Array resistivity imaging method suitable for multi-scene embankment hidden danger detection

Abstract

The invention discloses an array resistivity imaging method suitable for multi-scene embankment hidden danger detection, and belongs to the technical field of engineering exploration. On the basis of electrode arrangement of a traditional array resistivity method, an electrode used by the traditional array resistivity method is replaced with a patch type electrode capable of selecting a coupling agent. The measuring line can carry out data acquisition at positions where a traditional array resistivity method cannot be arranged, such as a vertical wall body, a hardened road surface, a building top surface and the like, and a data processing means for eliminating the influence of a hardened layer is provided to improve the resolution of data. And a nonlinear apparent resistivity inversion method based on improved shuffled frog leaping is provided for improving the inversion precision so as to cope with the influence of a complex building structure on acquired data. According to the method, the application range and the application scene of the array resistivity method are expanded, the influence of a building structure on measured data is eliminated, the signal-to-noise ratio of the measured data is improved, the inversion precision is improved, and an inversion result can be better judged and decoded.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of engineering exploration, and particularly relates to an array resistivity imaging method suitable for dam hidden danger detection in multiple scenes. BACKGROUND

[0002] The resistivity method (electrical method) exploration is one of important methods in geophysical exploration. It is a geophysical exploration method for solving engineering problems by observing and analyzing the change rule of electric field distribution based on the electrical difference of medium. The array resistivity method is an array electrical method exploration method. The occurrence form of a target body is inferred according to the distribution change of conduction current of the target body under the action of an applied electric field. The medium distribution in the detection range is judged according to the measured apparent resistivity contour map. The array resistivity method has good and wide application in structure analysis of engineering buildings, hidden danger detection and the like.

[0003] During the operation of earth-rock dams, hidden dangers such as concentrated seepage, cracks, ant nests and animal holes are prone to occur due to the damage of animals, the damage of anti-seepage walls, the uneven settlement of foundations, and the construction of buildings near and through the dam, etc. When the dam has hidden dangers and engineering measures are not taken in time to eliminate the dangers, the safe operation of the dam will be greatly affected. Therefore, timely and accurate detection of the position of the dam hidden danger is an effective method to curb the development of danger and ensure the safe and stable operation of the dam.

[0004] In the array resistivity method exploration, the stable contact of the electrode with the surface of the target medium is the core prerequisite for ensuring the effective injection of current and the accurate data acquisition. The traditional electrode cannot be efficiently and non-destructively applied in the scene of a hardened surface (such as a building wall, a concrete structure, and a rock mass). In the actual work of dam hidden danger detection, there are often a large number of hardened surfaces, such as the inside of the wall near the dam and the wall through the dam. Since the traditional electrode cannot be directly fixed on the wall surface, the measuring line cannot be arranged in a straight line due to the influence of the building structure, the resolution of the electric signal is significantly reduced due to the influence of the hardened layer, the effective detection depth is significantly reduced, and the like. This leads to low detection work efficiency, poor data acquisition accuracy, and significantly reduced effective detection depth. SUMMARY

[0005] The technical problem to be solved by the present application is to provide an array resistivity imaging method suitable for dam hidden danger detection in multiple scenes, expand the application scene of the array resistivity method, especially improve the signal-to-noise ratio and effective detection depth of data acquisition in the wall near the dam and the wall through the dam with generally hardened surfaces and complex working conditions, solve the problems of difficult wiring of the array resistivity method in the hardened surface layer and weakening of the collected electric signal by the hardened layer, solve the problem of insufficient accuracy of the current linear resistivity inversion method, and solve the problem that the array resistivity method cannot efficiently and accurately reflect the medium distribution characteristics under the hardened surface in a complex working condition environment.

[0006] The application is adopted with the following technical solutions to solve the above technical problems: Technical scheme: To achieve the purpose of the application, the technical scheme adopted by the application is: The array resistivity imaging method suitable for multi-scene embankment hidden danger detection comprises a host, a plurality of patch electrodes and a plurality of wires, the host is an array resistivity measuring instrument; On the basis of arranging electrodes in the traditional array resistivity method, the patch electrodes are used to replace the original electrodes, so that the measuring line can be arranged on the hardened surface which is difficult to arrange in the traditional method, and a data processing method is provided to eliminate the influence of the hardened layer on the apparent resistivity and amplify the electric signal; The nonlinear resistivity inversion method based on the improved hybrid frog leap is used to improve the inversion accuracy, to reduce the influence of complex building structures on the collected data, to expand the application range and application scene of the array resistivity method, to eliminate the influence of building structures on the measured data, and to improve the signal-to-noise ratio of the measured data.

