Method and system for integrity assessment of impermeable structures based on resistivity imaging

Abstract

The present application belongs to the technical field of anti-seepage structure evaluation, and particularly relates to an anti-seepage structure integrity evaluation method and system based on resistivity imaging, which comprises the following steps: firstly, collecting complex admittance data of monitoring points and extracting phase loss angle features to strip the interference of underground environment humidity fluctuation through phase information; then, constructing virtual part admittance gradient features in combination with structure anisotropy correction factors to enhance the recognition degree of small damage edges; calculating seepage probability weight by using reference gradient deviation, and identifying seepage clusters based on adaptive seed point screening and eight-neighbor connected domain marking algorithm to obtain continuous seepage area; finally, calculating integrity score through a nonlinear model by introducing risk sensitivity index and area penalty weight to realize accurate early warning and positioning. The present application effectively solves the problem of poor seepage recognition accuracy under complex background interference, and significantly improves the robustness and scientificity of the evaluation result.

Description

Technical Field

[0001] This invention relates to the field of anti-seepage structure assessment technology. More specifically, this invention relates to a method and system for assessing the integrity of anti-seepage structures based on resistivity imaging. Background Technology

[0002] In the fields of water conservancy seepage prevention projects, environmental sanitation landfills, and integrity monitoring of chemical storage facilities, seepage prevention structures are the core barrier to prevent the spillover of high-concentration pollutants and protect groundwater resources.

[0003] To ensure the reliability of the seepage prevention system during long-term operation, monitoring electrode arrays are usually installed in the overburden layer above the seepage prevention structure. The mainstream apparent resistivity detection technology injects direct current into the soil layer and collects the potential distribution. It uses the local resistivity distortion caused by the conductive path formed at the seepage membrane breakage point to locate the leakage point. This monitoring method based on resistivity characteristics has a certain degree of effectiveness in controlled environments such as laboratories and is the basic solution for online evaluation of seepage prevention structures in the engineering field.

[0004] However, in complex engineering environments, soil resistivity is highly sensitive to moisture content. Rainfall, seasonal fluctuations in groundwater levels, or local water accumulation caused by uneven compaction of the overburden layer can all cause drastic non-uniform shifts in background resistivity. These false electrical anomalies caused by fluctuations in environmental humidity often overlap with the current distortion caused by actual leakage points in terms of signal amplitude and spatial distribution characteristics. This makes it difficult for existing monitoring systems to accurately distinguish between the increase in local conductivity caused by increased soil moisture and the charge leakage caused by physical damage to the geomembrane.

[0005] Furthermore, existing assessment logic is mostly based on simple threshold judgments or linear weighting, failing to fully consider the complex admittance information exhibited by the underground medium under alternating electric fields. This results in a lack of sufficient identification dimensions during the signal extraction stage. When faced with minor damage or multi-point concentrated leakage, the fixed threshold judgment method is highly susceptible to environmental noise interference, leading to missed detections, or misjudging large-area humidity fluctuations as severe leakage. This ambiguity in signal characteristics caused by environmental fluctuations not only reduces the accuracy of leakage identification but also prevents the assessment system from providing a quantitative score that matches the degree of physical damage to the seepage prevention structure, severely restricting the reliability and specificity of safety early warning for seepage prevention projects. Summary of the Invention

[0006] To address the technical problems of existing technologies, which struggle to distinguish between environmental influences and actual leakage signals under conditions of humidity fluctuations and complex underground background interference, resulting in poor leakage identification accuracy and the inability to quantitatively assess structural integrity, this invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a method for assessing the integrity of a seepage-proof structure based on resistivity imaging, comprising: arranging a monitoring electrode array in the overburden layer above the seepage-proof structure; continuously acquiring response voltage and response current sequences at each monitoring point; obtaining complex admittance data at each monitoring point through fast Fourier transform; and extracting phase loss angle features at each monitoring point based on the complex admittance data; capturing local response jumps based on the second-order partial derivatives of the phase loss angle features with respect to spatial coordinates; calculating the squared difference between the phase loss angle features and the global average phase loss angle of the feature field to characterize the deviation; and weighting the local response jumps and the deviation using a structural anisotropy correction factor. The process involves coupling and constructing the imaginary admittance gradient features of each monitoring point; obtaining the leakage probability weight of each monitoring point based on the difference between the imaginary admittance gradient features and the baseline gradient feature values; acquiring the leakage probability weights of all monitoring points and arranging them in descending order of value; selecting candidate leakage seed points based on the anomaly screening quantile coefficient; identifying leakage clusters based on the candidate leakage seed points; and obtaining the area of ​​the continuous leakage region corresponding to each monitoring point by combining the grid cell area; obtaining the integrity assessment score of the seepage prevention structure based on the leakage probability weights, the area of ​​the continuous leakage region, and the total number of monitoring points; and performing leakage early warning and location positioning of the seepage prevention structure based on the integrity assessment score.

