A method for determining the properties of underground caves

Abstract

The present invention relates to a method for determining the properties of underground karst caves, comprising: obtaining horizontal electric field and vertical magnetic field signals of the karst cave, inputting the horizontal electric field and vertical magnetic field signals into a forward model, and obtaining resistivity and polarizability results; the forward model is obtained by training an original forward model using a training set, the training set including time domain features; training the original forward model using the training set includes: inputting the time domain features into the forward model, obtaining predicted data, setting a priori constraints, converting the constraints into a multi-data volume inversion objective function, performing Taylor expansion on the multi-data volume inversion objective function and omitting higher-order terms, obtaining first-order and second-order partial derivatives of the Taylor-expanded multi-data volume inversion objective function to obtain a data update model, and iteratively correcting the original forward model using the time domain features and predicted data according to the data update model. The present invention achieves precise detection of deep karst caves and precise differentiation of karst cave properties.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering geology, and in particular to a method for determining the properties of underground karst caves. Background Art

[0002] Karst caves are widespread, with complex and diverse landforms and fragile ecosystems. During construction, the development of caves can cause accidents such as foundation slippage, surface subsidence, and tunnel flooding, damaging buildings and hindering the operation and construction of roads and tunnels. Caves form as a result of long-term groundwater dissolution in limestone areas. Their distribution is complex, uncertain, and hidden. Therefore, meticulous exploration of underground caves and accurate assessment of their properties and spatial distribution are crucial measures to ensure safety in areas with abundant caves.

[0003] Based on their water content, caves can be divided into dry caves and water-filled caves. Water-filled caves are karst caves with perennial groundwater flow. Dry caves are fossilized caves separated from the free water surface. They develop in higher terrain and have a long history of development, often adorned with a variety of colorful stalactites. Currently, most caves are both dry and water-filled. Limestone, the predominant surrounding rock type in cave areas, has a relatively high resistivity. If a cave is filled with water, its resistivity is very low. However, if it is not filled with water, the resistivity of a dry cave is very high, far exceeding that of the surrounding limestone. Therefore, precise cave monitoring requires strong discrimination between high- and low-resistivity dry and water-filled caves.

[0004] Electrical methods commonly used for shallow karst exploration include electrical resistivity tomography (GERT), which uses resistivity differences in the subsurface medium for imaging, ground-penetrating radar (GPR), and loop-source transient electromagnetic (LSTEM) methods. However, both GPR and high-density electrical methods have limited detection depths, typically less than tens of meters. They are also often effective only for single low-resistance or high-resistance targets and cannot precisely resolve both. LST, based on the principle of electromagnetic induction, has much poorer resolution for high-resistance targets and cannot accurately locate high-resistance dry caves. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for determining the properties of underground caves. By forward modeling the multi-component electrical source of dry caves and water caves, it is found that water caves and dry caves with induced polarization will cause the sign reversal phenomenon of the horizontal electric field response, while dry caves will not cause the distortion phenomenon of the vertical magnetic field response. The difference in the sign reversal phenomenon of the horizontal electric field and the vertical magnetic field can be used as a basis for judging the properties of underground caves. When only the sign reversal of the horizontal electric field occurs, the underground cave is an empty cave. When the sign reversal phenomenon occurs simultaneously in the horizontal electric field and the vertical magnetic field, the underground cave is a water cave. Based on the verification of the measured data of Tonghua Cave obtained by the above-mentioned theoretical simulation, a new technical solution is provided for studying the properties of underground caves, especially for the detection of caves with larger burial depths and the resolution of cave properties.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for determining the properties of an underground cave, comprising:

[0008] Acquire horizontal electric field and vertical magnetic field signals of the cave, input the horizontal electric field and vertical magnetic field signals into a forward model, and obtain resistivity and polarizability results; the forward model is obtained by training the original forward model using a training set, and the training set includes: time domain features;

[0009] Training the original forward model using the training set includes: inputting the time domain features into the forward model, obtaining predicted data, setting a priori constraints, converting the constraints into a multi-volume inversion objective function, performing Taylor expansion on the multi-volume inversion objective function and ignoring higher-order terms, calculating first-order and second-order partial derivatives of the Taylor-expanded multi-volume inversion objective function to obtain a data update model, and iteratively correcting the original forward model using the time domain features and the predicted data according to the data update model.