[0007] As a further preferred scheme of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application, the data processing method, i.e., the hardened layer influence elimination and the nonlinear resistivity inversion method based on the improved hybrid frog leap, specifically comprises the following steps: Step 1, determine the working surface of the measuring line arrangement, select the number of electrodes, electrode spacing, isolation coefficient and device type according to the size and depth of the detection target, and select a suitable coupling agent; Step 2, check the ground resistance of each electrode, if the ground resistance is greater than the threshold value, inject salt water between the electrode and the ground to reduce the resistance, and start measuring after power on, observe the measurement value at any time during measurement, and inject salt water to reduce the resistance in time when abnormal value appears; Step 3, after completing the measurement, the thickness of the hardened layer is known through building design drawings and on-site observation, the corrected apparent resistivity is obtained through the hardened layer thickness and the measured apparent resistivity, the influence of the hardened layer is eliminated, and the electric signal is amplified; Step 4, after the influence of the hardened layer is eliminated, the corrected apparent resistivity is inverted by using the least square method with smoothing constraint and the nonlinear inversion method based on the improved hybrid frog leap, and the distribution of underground medium is judged according to the two inversion results.

[0008] Compared with the prior art, the application has the following technical effects: The array resistivity imaging method suitable for multi-scene embankment hidden danger detection provided by the application replaces the steel drill used by the array resistivity method with a patch electrode, so that the electrode has the ability to be easily carried, improves work efficiency, and the patch electrode can be used for measurement in areas such as a concrete pavement, a vertical embankment wall, a tunnel or a top of a building body, which further expands the application scenarios of the array resistivity method; the hardening layer influence elimination method mentioned in the application obtains the distortion degree of the apparent resistivity after the influence of the hardening layer through an analytical solution, and corrects the distortion degree by a certain ratio or interpolation, so as to eliminate the influence of the hardening layer on the gain of the electric signal, and the improved hybrid frog leap-based nonlinear inversion method is used to improve the inversion accuracy, and efficiently and accurately reflect the distribution characteristics of the medium under the hardening surface in a complex working condition environment. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 is a patch electrode schematic diagram of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application; Figure 2 is a measurement line arrangement schematic diagram of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application; Figure 3 is a hardening layer influence elimination effect schematic diagram of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application; Figure 4 is a hybrid frog leap-based nonlinear inversion flowchart of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application; Figure 5 is a hybrid frog leap-based nonlinear inversion result schematic diagram of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application; wherein, (a) is a least square, (b) is a standard frog leap, and (c) is an improved frog leap; Figure 6 is a measurement area position and measurement line schematic diagram of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application Figure 7 is a field data collection measurement line arrangement schematic diagram of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application, which is a sinking shop under the Qinhuai (Qinhuai River); Figure 8 is a hardening layer influence elimination effect schematic diagram of the array resistivity imaging method suitable for multi-scene embankment hidden danger detection of the application. DETAILED DESCRIPTION

[0010] The technical solutions of the application will be further described in detail below with reference to the drawings: With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort are within the protection scope of the present application. The purpose and effects of the present application will become more apparent from the following detailed description of the preferred embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

[0011] The present application provides an array resistivity imaging method suitable for multi-scene dike hidden danger detection, which expands the application scene of the array resistivity method, especially improves the signal-to-noise ratio and effective detection depth of data collected in generally hardened surface, embankment and through embankment buildings with complex working conditions, solves the problems of difficult wiring in the hardened surface layer and weakened collection of electric signals by the hardened layer, solves the problem of insufficient precision of the current linear resistivity inversion method, and solves the problem that the array resistivity method cannot efficiently and accurately reflect the distribution characteristics of the medium under the hardened surface in the complex working condition environment.

[0012] An array resistivity imaging method suitable for multi-scene dike hidden danger detection, comprising a host and a plurality of composite patch electrodes, a plurality of wires, the host being an array resistivity measuring instrument. The composite patch electrode is as shown in Figure 1 The measurement line arrangement schematic diagram is as shown in Figure 2 When arranging the measurement line, a suitable coupling agent is selected considering the spatial relationship between the measurement target and the measurement line arrangement surface. After completing the measurement line arrangement, the ground resistance of the electrode is ensured to be at a low level. During the measurement process, the ground resistance of the electrode should be paid attention to at all times, and the pouring of salt water is considered to improve the conductivity.