[0008] This invention deconstructs the signal from both phase and spatial gradient dimensions by introducing complex admittance measurement and imaginary admittance gradient features. It also eliminates the amplitude interference of soil moisture fluctuations on resistive components by utilizing phase loss angle features, thus solving the false alarm problem caused by background resistivity shifts. Furthermore, by combining adaptive seed point selection based on percentage distribution with connected domain area analysis, a nonlinear scoring model is constructed, enabling the assessment results to accurately reflect the true degree of damage to structural integrity caused by high-probability risk points and damage scale. This achieves closed-loop monitoring of seepage-proof structures from macroscopic health quantification to microscopic precise positioning in complex environments, significantly improving the robustness and scientific rigor of the assessment system.

[0009] Preferably, the complex admittance data of each monitoring point includes a real admittance component and an imaginary admittance component, wherein the real current component and the real voltage component of each monitoring point constitute the real admittance component, and the imaginary current component and the imaginary voltage component of each monitoring point constitute the imaginary admittance component; the real current component, the imaginary current component, the real voltage component, and the imaginary voltage component of each monitoring point are obtained by performing a fast Fourier transform on the response voltage sequence and the response current sequence.

[0010] Preferably, the formula for calculating the phase loss angle characteristics of each monitoring point is: In the formula, To monitor the phase loss angle characteristics of the monitoring points; It is the arctangent function; This is the real component of the current; This is the imaginary component of the voltage. This is the imaginary component of the current; This is the real component of the voltage.

[0011] This invention obtains the phase loss angle by calculating the ratio of the imaginary admittance component to the real admittance component, which can effectively filter out interference caused by changes in electrolyte concentration due to rainfall, and ensure high signal-to-noise ratio extraction of leakage signals in the phase domain.

[0012] Preferably, the formula for calculating the imaginary admittance gradient characteristics of each monitoring point is: In the formula, To monitor the imaginary admittance gradient characteristics of the monitoring points; For three-dimensional space, the Laplace operator is used. To monitor the phase loss angle characteristics of the monitoring points; This is the structural anisotropy correction factor, with a value ranging from 1.0 to 1.5; The global average phase loss angle of the feature field.

[0013] This invention introduces a three-dimensional spatial Laplacian operator and a structural anisotropy correction factor, and uses the spatial second derivative to capture local phase jumps, thereby enhancing the edge salience of minor damage and eliminating anisotropic artifacts caused by uneven electrode density.

[0014] Preferably, the formula for calculating the leakage probability weight of each monitoring point is: In the formula, Leakage probability weights for monitoring points; It is a natural exponential function; This is the gain sensitivity coefficient, and its value ranges from 10.0 to 20.0; To monitor the imaginary admittance gradient characteristics of the monitoring points; The baseline gradient eigenvalues ​​are used.

[0015] This invention sets the comparison zero point by using the benchmark gradient eigenvalue and constructs leakage probability weights using the Sigmoid mapping function, eliminating the absolute influence of environmental background on the weights and making the probability assessment more objective and valuable.

[0016] Preferably, obtaining the area of ​​the continuous leakage region corresponding to each monitoring point includes: selecting the leakage probability weight. The monitoring points within the top 5% are selected and marked as candidate leakage seed points. An eight-neighbor connected domain labeling algorithm is used to perform spatial growth with the candidate leakage seed points as the core, and the candidate leakage seed points that are spatially adjacent are classified into the same leakage cluster. The number of candidate leakage seed points contained in each leakage cluster is counted and combined with the area of ​​the grid cell to obtain the area of ​​the continuous leakage area corresponding to each monitoring point.

