Three-dimensional resistivity imaging method for fracture development of dumping site of strip mine

Abstract

The invention discloses a three-dimensional resistivity imaging method for fracture development of a strip mine dumping site, and belongs to the technical field of strip mine dumping site fracture development detection. The method comprises the following steps: S1, topographic survey and electrode layout; s2, acquiring potential data; s3, carrying out data processing and three-dimensional inversion; s4, evaluating an inversion result; s5, analyzing the resistivity distribution model; and S6, performing safety assessment and engineering management. According to the three-dimensional resistivity imaging method for fracture development of the strip mine dumping site, the detection capability and efficiency of fracture development of the strip mine dumping site are remarkably improved by combining data processing and an inversion algorithm, and technical support is provided for safety management and disaster prevention of the dumping site.

Description

Technical Field

[0001] The invention belongs to the technical field of crack development detection in open-pit mine dumps, and in particular relates to a three-dimensional resistivity imaging method for crack development in open-pit mine dumps. Background Art

[0002] The stability of the spoil dump is crucial to the safe production and environmental protection of the mine. However, the cracking phenomenon inside the spoil dump is one of the key factors threatening its safety. These cracks are fissures formed in the pile under the action of external stress. They not only destroy the mechanical stability of the pile, but also may cause a series of serious geological disasters, such as soil sliding, leakage and settlement, which are directly related to the safe production and environmental protection of the mining area.

[0003] In order to effectively prevent disasters caused by cracks in spoil dumps, it is necessary to accurately understand the development law and evolution process of cracks and their interaction with the surrounding geological environment. The current methods used to detect cracks in spoil dumps mainly include geophysical detection methods such as three-dimensional DC resistivity CT method, micro-motion detection method, and ground penetrating radar. These methods can dynamically monitor the generation and development of cracks in real time, and provide a scientific basis for the stability assessment of spoil dumps by analyzing their spatial distribution, size characteristics, and stress changes.

[0004] Although existing detection technologies have achieved the monitoring of cracks to a certain extent, due to the complex cracking mechanism of spoil dumps and the heterogeneity of geological conditions, existing methods still face challenges in practical application, such as the need to improve detection accuracy and spatial resolution to capture small changes in cracks or deep cracks. In addition, the different geological conditions in mining areas also limit the actual effect and accuracy of some technologies.

[0005] Therefore, the focus of future research should be on the application of multi-source data fusion technology to improve the comprehensive performance, accuracy and real-time performance of the detection method, and achieve more efficient and reliable monitoring and management of spoil dump safety. In particular, three-dimensional resistivity imaging (3D ERT), as a geophysical detection technology, has been widely used in groundwater exploration and mine detection, but its application in spoil dump crack detection is still in the exploratory stage. It has the potential to achieve more accurate and efficient crack detection and monitoring purposes by optimizing the inversion algorithm and combining other geophysical methods. Summary of the invention

[0006] The purpose of the present invention is to provide a three-dimensional resistivity imaging method for the development of cracks in open-pit mine dumps. By combining data processing and inversion algorithms, this technology significantly improves the detection capability and efficiency of crack development in open-pit mine dumps, providing technical support for the safety management and disaster prevention of dumps.

[0007] To achieve the above object, the present invention provides a three-dimensional resistivity imaging method for crack development in an open-pit mine dump, comprising the following steps:

[0008] S1. Arrange electrode arrays on the step surface and top area of ​​the spoil pile;

[0009] S2, injecting a known current into the ground through the selected power supply electrode and recording the potential value of the electrode when it is used as a measuring electrode;

[0010] S3, performing denoising and standardization preprocessing on the data collected in S2;

[0011] S4, using an inversion algorithm to generate a three-dimensional resistivity distribution model of the spoil pile area;

[0012] S5, analyzing the spatial position, geometric shape and distribution law of the cracks inside the spoil pile according to the three-dimensional resistivity distribution model in S4;

[0013] S6. Based on the results of the analysis in S5, conduct a comprehensive assessment of the safety of the spoil pile and provide an engineering control plan.

[0014] Preferably, in S2, one electrode is selected as a power supply electrode, and the remaining electrodes are used as measuring electrodes, and the electrodes are powered in turn according to the above method.