[0013] The hardened layer influence elimination effect schematic diagram is as shown in Figure 3 After completing the data collection, the apparent resistivity is corrected according to the thickness of the hardened layer. After the correction is completed, the result is inverted by using the non-linear resistivity inversion method based on the improved hybrid frog jump. Finally, the distribution of the underground medium is interpreted according to the inversion result.

[0014] The multi-scene embankment hidden danger array resistivity imaging detection method provided by the application replaces the steel drill used by the array resistivity method with a patch electrode, so that the electrode has the ability to be easily transported, improves work efficiency, and the patch electrode can be used for measurement in areas such as concrete road surfaces, vertical embankment walls, tunnels or building top bodies, which further expands the application scenarios of the array resistivity method; the hardening layer influence elimination method mentioned in the application obtains the distortion degree of the apparent resistivity after the influence of the hardening layer through an analytical solution, and corrects it by a certain ratio or interpolation, so as to eliminate the influence of the hardening layer on the gain of the electric signal, and at the same time, the improved mixed frog leap-based nonlinear inversion method is used to improve the inversion accuracy, and the problem of efficiently and accurately reflecting the medium distribution characteristics under the hardening surface under complex working conditions is solved.

[0015] As Figure 4 shown, according to the hardening layer influence elimination, the improved mixed frog leap-based nonlinear resistivity inversion method is used for inversion of the result, including the following steps: Step 1: complete measurement to obtain apparent resistivity, which can be obtained according to the following formula and steps (1) according to Maxwell's equation, calculate the change of current density affected by the hardening layer, the formula is as follows: = wherein, is the current density when the ground is horizontal and the underground medium is homogeneous and isotropic, is the change of current density caused by the target body, is the distortion of current density caused by the interference (hardening concrete layer) at the measurement electrodes M and N.

[0016] (2) through step (1), the formula of the apparent resistivity is as follows: wherein, is the apparent resistivity, is the resistivity of the measurement electrode.

[0017] (3) through step (2), the formula of the apparent resistivity after distortion correction is as follows: wherein, is the measured apparent resistivity, is the apparent resistivity caused by the interference, is the resistivity of the surrounding rock.

[0018] (3) through step (3), the formula of the apparent resistivity of the target body is as follows: After the corrected apparent resistivity is obtained, the ratio of the hardened layer thickness to the electrode spacing under the maximum allowable error rate needs to be considered, and the hardened layer thickness can be easily known through building design drawings and on-site observation, (4) The formula of the relationship between the ratio of the hardened layer thickness to the electrode spacing and the maximum isolation coefficient under different maximum allowable error rates is as follows: wherein, is the maximum isolation coefficient, is the maximum allowable error rate, is the hardened layer thickness, is the electrode spacing.

[0019] Through the above steps, the influence of the hardened layer can be eliminated under the condition of limiting the maximum error rate, so as to achieve the purposes of increasing the electric signal, increasing the effective detection depth, and improving the data signal-to-noise ratio.

[0020] Second step: after the elimination of the influence of the hardened layer, the corrected apparent resistivity is inversed by using the least square method with smoothing constraint (traditional linear method) and the nonlinear inversion method based on improved hybrid frog leap. The principle of the traditional linear method is not repeated, and the specific principle and implementation steps of the nonlinear inversion method based on improved hybrid frog leap are as follows: (1) Initialize the population. Randomly generate an initial frog population (solution set), the number of which is , and each frog represents a feasible solution (d-dimensional vector). The total number of iterations of the frog population is , and the number of iterations of the subgroup is .

[0021] (2) Calculate the fitness of all frogs, sort the frogs from good to bad according to the fitness value, and distribute them to m gene groups (subgroups) according to certain rules, each group containing n frogs, , the distribution method is: the first frog enters the first group, the second frog enters the second group, …, the th frog enters the th group; the th frog returns to the first group, and so on. This distribution method can maintain the diversity of frogs in each group (mixing of good and bad).

[0022] (3) In the subgroup, the frog updates its own position by learning and imitating the behavior of other frogs with better position information (especially the better frogs), so that its fitness is improved. In the current gene group, mark the frog with the best fitness ( ) and the worst frog ( ).

[0023] (4) Update the position of the worst frog by jumping to the best frog to improve the worst solution: where, represents the jump step, i.e., the random weight, represents the current iteration number within the sub-population, and ii represents the current iteration number of the population; The distance of the worst frog after moving is: where, is the upper limit of the step size, obeys the standard normal distribution with a mean of 0 and a variance of 1.

