Method for monitoring an intrusion tunnel based on a resistivity variation gradient

Abstract

This invention discloses a method for monitoring intrusion tunnels based on resistivity change gradients. First, an initial electrical resistivity (EPR) test is performed to obtain initial apparent resistivity data. This data is then processed to obtain a baseline resistivity grid data matrix. After a time interval, EPR testing is performed again, and the resistivity grid data matrix for the profile at time t is obtained. Gradient operations are performed on the two matrices to obtain the resistivity change gradient map of the profile at time t. The matrix at time t is then used as the new baseline resistivity data matrix. This process is repeated to obtain the resistivity change gradient maps for subsequent time intervals. Finally, a threshold is set to analyze and judge high-anomaly areas, thus distinguishing between intrusion tunnels and high-resistivity interference tunnels. Therefore, this invention can effectively distinguish between intrusion tunnels and high-resistivity interference tunnels during monitoring, greatly reducing false alarms or missed alarms and effectively ensuring the monitoring accuracy of intrusion tunnels.

Description

Technical Field

[0001] This invention relates to a monitoring method for identifying real underground intrusion tunnels, specifically a method for monitoring intrusion tunnels based on resistivity change gradients, which is particularly suitable for complex geological conditions. Background Technology

[0002] Electrical resistivity tomography (E tomography) technology has seen some application and research in monitoring intrusive tunnels. By monitoring the distribution of electrical properties at different time points, it can detect potential underground tunnels and other illegal passages. However, current monitoring processes often suffer from false alarms or missed alarms due to the inability to distinguish between real and false anomalies. For example, high-resistivity interference such as karst caves or geological structures may resemble intrusive tunnels, leading to false alarms; conversely, some objects may be identified as intrusive tunnels but are not detected as interference, resulting in missed alarms. Misidentifying other objects as intrusive tunnels reduces the efficiency of the entire monitoring system, and failure to detect intrusive tunnels in a timely manner can lead to system failure. Therefore, researching methods to distinguish between genuine intrusive tunnels and false anomalies is essential.

[0003] Therefore, how to provide a new method to effectively distinguish between intrusion tunnels and high-resistivity interference tunnels during the monitoring process, thereby greatly reducing false alarms or missed alarms and effectively ensuring the monitoring accuracy of intrusion tunnels, is a technical problem that urgently needs to be solved in this industry. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for monitoring intrusion tunnels based on resistivity change gradients. During the monitoring process, it can effectively distinguish between intrusion tunnels and high-resistivity interference tunnels, thereby greatly reducing false alarms or missed alarms and effectively ensuring the monitoring accuracy of intrusion tunnels.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for monitoring intrusion tunnels based on resistivity change gradient, the specific steps of which are as follows:

[0006] Step 1: Deploy the monitoring system: First, determine the location of the boundary line. One side of the boundary line is the monitoring area. Lay out a row of electrode measuring lines on the boundary line. Each measuring electrode on the electrode measuring line is connected to the electrical resistivity monitoring instrument to complete the deployment of the electrical resistivity monitoring system.

[0007] Step 2, Initial Electrical Resistivity Monitoring: The electrical resistivity monitoring system is activated for the initial monitoring. The initially acquired apparent resistivity data is used as the initial apparent resistivity data. Next, the subsurface profile where the electrode survey line is located is acquired and gridded. The initial apparent resistivity data is then inverted based on each grid to obtain the resistivity grid data matrix of the profile, which serves as the baseline resistivity grid data matrix R. b ;

[0008] Step 3, Re-evaluation using electrical resistivity: After the initial data acquisition, perform another electrical resistivity measurement at an interval t. Use the measured apparent resistivity data to perform resistivity inversion based on the grid defined in Step 2, obtaining the resistivity grid data matrix R. t This serves as the resistivity grid data matrix for the profile at time t;

[0009] Step 4, Electrical resistivity data processing: Process the resistivity data matrix R from Step 3. t Compared with the baseline grid data matrix R in step two b Gradient calculation yields the resistivity gradient matrix R. dt And the matrix R dt Generate an image to obtain the gradient map of the profile resistivity change at time t; then, the matrix R... t As the new baseline resistivity data matrix R b Then, steps three and four are repeated every time interval t to obtain the resistivity gradient data R for each time period. d ;

[0010] Step 5: Identify the intrusion tunnel: Analyze the resistivity gradient data R obtained in Step 4 for each time period. d The analysis determines whether the high anomaly areas in the resistivity inversion profile are intrusion tunnels, ultimately distinguishing between intrusion tunnels and high-resistivity interference tunnels.