[0015] Preferably, in S3, the objective function calculation method of the inversion process is as follows:

[0016]

[0017] in, represents the difference vector between the measured data and the model forward data, represents resistivity, J represents Jacobian coefficient matrix, λ represents Lagrangian factor, and R represents roughness matrix.

[0018] Preferably, in S3, the three-dimensional resistivity distribution model parameters are adjusted according to the objective function, and the model parameter updating formula is:

[0019]

[0020] in, is the updated resistivity, J T is the transpose of the Jacobian matrix, R T is the transpose of the roughness matrix.

[0021] Preferably, in S3, the three-dimensional resistivity distribution model is smoothed using a roughness matrix.

[0022] Preferably, in S5, risk areas are identified based on the three-dimensional resistivity model and the fracture distribution map.

[0023] Preferably, in S5, the resistivity distribution is presented as a three-dimensional image using three-dimensional visualization technology.

[0024] Therefore, the present invention adopts the above-mentioned three-dimensional resistivity imaging method for crack development in open-pit mine dumps. Compared with the prior art, the present invention has the following significant beneficial effects:

[0025] (1) The present invention uses three-dimensional resistivity tomography (3D ERT) technology to reveal the spatial distribution and evolution of cracks in the dump, especially for the detection of small changes or deep cracks, providing higher detection accuracy and spatial resolution;

[0026] (2) The present invention can obtain the depth, width, direction and distribution characteristics of the cracks in the pile, providing more three-dimensional and detailed geological information for comprehensive assessment of the stability of the spoil dump. This helps to identify potential safety risk areas and provide a scientific basis for subsequent risk assessment and preventive measures;

[0027] (3) The present invention can reflect the evolution of cracks in real time, which is crucial for dynamically tracking changes in the dump and timely discovering and responding to potential safety risks;

[0028] (4) In the data processing stage, the present invention uses a roughness matrix to smooth the model, avoiding overfitting and unreasonable mutations in resistivity distribution, ensuring that the final potential distribution diagram is physically reasonable, and improving the accuracy and reliability of the model.

[0029] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of a three-dimensional resistivity imaging method for crack development in an open-pit mine dump according to the present invention;

[0031] Figure 2 It is a schematic diagram of a three-dimensional resistivity imaging method for crack development in an open-pit mine dump according to the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used in the present invention should be the common meaning understood by people with general skills in the field to which the present invention belongs.

[0033] Embodiment 1

[0034] like Figure 1 As shown, a three-dimensional resistivity imaging method for crack development in an open-pit mine dump of the present invention comprises the following steps:

[0035] S1. Topographic survey and electrode layout;

[0036] First, conduct a comprehensive topographic survey of the spoil pile site to analyze the step distribution, top surface area, and bottom area geometric features of the spoil pile. Afterwards, lay a circle of electrodes at the edge of each step to ensure that the electrical information of each step surface can be collected. Evenly lay out the electrodes on the top platform to cover the entire top area. Reasonably adjust the electrode spacing according to actual detection needs to ensure measurement resolution while reducing external interference. Give each laid electrode a unique number to clearly identify its function as a power supply electrode or a measurement electrode. This helps to accurately control the current injection point and voltage measurement point during the data acquisition stage, and facilitates tracking and management during data processing;

[0037] S2, potential data acquisition;

[0038] At the beginning, select one electrode from the electrodes arranged in S1 as the power supply electrode, and inject a known current into the ground through this electrode. Then use the remaining electrodes as measuring electrodes, record the corresponding potential values, and clearly record the position of the power supply electrode and the injected current value. After completing a measurement, switch the power supply electrode to the next electrode in sequence, and repeat the above measurement process. Until all electrodes have been used as power supply electrodes once. Real-time monitoring is carried out during the data collection process to avoid abnormal data, and all collected data are stored and backed up in real time.