[0024] (5) If the new solution is better than the old solution in step (4), use the new solution to replace the old solution; if the step (4) update fails to improve , replace with the global optimal solution of the population and recalculate.

[0025] (6) If neither the new solution obtained in step (4) nor the new solution obtained in step (5) can improve , generate a new solution randomly to replace .

[0026] (7) After each sub-population completes its local search process, all individuals of the sub-populations will be recombined into a complete population, at this time, the algorithm will perform information exchange in the global range (global exploration), and reordering according to the fitness values of all individuals, while updating the position information of the optimal individual in the whole population (8) The algorithm ends. If the number of iterations reaches the pre-defined maximum number of iterations, terminate the calculation and output the optimal result; otherwise, re-execute step (2). Generally, the hybrid frog jumping algorithm program terminates when the pre-defined global maximum number of iterations is reached and the fitness value does not change or changes very little. The results of nonlinear inversion based on improved hybrid frog jumping are shown in Figure 5 , where (a) is the least square, (b) is the standard frog jump, and (c) is the improved frog jump. In summary, by introducing random disturbance obeying normal variation in the local search process and introducing adaptive moving operator in the update of moving step, the ability to jump out of local optimum and the convergence efficiency of the algorithm are improved, thereby improving the overall performance of the algorithm.

[0027] The collected apparent resistivity is inverted by the above steps to further improve the accuracy of inversion, and the distribution of the underground medium is more accurately interpreted by combining the linear and nonlinear inversion results.

[0028] The following is an example of detecting the distribution of the medium behind the vertical wall in the Qinhuai River (Shui Mu Qinhuai) using the multi-scene embankment hidden danger array resistivity imaging detection method. The location of the work area is shown in Figure 6 .

[0029] In the embankment building-Shui Mu Qinhuai (Qinhuai River) measurement area, a wall array resistivity method measurement line 1 was arranged, 43 electrodes were arranged on the measurement line, the electrode spacing was 1 m, the maximum isolation coefficient was 10, and the actual arrangement of the measurement line is as follows.

[0030] The field data acquisition measurement line arrangement diagram of Shui Mu Qinhuai (Qinhuai River) sunken shops is shown in Figure 7 , and the measurement line results in the embankment building-Shui Mu Qinhuai (Qinhuai River) measurement area after eliminating the influence of the hardened layer are as follows. The effect diagram of eliminating the influence of the hardened layer is shown in Figure 8 . The upper diagram is not eliminated, and the lower diagram is eliminated.

[0031] It can be found that after eliminating the influence of the hardened layer gain signal, the inversion effect of the apparent resistivity has been greatly improved, and the originally disordered high resistance area has been effectively suppressed, so that the following interpretation can be made: The low resistance 1 area may be the gap between the concrete and the rear wall due to rainwater leaching, the low resistance 2 area may be a water passage, and the low resistance 3 area is an uneven part in the wall, which is speculated to be due to high water content. The low resistance 2 area is verified to be true, and it can be seen that the effect of the proposed data processing method is very good.

[0032] Those skilled in the art can understand that the above description is only a preferred example of the application and is not intended to limit the application. Although the application has been described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions described in the foregoing examples or make equivalent replacements for part of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application. All technical features in the embodiments can be freely combined according to actual needs.

[0033] Finally, it should be noted that: the above description is only a preferred embodiment of the application and is not intended to limit the application. Although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent replacements for part of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. An array resistivity imaging method applicable to the detection of hidden dangers in dikes in multiple scenarios, characterized in that: It includes a main unit, multiple patch electrodes, and multiple wires, wherein the main unit is an array resistivity measuring instrument; Based on the traditional array resistivity method for arranging electrodes, patch electrodes are used to replace the original electrodes, allowing the test lines to be arranged on hardened surfaces that are difficult to arrange using traditional methods. Furthermore, a data processing method is provided to eliminate the influence of the hardened layer on the apparent resistivity and to enhance the electrical signal. The accuracy of inversion is improved by using an improved hybrid frog-jump nonlinear resistivity inversion method. This method is used to reduce the impact of complex building structures on the acquired data, expand the applicability and application scenarios of the array resistivity method, eliminate the influence of building structures on the measurement data, and improve the signal-to-noise ratio of the measured data.