[0017] Preferably, the formula for calculating the integrity assessment score of the seepage-proof structure is: In the formula, Scoring for integrity assessment; This represents the total number of monitoring points; For the first Leakage probability weights for each monitoring point; This is a risk sensitivity index, with a value ranging from 1.5 to 3.0; This is the area penalty weight, and its value ranges from 5.0 to 15.0; For the first The area of ​​the continuous leakage zone to which each monitoring point belongs; This represents the total area of ​​the monitored region.

[0018] This invention constructs a nonlinear scoring formula that includes a risk sensitivity index and an area penalty weight. By imposing exponential penalties on high-probability failure points and large-area connected failures, it realistically simulates the physical process of the seepage barrier layer evolving from point leakage to overall failure, making the scoring more consistent with the actual lifespan of the project.

[0019] Preferably, the response voltage sequence and response current sequence of each monitoring point are acquired simultaneously by injecting an alternating current signal with a frequency range of 10Hz to 1kHz into the monitoring electrode array using a multi-channel complex impedance analyzer.

[0020] Preferably, the leakage warning and location of the seepage prevention structure are performed based on the integrity assessment score, including: when the integrity assessment score is lower than the preset alarm threshold three times in a row, it is determined that there is a real leakage risk in the seepage prevention structure and an early warning is triggered; using the spatial coordinates of each monitoring point and the corresponding imaginary admittance gradient features, a bilinear interpolation algorithm is used to generate a heat map of the leakage location in the area to be monitored.

[0021] In a second aspect, the present invention provides a system for assessing the integrity of a seepage-proof structure based on resistivity imaging, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned method for assessing the integrity of a seepage-proof structure based on resistivity imaging is implemented.

[0022] By adopting the above technical solution, the above-mentioned method for assessing the integrity of seepage prevention structures based on resistivity imaging is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. A terminal device can then be made based on the memory and processor for convenient use.

[0023] The beneficial effects of this invention are as follows:

[0024] This invention deconstructs the signal from both phase and spatial gradient dimensions by introducing complex admittance measurement and imaginary admittance gradient features. It also eliminates the amplitude interference of soil moisture fluctuations on resistive components by utilizing phase loss angle features, thus solving the false alarm problem caused by background resistivity shifts. Furthermore, by combining adaptive seed point selection based on percentage distribution with connected domain area analysis, a nonlinear scoring model is constructed, enabling the assessment results to accurately reflect the true degree of damage to structural integrity caused by high-probability risk points and damage scale. This achieves closed-loop monitoring of seepage-proof structures from macroscopic health quantification to microscopic precise positioning in complex environments, significantly improving the robustness and scientific rigor of the assessment system. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the method for assessing the integrity of a seepage-proof structure based on resistivity imaging in this invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] This invention discloses a method for assessing the integrity of seepage-proof structures based on resistivity imaging, referring to... Figure 1 This includes steps S1-S5:

[0029] S1: An array of monitoring electrodes is arranged in the overburden layer above the seepage-proof structure to continuously collect the response voltage and response current sequences at each monitoring point. The complex admittance data of each monitoring point is obtained through fast Fourier transform.

[0030] It should be noted that due to the complex electromagnetic interference and seasonal humidity fluctuations in the underground environment where the seepage prevention structure is located, traditional single-frequency apparent resistivity acquisition methods are prone to capturing false electrical anomalies, resulting in a low signal-to-noise ratio in the identification results. To separate environmental noise from structural leakage signals at the underlying logic level, this invention utilizes multi-frequency excitation signals to construct a complex electrode polarization manifold space. By extracting the response characteristics of the electrode-soil interface at different frequencies, a data foundation is laid for subsequent identification of the polarization capacitance characteristics of leakage channels.