[0011] The principle by which this application distinguishes between intrusion tunnels and high-resistivity interference caverns is as follows: The inventors discovered that although both intrusion tunnels and high-resistivity interference caverns appear as high-anomaly areas (i.e., high-resistivity regions) during resistivity inversion imaging, the actual intrusion tunnels continue to be excavated in subsequent processes (i.e., the extent of the intrusion tunnel continues to expand over a period of time). This results in a continuous increase in resistivity on the resistivity profile of the same high-anomaly area at different time periods, meaning that there is a high resistivity gradient change at each time period. In contrast, the formation of high-resistivity interference caverns is a one-time event (e.g., a sudden collapse at a certain underground location). Alternatively, it may require a long time to form (for example, it may take several or even more than ten years for pores to form due to the impact and etching of groundwater flow). Therefore, there will not be a large change in resistivity gradient over time, that is, the change in resistivity gradient in each time period is extremely low. Using this discovery, a reasonable threshold can be set. During the electrical monitoring process, after a high anomaly area is found through resistivity inversion imaging, it is only necessary to obtain the resistivity gradient of each time period in the subsequent multiple time periods and compare it with the threshold. If it continues to exceed the threshold, it indicates that it is an intrusion tunnel; otherwise, it is a high-resistivity interference cave. This enables the distinction and determination of intrusion tunnels and high-resistivity interference caves.

[0012] Furthermore, in step one, the distance between two adjacent electrodes on the electrode measuring line is 1 to 3 meters.

[0013] Furthermore, in step two, the cross-section is meshed to form an m*n rectangular grid arrangement.

[0014] Furthermore, the specific formula for gradient calculation in step four is as follows:

[0015]

[0016] In the above formula, R t Let R be the resistivity data matrix for each grid at time t, where R represents the resistivity value of each grid. b For the baseline resistivity data matrix, R b (i, j) is R b The value of the i-th row and j-th column of the matrix, R t (i, j) is R t The value of the i-th row and j-th column of the matrix, R dt Let be the gradient matrix of resistivity change for each grid at time t.

[0017] Furthermore, step five specifically involves: setting the resistivity gradient data R for each time period. d The standard deviation σ (where the standard deviation refers to the resistivity gradient data R over a time period) is twice the standard deviation of the resistivity gradient data. d The standard deviation of all grid values ​​(plus the resistivity gradient data R over that time period) d The average value (this average value is the resistivity gradient data R over a period of time) d The average gradient values ​​of each grid are used as the threshold for that time period. If there is a region in the resistivity change gradient map of the profile obtained in a certain time period that exceeds the threshold of that time period, it is regarded as a high anomaly area. Then, the gradient data of multiple consecutive time periods after that time period are obtained. If the gradient data of the high anomaly area in each of the above time periods is consistently higher than the threshold corresponding to each time period, then the location of the high anomaly area is determined to be the location of the intrusion tunnel. Otherwise, the high anomaly area is judged to be a high-resistivity interference tunnel.