[0039] S3, data processing and 3D inversion;

[0040] The collected current and voltage data are preprocessed. The preprocessing mainly includes two aspects. On the one hand, it is to eliminate the noise introduced by instrument errors or environmental interference (such as electromagnetic interference, poor electrode contact, etc.). On the other hand, it is to convert the original data into a unified standard form to eliminate the differences under different measurement conditions. The preprocessed data will be used in the inversion process of three-dimensional resistivity imaging. The goal of inversion is to adjust and optimize the resistivity distribution model of the underground medium by calculating the difference between the measured data and the data predicted by the forward model, so that the model can fit the actual observed data as much as possible. The key to the inversion process is to construct an objective function, which describes the difference between the measured data and the model predicted data. Its specific expression is:

[0041]

[0042] in, is the difference vector between the measured data and the model forward data, is the parameter vector of each node of the model, representing the resistivity at each location, J is the Jacobian coefficient matrix, reflecting the impact of model parameter changes on the observed data, and λ is the Lagrangian factor, which is used to control the smoothness of the model. A larger λ can prevent sudden changes in the model resistivity. A larger initial value is assigned to λ at the beginning of the inversion, and λ is gradually reduced as the number of iterations increases.

[0043] At each iteration, the resistivity distribution model parameters are adjusted according to the objective function to reduce the difference between the measured data and the model predicted data. The update formula of the resistivity distribution model parameters is:

[0044]

[0045] in, is the parameter vector of each node of the updated model, representing the updated resistivity at each location.

[0046] During the inversion process, the roughness matrix is ​​used to smooth the model to avoid overfitting of the model and abnormal changes in resistivity distribution. The roughness matrix R is expressed as:

[0047]

[0048] or,

[0049]

[0050] S4. Evaluate the inversion results;

[0051] Evaluate the fit of the inversion results. If the error is large, adjust the inversion parameters or increase the number of iterations until the error converges to a reasonable range. By adjusting the model smoothness, avoid unreasonable resistivity fluctuations and ensure that the final potential distribution map is physically reasonable. In order to evaluate the quality of the inversion results, calculate the root mean square error (RMS) between the resistivity distribution model predicted data and the observed data. The smaller the RMS value, the smaller the error between the inversion result and the actual data, and the better the model fit. The calculation formula for RMS is:

[0052]

[0053] Where N is the number of observations, d i is the ith observation data, Represents the model prediction value. If the RMS value is large, it is necessary to further adjust the inversion parameters or increase the number of iterations until the error between the model and the data converges to a reasonable range.

[0054] In addition to paying attention to the RMS value, it is also necessary to carefully check the electrical mutations that may appear during the inversion process, that is, abnormal resistivity fluctuations. If abnormalities are found, the model smoothness can be improved by adjusting the roughness matrix or changing the λ value.

[0055] S5. Analyze the resistivity distribution model;

[0056] After completing the inversion and optimizing the potential distribution model of the underground medium, the potential distribution data is converted into a three-dimensional resistivity distribution model. Since different geological bodies (such as fractures, water-bearing zones, or loose bodies) usually exhibit different resistivity characteristics, abnormal geological bodies inside the spoil pile can be identified by analyzing the resistivity distribution. Generally, low-resistivity areas may be associated with underground fractures, water-bearing zones, or loose bodies, which may have higher risks, such as landslides, collapses, or water infiltration; high-resistivity areas may correspond to dense rock formations or hard soils, which are relatively stable and not prone to deformation.

[0057] During the analysis process, it is necessary to combine the data from different survey lines and conduct a comprehensive analysis of the resistivity distribution from multiple perspectives to determine the spatial position, geometric shape and distribution law of the anomaly. In addition, three-dimensional visualization technology can be used to present the resistivity distribution as an intuitive three-dimensional image, clearly showing the structural characteristics of the anomaly, and providing an important basis for subsequent fracture risk assessment and geological interpretation;

[0058] S6. Safety assessment and engineering management;

[0059] Based on the three-dimensional resistivity model and crack distribution map, the distribution characteristics of low resistivity areas are analyzed to identify potential high-risk areas. Specific engineering treatment plans are proposed for the identified high-risk areas, such as grouting reinforcement, slope drainage and shotcrete. It is recommended to conduct three-dimensional resistivity measurements regularly, dynamically track the changes in the spoil pile, promptly identify potential safety risks, and ensure the long-term stability and safety of the spoil pile.

[0060] like Figure 2 As shown in the figure, the three-dimensional resistivity imaging method is used to detect cracks on the spoil pile structure:

[0061] Assume that the spoil pile is divided into four layers, and the dimensions from the bottom to the top are: first layer: 100m×100m; second layer: 80m×80m; third layer: 60m×60m; fourth layer (top layer): 40m×40m.

[0062] The distance between each layer of electrodes is 10m. Bottom layer (first layer): 36 electrodes; second layer: 20 electrodes; third layer: 28 electrodes; top layer (fourth layer): 16 electrodes, a total of 100 electrodes. These 100 electrodes are numbered from electrode 1 to electrode 100.