[0031] Specifically, a monitoring electrode array is laid out on the surface of the soil layer covering the seepage prevention structure to be monitored according to a preset grid spacing, and the area of ​​the grid unit represented by each monitoring electrode is recorded; an alternating current signal with a frequency range of 10Hz to 1kHz is injected into the monitoring electrode array using a multi-channel complex impedance analyzer. This frequency band can cause the electrolyte channel at the seepage hole to produce significant complex impedance characteristics, i.e., frequency dependence; the response voltage sequence and response current sequence of each monitoring point are collected simultaneously.

[0032] Furthermore, a Fast Fourier Transform is performed on the acquired response voltage and response current sequences. Through frequency domain decomposition, the current and voltage responses are transformed into complex forms containing phase information. The complex current can be expressed as... Complex voltage can be expressed as , The imaginary unit is used; the real component of the current at each monitoring point is extracted. Imaginary part of current Real component of voltage and the imaginary part of voltage .

[0033] Furthermore, acquire the complex admittance data at each monitoring point. Complex admittance data It includes real and imaginary admittance components, where the real component of the current at each monitoring point... and the real component of voltage The real admittance component and the imaginary current component at each monitoring point constitute the real admittance component. and the imaginary part of voltage It forms the imaginary admittance component.

[0034] The spatial distribution of the real admittance component reflects the steady-state conductivity of the soil medium, while the imaginary admittance component captures the interfacial capacitance effect caused by ion enrichment at the damaged part of the geomembrane. When structural leakage exists, the imaginary admittance component exhibits a nonlinear rise with frequency, and its value increases with the increase of electrolyte concentration in the leakage channel. This allows the present invention to distinguish leakage points from ordinary damp areas from the perspective of physical mechanisms.

[0035] S2: Extract the phase loss angle characteristics of each monitoring point based on the complex admittance data.

[0036] It should be noted that fluctuations in water content caused by natural factors such as rainfall will generate a global background impedance drift above the seepage prevention structure, making a single impedance value unable to accurately characterize the degree of damage. To prevent the evaluation index from failing due to changes in the background environment, this invention introduces a phase loss angle feature. By quantifying the proportional relationship between the real and imaginary parts in energy dissipation, a robust evaluation operator is constructed that is insensitive to absolute resistivity values ​​but only sensitive to the microstructure of leakage.

[0037] Specifically, according to the complex number division formula:

[0038]

[0039] The tangent of the phase angle of the complex number obtained by this calculation formula is equal to the ratio of the imaginary part to the real part, which is... By performing an arctangent operation, the pure phase loss angle characteristic can be directly decoupled without relying on the intermediate variable of the complex impedance magnitude. .

[0040] Therefore, based on the real component of the current The imaginary component of the current The real component of the voltage and the imaginary part of the voltage Calculate the phase loss angle characteristics of each monitoring point. It describes the phase difference between complex voltage and complex current, and is used to characterize the complex admittance characteristics of the leakage channel; the specific calculation formula is:

[0041]

[0042] In the formula, To monitor the phase loss angle characteristics of the monitoring points; It is the arctangent function; This is the real component of the current; This is the imaginary component of the voltage; This is the imaginary component of the current; This is the real component of the voltage.

[0043] The calculation formula utilizes the geometric mapping logic of complex number operations, constructing a proportional function through the cross-product of the real and imaginary components. This automatically eliminates common-mode errors caused by amplitude fluctuations in the excitation source signal. When the geomembrane is intact, the electric field exhibits purely resistive characteristics, and the phase loss angle tends to zero. When leakage occurs, the enhanced double-layer polarization effect at the leakage pores leads to changes in the phase loss angle. Significant shifts occur, thus enabling highly sensitive identification of structural damage.

[0044] It should be noted that this invention defines the acquisition logic of phase loss angle characteristics by introducing complex number arithmetic rules, and constructs a mapping relationship from the physical meaning, i.e., interface polarization effect, to the mathematical expression, i.e., complex number division phase decoupling. This effectively avoids the numerical instability problem caused by computational path redundancy and significantly improves the positioning robustness in complex non-uniform electromagnetic field environments underground.

[0045] S3. Based on the phase loss angle characteristics, the global average phase loss angle of the characteristic field, and the structural anisotropy correction factor, the imaginary admittance gradient characteristics of the monitoring point are obtained.

