-distance subdivisions at a third subdivision interval, such that the number of vertical grids is 1.5 times the larger of the number of directional electrodes and the number of inclined electrodes. The plurality of three-dimensional mesh units are composed of the directional mesh number, the inclined mesh number, and the vertical mesh number.

9. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 6, characterized in that, The attitude parameters corresponding to each structural surface anomalous body are calculated in the following manner: Extract the spatial location information of all three-dimensional mesh elements in the structural surface anomaly. The least squares method is used to fit the spatial position information of all three-dimensional mesh elements in the structural surface anomaly into a plane equation representing the spatial plane; Calculate the normal vector of the plane equation, and use the projection direction of the normal vector of the plane equation onto the horizontal plane as the dip parameter of the structural surface anomaly, the angle between the normal vector of the plane equation and the vertical direction as the tilt parameter of the structural surface anomaly, and the direction perpendicular to the dip parameter as the orientation parameter of the structural surface anomaly.

10. The method for rapid three-dimensional resistivity imaging of open-pit mine bench slope structure according to claim 1, characterized in that, The process of performing three-dimensional visualization imaging on at least one structural surface anomaly identified in the three-dimensional resistivity model, and the corresponding attitude parameters of each structural surface anomaly, to obtain a three-dimensional image of the structural surface characterizing the distribution features of the internal structural surfaces of the open-pit mine bench slope, includes: Using the three-dimensional resistivity model as the imaging data volume, the spatial location and corresponding orientation parameters of the identified at least one structural surface anomaly are labeled on the three-dimensional resistivity model to obtain the labeled three-dimensional resistivity model. Using isosurface extraction or volume rendering techniques, the labeled three-dimensional resistivity model is rendered in three dimensions to generate three-dimensional spatial morphology images of each structural surface anomaly. The spatial morphology of each structural surface anomaly is displayed by cutting cross-sections, adjusting transparency, or color mark mapping, and output as a three-dimensional image of the structural surface.

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