[0063] At the beginning, electrode No. 1 is selected as the power supply electrode, and the remaining 99 electrodes are used as measuring electrodes to start data collection and obtain the first set of potential data. Then, electrode No. 2 is set as the power supply electrode, and the remaining electrodes are used as measuring electrodes to perform potential measurement again to obtain the second set of data. The above method is carried out in sequence until electrode No. 100 is used as the power supply electrode, and all 100 sets of data collection are completed.

[0064] After data collection is completed, these raw data are preprocessed, including denoising and standardization steps, to ensure the accuracy and reliability of the data. The preprocessed data are used to construct a three-dimensional potential data volume in the spoil pile area. Since each measurement point will have multiple data points, through multi-point constraint optimization, the potential data of each location can be finally obtained. These data will be used to construct a potential distribution model that matches the actual geological conditions, providing an important basis for detecting and monitoring spoil pile cracks, and providing support for subsequent risk assessment and preventive measures.

[0065] The final three-dimensional resistivity distribution map can be further converted into an intuitive three-dimensional image, which clearly shows the structural characteristics of the cracks inside the spoil pile, such as depth, width, direction and their distribution characteristics in the pile. By analyzing the high resistivity area and the low resistivity area, potential high-risk areas can be identified.

[0066] Combined with the known geological background, the expansion trend of the cracks and their impact on the stability of the spoil pile are further analyzed.

[0067] Therefore, the present invention adopts the above-mentioned three-dimensional resistivity imaging method for the development of cracks in open-pit mine dumps. This technology significantly improves the detection capability and efficiency of the development of cracks in open-pit mine dumps by combining optimized data processing and inversion algorithms, and provides technical support for the safety management and disaster prevention of dumps.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A three-dimensional resistivity imaging method for crack development in open-pit mine dumps, characterized in that: The following steps are involved: S1. Arrange electrode arrays on the step surface and top area of ​​the spoil pile; S2, injecting a known current into the ground through the selected power supply electrode and recording the potential value of the electrode when it is used as a measuring electrode; S3, performing denoising and standardization preprocessing on the data collected in S2, the preprocessed data will be used in the inversion process of three-dimensional resistivity imaging, and using the inversion algorithm to generate a three-dimensional resistivity distribution model of the spoil pile area; S4, use the root mean square error to evaluate the fitting degree of the inversion results; S5, analyzing the spatial position, geometric shape and distribution law of the cracks inside the spoil pile according to the three-dimensional resistivity distribution model in S4; S6. Based on the results of the analysis in S5, conduct a comprehensive assessment of the safety of the spoil pile and provide an engineering control plan.

2. A three-dimensional resistivity imaging method for crack development in an open-pit mine dump according to claim 1, characterized in that: In S2, one electrode is selected as the power supply electrode, and the remaining electrodes are used as the measuring electrodes, and the electrodes are powered in turn according to the above method.

3. The three-dimensional resistivity imaging method for crack development in an open-pit mine dump according to claim 1, characterized in that: In S3, the objective function calculation method of the inversion process is as follows: in, represents the difference vector between the measured data and the model forward data, represents resistivity, J represents Jacobian coefficient matrix, λ represents Lagrangian factor, and R represents roughness matrix.

4. The method for three-dimensional resistivity imaging of crack development in an open-pit mine dump according to claim 1, characterized in that: In S3, the three-dimensional resistivity distribution model parameters are adjusted according to the objective function, and the model parameter update formula is: in, is the updated resistivity, J T is the transpose of the Jacobian matrix, R T is the transpose of the roughness matrix.

5. The method for three-dimensional resistivity imaging of crack development in open-pit mine dump according to claim 1, characterized in that: In S3, the roughness matrix is ​​used to smooth the three-dimensional resistivity distribution model.

6. The method for three-dimensional resistivity imaging of crack development in an open-pit mine dump according to claim 1, characterized in that: In S5, risk areas are identified based on the three-dimensional resistivity model and fracture distribution map.

7. The method for three-dimensional resistivity imaging of crack development in an open-pit mine dump according to claim 1, characterized in that: In S5, the resistivity distribution is presented as a three-dimensional image using three-dimensional visualization technology.

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