[0046] It should be noted that when dealing with minor damage, the signal intensity of the phase loss angle feature is often submerged in the complex underground background texture, making it difficult to accurately delineate the edge. Furthermore, the deployment of monitoring electrode arrays is often limited by terrain, resulting in anisotropic deviations with varying aspect ratios, which can easily produce artifacts. Therefore, this invention constructs a spatial topological difference mechanism, uses gradient operators to capture local phase transitions, and introduces a correction factor to balance array density differences, thereby achieving significant enhancement of the edge of minor leaks.

[0047] Specifically, when the phase loss angle characteristic value of all monitoring points After the calculation is completed, a numerical matrix is ​​formed on the two-dimensional plane. Through the interpolation algorithm, the discrete phase loss angle eigenvalues ​​are transformed into a continuous surface distribution, forming a feature field with spatial distribution properties, so as to perform subsequent Laplace operator operations.

[0048] Furthermore, the Laplace operator is used to perform second-order difference operations on the spatially distributed characteristic field. In the normal geomembrane region, the electric field distribution is gentle, and the second-order difference is zero or minimal. At the leakage point, the current undergoes severe distortion, and the phase loss angle eigenvalue will show a sharp jump valley or peak. Therefore, the second-order difference can produce a very strong response to this sudden jump, thereby achieving edge enhancement.

[0049] Furthermore, the phase loss angle characteristics of all monitoring points within the monitoring area were statistically analyzed. Calculate their arithmetic mean to obtain the global average phase loss angle of the feature field. .

[0050] Furthermore, a structural anisotropy correction factor for the monitoring electrode array is defined. This value is used to characterize the impact of differences in the density of the monitoring electrode array in different directions on the calculation of the spatial derivative. If this value is set too small, strip-like artifacts will appear in the evaluation field in the direction of sparse electrodes; if it is set too large, it will smooth out tiny point-like damage features. Therefore, its value range is set to 1.0 to 1.5. In this embodiment, the structural anisotropy correction factor is used. The value is set to 1.25; in other embodiments, the implementer can adaptively set the value according to the arrangement spacing ratio of the monitoring electrode array.

[0051] Furthermore, the imaginary admittance gradient characteristics of the monitoring points are calculated. The formula for its calculation is:

[0052]

[0053] In the formula, To monitor the imaginary admittance gradient characteristics of the monitoring points; For three-dimensional space, the Laplace operator is used. To monitor the phase loss angle characteristics of the monitoring points; The second-order partial derivative of the phase loss angle feature with respect to spatial coordinates is used to capture local response jumps. This is a structural anisotropy correction factor; The global average phase loss angle of the feature field; The deviation is represented by the squared difference between the characteristic phase loss angle and the global average phase loss angle of the characteristic field.

[0054] This formula achieves feature extraction through nonlinear superposition; the first term utilizes the three-dimensional Laplacian operator. Phase loss angle features Taking the second-order spatial derivative, when leakage holes exist, the phase loss angle characteristic is... A dramatic spatial jump occurs at the edge of the hole, causing a significant increase in the calculated value of the first term, thus accurately pinpointing the damage boundary; the second term uses a structural anisotropy correction factor. Weighted calculation of phase loss angle features Compared with the global average phase loss angle The square of the difference is used to enhance the contrast between outliers and the background; when the imaginary admittance gradient feature The higher the value, the more likely the monitoring point is on the verge of a structural abrupt change.

[0055] It should be noted that the imaginary admittance gradient feature constructed in this invention significantly enhances the recognition accuracy of minute damage edges through spatial topological difference, and can effectively distinguish between local structural damage and natural material aging.

[0056] S4. Based on the difference between the imaginary admittance gradient feature and the baseline gradient feature value, obtain the leakage probability weight of each monitoring point; based on the leakage probability weight, the area of ​​the continuous leakage area and the total number of monitoring points, obtain the integrity assessment score of the seepage prevention structure.