[0018] Compared with existing technologies, this invention first performs initial electrical resistivity monitoring to obtain initial apparent resistivity data. After processing the initial apparent resistivity data, a baseline resistivity grid data matrix is ​​obtained. Then, after a period of time, electrical resistivity monitoring is performed again, and the resistivity grid data matrix of the profile at time t is obtained after processing. Gradient operations are performed on the two matrices to obtain the profile resistivity change gradient map at time t. The matrix at time t is then used as the new baseline resistivity data matrix. This process is repeated to obtain the profile resistivity change gradient maps for subsequent time intervals. Finally, a corresponding threshold is set for each time interval. By comparing the gradient data of each time interval with the corresponding threshold, high anomaly areas are analyzed and judged to distinguish between intrusion tunnels and high-resistivity interference caves. Therefore, this invention can effectively distinguish between intrusion tunnels and high-resistivity interference caves during the monitoring process, thereby greatly reducing false alarms or missed alarms and effectively ensuring the monitoring accuracy of intrusion tunnels. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the deployment of the monitoring system in this invention;

[0020] Figure 2 This is a schematic diagram of the underground cross-section after the gridding of the present invention;

[0021] Figure 3 This is the first time that apparent resistivity data has been collected in this invention;

[0022] Figure 4 It is based on Figure 3 Data inversion yields a profile image of the baseline resistivity grid data matrix;

[0023] Figure 5 This is a graph of apparent resistivity data collected at interval t in this invention;

[0024] Figure 6 It is based on Figure 5 Data inversion yields a profile image of the resistivity grid data matrix at time t;

[0025] Figure 7 This is the gradient diagram of the cross-sectional resistivity change at time t in this invention;

[0026] Figure 8 This is a timeline diagram of electrical data monitoring and processing in this invention. Detailed Implementation

[0027] The present invention will be further described below.

[0028] like Figure 1 As shown, the specific steps of the present invention are as follows:

[0029] Step 1: Deploy the monitoring system. First, determine the location of the boundary line. One side of the boundary line is the monitoring area. Lay out a row of electrode measuring lines on the boundary line. The distance between two adjacent measuring electrodes on the electrode measuring line is 1 to 3 meters. Each measuring electrode on the electrode measuring line is connected to the electrical resistivity monitoring instrument to complete the deployment of the electrical resistivity monitoring system.

[0030] Step 2, Initial Electrical Resistivity Monitoring: The electrical resistivity monitoring system is activated for the initial monitoring. The initially acquired apparent resistivity data is used as the initial apparent resistivity data. Next, the subsurface profile where the electrode survey line is located is obtained, such as... Figure 2 As shown, the profile is meshed into an m*n rectangular grid, and the initial apparent resistivity data is inverted based on each grid, as follows. Figure 3 and 4 As shown, the resistivity grid data matrix of this profile is obtained as the baseline resistivity grid data matrix R. b ;

[0031] Step 3, Re-electro-electrical resistivity monitoring: After the initial data acquisition, perform a second electrical resistivity measurement at an interval t. Use the measured apparent resistivity data to perform resistivity inversion based on the grid defined in Step 2, such as... Figure 5 and 6 As shown, the resistivity grid data matrix R is obtained. t This serves as the resistivity grid data matrix for the profile at time t;

[0032] Step 4, Electrical resistivity data processing: Process the resistivity data matrix R from Step 3. t Compared with the baseline grid data matrix R in step two b Gradient calculation yields the resistivity gradient matrix R. dt The specific formula is as follows:

[0033]

[0034] In the above formula, R t Let R be the resistivity data matrix for each grid at time t, where R represents the resistivity value of each grid. b For the baseline resistivity data matrix, R b (i, j) is R b The value of the i-th row and j-th column of the matrix, R t (i, j) is R t The value of the i-th row and j-th column of the matrix, R dt Let be the gradient matrix of resistivity change for each grid at time t;

[0035] The matrix R dt Generate images, such as Figure 7 As shown, the gradient map of resistivity change at time t is obtained; then the matrix R is... t As the new baseline resistivity data matrix Rb Then, steps three and four are repeated every time interval t, where t is 24 hours. Figure 8 As shown, resistivity gradient data R for each time period is obtained. d ;