2. The array resistivity imaging method for detecting hidden dangers in dikes in multiple scenarios according to claim 1, characterized in that: The data processing method, namely the hardened layer effect elimination and the nonlinear resistivity inversion method based on the improved hybrid frog jumping method, specifically includes the following steps: Step 1: Determine the working surface for the survey line layout; select the number of electrodes, electrode spacing, isolation coefficient, and device type according to the size and depth of the target being detected; and select a suitable coupling agent. Step 2: Check the grounding resistance of each electrode. If the grounding resistance is greater than the threshold, inject salt water between the electrode and the ground to reduce the resistance. Power on and start the measurement. Observe the measurement value at all times during the measurement. If an abnormal value occurs, inject salt water in time to reduce the resistance. Step 3: After completing the measurement, the thickness of the hardened layer can be easily determined through architectural design drawings and on-site observation. The corrected apparent resistivity is obtained by comparing the thickness of the hardened layer with the measured apparent resistivity, thus eliminating the influence of the hardened layer and increasing the gain of the electrical signal. Step 4: After eliminating the influence of the hardened layer, the corrected apparent resistivity is inverted using the least squares inversion method with smoothing constraints and the nonlinear inversion method based on the improved hybrid frog leap method. The distribution of the underground medium is interpreted by combining the two inversion results.

3. The array resistivity imaging method for detecting hidden dangers in dikes in multiple scenarios according to claim 1, characterized in that: The patch electrode is a specially designed composite electrode used to be arranged on a hardened surface, and consists of a metal electrode patch, a wire, and a metal clip.

4. The array resistivity imaging method for detecting hidden dangers in dikes in multiple scenarios according to claim 1, characterized in that: The nonlinear resistivity inversion method based on the improved hybrid frog leap method specifically includes the following steps; This method simulates the foraging behavior of frog groups in a rocky swamp to find a better solution. An adaptive movement factor is introduced as an improvement, and the grouping strategy is modified. While maintaining the original advantages of strong global search capability, relative insensitivity to the initial solution, and good flexibility, it improves the shortcomings of slow convergence speed and susceptibility to local optima. When applied to resistivity inversion, it further enhances the accuracy of the inversion. The improved grouping measurement is as follows: After sorting M×N individuals according to fitness, the first to M individuals are randomly assigned to each group (1 individual per group), the (M+1) to (2×M) individuals are randomly assigned to each group (1 individual per group), and the subsequent assignment method is similar until the (M-1)×N+1 to (M×N) individuals are randomly assigned to each group (1 individual per group). The improved adaptive movement operator is: in, ii represents the current iteration number within the subpopulation, and ii represents the current iteration number of the population. Take 0.5; The search process is as follows: (1) Initialize the population: Randomly generate an initial frog population, i.e., the solution set, with a quantity of... Each frog represents a feasible solution, i.e., a d-dimensional vector. The total number of iterations for the frog population is... The number of subgroup iterations is ; (2) Calculate the fitness of all frogs, sort the frogs from best to worst fitness value, and assign them to m meme groups (subgroups) according to certain rules. Each subgroup contains n frogs. The allocation method is as follows: the first frog enters group 1, the second frog enters group 2, ..., and so on. Only enter the first Group; No. Return only to group 1, and so on; (3) Within a subgroup, frogs update their own position by learning from and imitating the behavior of other frogs with better location information, thereby improving their fitness. In the current meme group, the frog with the best fitness is marked. And the worst frog ; (4) Update the position of the worst frog and improve the worst solution by jumping towards the best frog: in, This indicates the jump step size, which is the random weight. ii represents the current iteration number within the subpopulation, and ii represents the current iteration number of the population. The worst distance the frog moves for: in, This is the upper limit of the step size. The distance the frog moved. It follows a standard normal distribution with a mean of 0 and a variance of 1. (5) If the fitness of the new solution is better than that of the old solution in step (4), then the new solution is used to replace the old solution; if the update in step (4) fails to improve the fitness, then the new solution is used instead of the old solution. Then use the population-global optimal solution. Alternative Recalculate: (6) If the new solutions obtained in steps (4) and (5) fail to improve the solution Then a new solution is randomly generated to replace it. ; (7) After each subpopulation completes its local search process, the individuals of all subpopulations will be merged back into a complete population. At this time, the algorithm will perform global information exchange, i.e., global exploration, and reorder the individuals according to their fitness values, while updating the best individual in the entire population. Location information; (8) Algorithm termination: If the number of iterations reaches the preset maximum number of iterations, the calculation is terminated and the optimal result is output; otherwise, step (2) is executed again; when the preset global maximum number of iterations and the fitness value no longer change or change very little, the hybrid frog jumping algorithm program terminates.

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