[0057] It should be noted that the integrity of the seepage barrier structure is not a simple linear sum of the probabilities of leakage at each point. The damage to the barrier function caused by high-probability local breakdown points is far greater than that caused by low-probability suspected points, and large-area continuous damage can lead to structural failure risks, rather than a simple accumulation of isolated small holes. In order to achieve automated assessment and eliminate the interference of environmental background noise on threshold setting, an adaptive feature screening and spatial clustering identification mechanism must be established. Therefore, this invention provides a more practical quantitative health index by constructing a seed point extraction algorithm based on percentage distribution and a nonlinear damage mapping model.

[0058] Specifically, based on the imaginary admittance gradient characteristics of the monitoring points Obtain its leakage probability weight The formula for its calculation is:

[0059]

[0060] In the formula, Leakage probability weights for monitoring points; It is a natural exponential function; This is the gain sensitivity coefficient; To monitor the imaginary admittance gradient characteristics of the monitoring points; The baseline gradient eigenvalues ​​are used.

[0061] In this formula, the baseline gradient eigenvalues These are the baseline statistical values ​​of the imaginary admittance gradient characteristics of each monitoring point, measured during the initial system installation and under conditions where no leakage has been confirmed. These values ​​are used to provide a comparison zero point; the gain sensitivity coefficient... This is an adjustment parameter set to balance the steepness of the leakage probability mapping with the ability to suppress environmental background noise. If the value is set too small, the discrimination of the leakage probability weight is insufficient, resulting in an overly smooth scoring result that cannot accurately identify discrete and small leakage points. If the value is set too large, the system will become overly sensitive to small random fluctuations in the imaginary admittance gradient, producing spurious scoring anomalies. Therefore, its value range is set to 10.0 to 20.0. In this embodiment, it is set to 15.5 to retain the true leakage characteristics while suppressing background clutter. In other embodiments, the implementer can set the value according to the compaction of the overburden soil or the range of dielectric constant fluctuations.

[0062] Among them, when the imaginary admittance gradient feature Much larger than the baseline gradient eigenvalue At that time, leakage probability weight A value close to 1 objectively reflects the absolute degree of degradation of material properties.

[0063] Furthermore, continuous leakage areas at each monitoring point are determined through spatial attribute correlation. The leakage probability weights of all monitoring points within the monitoring area are then obtained. And weight the leakage probability of all monitoring points. Sort by numerical value from largest to smallest; select leakage probability weights. Monitoring points within the top 5% are selected and marked as candidate leakage seed points. An eight-neighbor connected component labeling algorithm is used, with the candidate leakage seed points as the core for spatial growth. This involves determining whether each seed point has adjacent candidate leakage seed points in its eight-neighbor direction; if so, they are grouped into the same leakage cluster. The number of candidate leakage seed points in each leakage cluster is counted, and combined with the grid cell area, the area of ​​the continuous leakage region corresponding to each monitoring point is obtained. .

[0064] Furthermore, the integrity assessment score of the seepage prevention structure is calculated. The formula for its calculation is:

[0065]

[0066] In the formula, Scoring for integrity assessment; This represents the total number of monitoring points; For the first Leakage probability weights for each monitoring point; As a risk sensitivity index; Area penalty weight; For the first The area of ​​the continuous leakage zone to which each monitoring point belongs; This represents the total area of ​​the monitored region.

[0067] In this formula, the total number of monitoring points The homogenization process ensures that the scores are within the standard range.

[0068] The risk sensitivity index is a sensitivity parameter set to balance the severity of punishment for high-risk points with the robustness of the assessment results. If the value is set too small, the system will not adequately deduct the weight of high-probability leakage points, making it difficult to differentiate the numerical difference between local breakdown points and the normal background, resulting in the assessment score not reflecting the actual damage intuitively. If the value is set too large, it will produce excessive non-linear punishment, causing a significant drop in the score even for a very small number of suspected anomalies, resulting in a disconnect between the integrity assessment results and the actual physical life. Therefore, its value range is set to 1.5 to 3.0, and in this embodiment it is set to 2.0 to ensure that the mathematical logic accurately focuses on significant anomalies and reflects the non-linear punishment for high-probability leakage points. As the value approaches 1, the deduction amount increases rapidly with the exponent; in other embodiments, implementers can make flexible adjustments based on the safety level of the seepage prevention project.