[0036] Step 5: Identify the intrusion tunnel: Analyze the resistivity gradient data R obtained in Step 4 for each time period. d The analysis determines whether high anomaly areas in the resistivity inversion profile are intrusion tunnels, ultimately distinguishing between intrusion tunnels and high-resistivity interference caves. Specifically, this involves setting resistivity gradient data R for each time period. d Twice the standard deviation σ plus the resistivity gradient data R over that time period d The average value is the threshold for that time period; if there is a region in the profile resistivity change gradient map obtained in a certain time period that exceeds the threshold of that time period, it is regarded as a high anomaly area. Then, the gradient data of 3 to 5 consecutive time periods after that time period are obtained. If the gradient data of the high anomaly area in each of the above time periods is consistently higher than the threshold corresponding to each time period, then the location of the high anomaly area is determined to be the location of the intrusion tunnel; otherwise, the high anomaly area is judged to be a high-resistivity interference tunnel.

[0037] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring intrusion tunnels based on resistivity change gradient, characterized in that, The specific steps are as follows: Step 1: Deploy the monitoring system: First, determine the location of the boundary line. One side of the boundary line is the monitoring area. Lay out a row of electrode measuring lines on the boundary line. Each measuring electrode on the electrode measuring line is connected to the electrical resistivity monitoring instrument to complete the deployment of the electrical resistivity monitoring system. Step 2, Initial Electrical Resistivity Monitoring: The electrical resistivity monitoring system is activated for the initial monitoring. The initially acquired apparent resistivity data is used as the initial apparent resistivity data. Next, the subsurface profile where the electrode survey line is located is acquired and gridded. The initial apparent resistivity data is then inverted based on each grid to obtain the resistivity grid data matrix of the profile, which serves as the baseline resistivity grid data matrix. ; Step 3: Re-evaluation using electrical resistivity: After the initial data acquisition, perform another electrical resistivity measurement at an interval t. Use the measured apparent resistivity data to perform resistivity inversion based on the grid defined in Step 2, obtaining the resistivity grid data matrix. This serves as the resistivity grid data matrix for the profile at time t; Step 4: Electrical resistivity data processing: Process the resistivity data matrix from Step 3. With the baseline grid data matrix in step two Perform gradient calculations to obtain the resistivity gradient matrix. and the matrix Generate an image to obtain the gradient map of the profile resistivity change at time t; then, the matrix... As a new baseline resistivity data matrix Then, steps three and four are repeated every time interval t to obtain the resistivity gradient data R for each time period. d The specific formula for gradient calculation is as follows: In the above formula, Let be the resistivity data matrix for each grid at time t, where each grid contains the resistivity value. This is the baseline resistivity data matrix. (i, j) is The value of the i-th row and j-th column of the matrix. (i, j) is The value of the i-th row and j-th column of the matrix. Let be the gradient matrix of resistivity change for each grid at time t; Step 5: Identify the intrusion tunnel: Analyze the resistivity gradient data R obtained in Step 4 for each time period. d The analysis determines whether high anomaly areas in the resistivity inversion profile are intrusion tunnels, ultimately distinguishing between intrusion tunnels and high-resistivity interference caves. Specifically, this involves setting resistivity gradient data R for each time period. d Twice the standard deviation σ plus the resistivity gradient data R over that time period d The average value is the threshold for that time period. If there is a region in the profile resistivity change gradient map obtained in a certain time period that exceeds the threshold of that time period, it is regarded as a high anomaly area. Then, the gradient data of multiple consecutive time periods after that time period are obtained. If the gradient data of the high anomaly area in each of the above time periods is consistently higher than the threshold corresponding to each time period, then the location of the high anomaly area is determined to be the location of the intrusion tunnel; otherwise, the high anomaly area is judged to be a high-resistivity interference tunnel.

2. The method for monitoring intrusion tunnels based on resistivity change gradients according to claim 1, characterized in that, In step one, the distance between two adjacent measuring electrodes on the electrode measuring line is 1~3m.

3. The method for monitoring intrusion tunnels based on resistivity change gradients according to claim 1, characterized in that, In step two, the cross-section is meshed to form an m*n rectangular grid arrangement.

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