[0069] The area penalty weight is a penalty parameter set to balance the mapping relationship between the scale effect of leakage and the rate of score decline. If this value is set too small, the system's response sensitivity to large-area clustered leakage is insufficient, failing to reflect the serious threat of continuous damage to structural integrity. If it is set too large, it will lead to an overreaction to small, localized, multi-point damage, causing the score results to fluctuate too rapidly under complex conditions. Therefore, its value range is set to 5.0 to 15.0, and in this embodiment, it is set to 10.0 to adjust the contribution of the area factor to the total score. account for the total area When the proportion increases, the integrity assessment score The leakage rate decreased significantly, realistically simulating the process of localized point leakage evolving into structural failure; in other embodiments, implementers can make adaptive settings based on the tensile strength or elongation index of the waterproofing material.

[0070] S5. Based on the integrity assessment score, leak warning and location are provided for the seepage prevention structure.

[0071] It should be noted that while quantitative scoring can reflect the overall trend, it cannot provide intuitive guidance for repair and construction. Furthermore, in actual engineering projects, a certain tolerance must be maintained for low-risk fluctuations. By setting alarm thresholds and using a heat map visualization mechanism, a closed-loop monitoring system from macro-level early warning to micro-level positioning is achieved, ensuring the timeliness and accuracy of engineering maintenance.

[0072] Specifically, the preset alarm threshold is 85 points, and its setting logic is based on the maximum allowable leakage rate of the seepage prevention structure design. In other embodiments, implementers can adjust it according to industry standards; real-time monitoring of integrity assessment scores. Changes in the integrity assessment score are addressed by setting a continuous triggering mechanism to eliminate the interference of transient noise on the assessment results. If the temperature drops below the alarm threshold three times consecutively, it is determined that there is a real risk of leakage in the waterproofing structure.

[0073] Furthermore, the spatial coordinates of each monitoring point and the corresponding imaginary admittance gradient characteristics are utilized. A bilinear interpolation algorithm is used to generate a heat map of the leakage location in the area to be monitored; in the heat map of the leakage location, the imaginary admittance gradient characteristics are... The area with the highest value is marked as the core risk area, and the precise coordinates are output in conjunction with the geographic information system.

[0074] This invention solves the problem of poor identification accuracy of traditional methods under humidity fluctuation conditions by constructing an evaluation system that includes complex admittance feature extraction, spatial gradient enhancement, and weighted scoring, thereby improving the robustness of leakage identification.

[0075] This invention also discloses a resistivity imaging-based system for assessing the integrity of seepage-proof structures, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the resistivity imaging-based method for assessing the integrity of seepage-proof structures according to this invention.

[0076] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A method for assessing the integrity of seepage-proof structures based on resistivity imaging, characterized in that, include: A monitoring electrode array is arranged in the overburden layer above the seepage prevention structure to continuously collect the response voltage sequence and response current sequence of each monitoring point. The complex admittance data of each monitoring point is obtained by fast Fourier transform, and the phase loss angle characteristics of each monitoring point are extracted based on the complex admittance data. The local response jump is captured based on the second-order partial derivative of the phase loss angle feature with respect to the spatial coordinates, and the square of the difference between the phase loss angle feature and the global average phase loss angle of the feature field is calculated to characterize the deviation. The local response jump and the deviation are weighted and coupled through the structural anisotropy correction factor to construct the imaginary admittance gradient feature of each monitoring point. Based on the difference between the imaginary admittance gradient feature and the baseline gradient feature value, the leakage probability weight of each monitoring point is obtained. The leakage probability weights of all monitoring points are obtained and arranged in descending order of value. Candidate leakage seed points are selected based on the anomaly screening quantile coefficient. Leakage clusters are identified based on the candidate leakage seed points, and the area of ​​the continuous leakage area corresponding to each monitoring point is obtained by combining the grid cell area. Based on the leakage probability weight, the area of ​​the continuous leakage zone, and the total number of monitoring points, an integrity assessment score for the seepage prevention structure is obtained. The formula for calculating the integrity assessment score of the seepage prevention structure is as follows: In the formula, Scoring for integrity assessment; This represents the total number of monitoring points; For the first Leakage probability weights for each monitoring point; This is a risk sensitivity index, with a value ranging from 1.5 to 3.0; This is the area penalty weight, and its value ranges from 5.0 to 15.0; For the first The area of ​​the continuous leakage zone to which each monitoring point belongs; The total area of ​​the monitored region; Based on the integrity assessment score, leakage warning and location are performed on the seepage prevention structure.

2. The method for assessing the integrity of seepage-proof structures based on resistivity imaging according to claim 1, characterized in that, The complex admittance data of each monitoring point includes a real admittance component and an imaginary admittance component. The real admittance component is composed of the real components of the current and voltage at each monitoring point, and the imaginary admittance component is composed of the imaginary components of the current and voltage at each monitoring point. The real components of the current, the imaginary components of the current, the real components of the voltage, and the imaginary components of the voltage at each monitoring point are obtained by performing a fast Fourier transform on the response voltage sequence and the response current sequence.

3. The method for assessing the integrity of seepage-proof structures based on resistivity imaging according to claim 2, characterized in that, The formula for calculating the phase loss angle characteristics of each monitoring point is as follows: ； In the formula, To monitor the phase loss angle characteristics of the monitoring points; It is the arctangent function; This is the real component of the current; This is the imaginary component of the voltage. This is the imaginary component of the current; This is the real component of the voltage.

4. The method for assessing the integrity of seepage-proof structures based on resistivity imaging according to claim 1, characterized in that, The formula for calculating the imaginary admittance gradient characteristics of each monitoring point is as follows: ； In the formula, To monitor the imaginary admittance gradient characteristics of the monitoring points; For three-dimensional space, the Laplace operator is used. To monitor the phase loss angle characteristics of the monitoring points; This is the structural anisotropy correction factor, with a value ranging from 1.0 to 1.5; The global average phase loss angle of the feature field.

5. The method for assessing the integrity of seepage-proof structures based on resistivity imaging according to claim 1, characterized in that, The formula for calculating the leakage probability weight of each monitoring point is as follows: ； In the formula, Leakage probability weights for monitoring points; It is a natural exponential function; This is the gain sensitivity coefficient, and its value ranges from 10.0 to 20.0; To monitor the imaginary admittance gradient characteristics of the monitoring points; The baseline gradient eigenvalues ​​are used.

6. The method for assessing the integrity of seepage-proof structures based on resistivity imaging according to claim 1, characterized in that, The process of obtaining the area of ​​the continuous leakage zone corresponding to each monitoring point includes: Select the leakage probability weight The monitoring points within the top 5% are selected and marked as candidate leakage seed points. An eight-neighbor connected component labeling algorithm is used to perform spatial growth with the candidate seepage seed points as the core, and candidate seepage seed points that are spatially adjacent are classified into the same seepage cluster. The area of ​​the continuous leakage region corresponding to each monitoring point is obtained by counting the number of candidate leakage seed points contained in each leakage cluster and combining it with the area of ​​the grid unit.

7. The method for assessing the integrity of seepage-proof structures based on resistivity imaging according to claim 1, characterized in that, The response voltage and response current sequences of each monitoring point were acquired simultaneously by injecting alternating current signals with a frequency range of 10Hz to 1kHz into the monitoring electrode array using a multi-channel complex impedance analyzer.

8. The method for assessing the integrity of seepage-proof structures based on resistivity imaging according to claim 1, characterized in that, Based on the integrity assessment score, leakage warning and location are performed on the seepage prevention structure, including: When the integrity assessment score is lower than the preset alarm threshold three times in a row, it is determined that there is a real risk of leakage in the seepage prevention structure and an early warning is triggered. Using the spatial coordinates of each monitoring point and the corresponding imaginary admittance gradient characteristics, a bilinear interpolation algorithm is used to generate a heat map of the leakage location in the area to be monitored.

9. A system for assessing the integrity of seepage-proof structures based on resistivity imaging, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the method for assessing the integrity of a seepage-proof structure based on resistivity imaging according to any one of claims 1-8.

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