[0010] Optionally, acquiring the horizontal electric field and vertical magnetic field signals of the cave includes:

[0011] Obtaining the depth of the cave, and determining the source and offset of the grounding wire based on the depth of the cave, for simultaneously deploying multiple observation devices around the cave;

[0012] Based on the multi-point observation device, forward simulation is carried out to calculate the abnormal responses caused by caves of different sizes, burial depths and shapes, establish a curve set, and infer the acquisition time range from the time when the abnormal response is caused. The horizontal electric field and vertical magnetic field signals in different time periods are obtained by using a timed sampling method.

[0013] Optionally, obtaining the time domain feature includes:

[0014] Remove background noise from the original horizontal electric field and vertical magnetic field signals and improve signal strength; the background noise includes: environmental noise and interference generated by the device itself;

[0015] The signal after the intensity is increased is normalized and segmented to obtain signal features in different time periods, and the time domain features are extracted using gradient changes.

[0016] Optionally, extracting the time domain features includes:

[0017] The time domain features are compared and analyzed with the curve set. If a negative value appears on the data curve, it is analyzed whether it is an irregular change caused by noise or a regular change caused by underground caves. If the time domain features are regular changes, they are used to identify potential cave properties.

[0018] Optionally, setting the priori constraint conditions includes:

[0019] P a (m)=φ(m)+as(m)

[0020] Among them, P a (m) is the overall objective function, a is the regularization factor, φ(m) is the sum of squares of the differences between the observed data and the predicted data, and s(m) is the stabilizer.

[0021] Optionally, the expression of the multi-volume inversion objective function is:

[0022] P α (m)=||W1[d obs1 -F1(m)]|| 2 +||W2[d obs2 -F2(m)]| 2 +…+||W n [d obsn -F n (m)]|| 2 +α||mm ref || 2

[0023] Among them, d obsn is the measured data of different field quantities or observation area responses, F n (m) is the response function, W n The weight coefficient matrix of the measured data, m ref is the prior model, α is the regularization factor, W1 and W2 are the weight coefficient matrices of the first and second iterations of the measured data, and m is the model corresponding to the measured data.

[0024] Optionally, obtaining the data update model includes:

[0025]

[0026] Among them, J is the sensitivity matrix, m k is the iterative model, n is the number of iterations, W n is the weight coefficient matrix of the measured data at the nth iteration, α is the regularization factor, and T is the transposition operation.

[0027] Optionally, the method further includes: presenting the resistivity and polarizability results in the form of a chart or report to demonstrate the determined cave properties and geological characteristics.

[0028] The beneficial effects of the present invention are:

[0029] The present invention updates the model according to the data, utilizes the time domain characteristics and the predicted data to iteratively correct the original forward model, and uses the model that meets the accuracy requirements to simulate the actual geological conditions, thereby realizing the fine detection of deep caves and the fine distinction of the cave properties, and also realizing the effectiveness of the cave property determination and the efficiency of the specific implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 This is a flow chart of a method for determining the properties of an underground cave according to an embodiment of the present invention;

[0032] Figure 2 Schematic diagram of resistivity and polarizability results of an embodiment of the present invention;

[0033] Figure 3 Response curves caused by dry caves according to an embodiment of the present invention; (a) is the horizontal electric field caused by the dry cave, and (b) is the vertical magnetic field caused by the dry cave;

[0034] Figure 4 Response curves caused by water caves according to an embodiment of the present invention; (a) is the horizontal electric field caused by water caves, and (b) is the vertical magnetic field caused by water caves;

[0035] Figure 5 A survey line layout diagram according to an embodiment of the present invention;

[0036] Figure 6 Response curves of typical measuring points of measuring line L18 according to an embodiment of the present invention; (a) is the response curve at the measuring point 10 m away, and (b) is the response curve at the measuring point 200 m away;

[0037] Figure 7 Response curves of typical measuring points of measuring line L12 according to an embodiment of the present invention; (a) is the response curve at the measuring point 20 m away, (b) is the response curve at the measuring point 70 m away, and (c) is the response curve at the measuring point 250 m away.

[0038] Figure 8 1 is a multi-channel response curve on the L18 measurement line of an embodiment of the present invention; (a) is the horizontal electric field, and (b) is the vertical magnetic field;

[0039] Figure 9 Schematic diagram of L18 resistivity inversion results according to an embodiment of the present invention;

[0040] Figure 10 1 is a multi-channel response curve on the L12 measurement line of an embodiment of the present invention; (a) is the horizontal electric field, and (b) is the vertical magnetic field;

[0041] Figure 11 Schematic diagram of L12 resistivity inversion results according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] like Figure 1 As shown, this embodiment discloses a method for determining the properties of underground caves, including: obtaining horizontal electric field and vertical magnetic field signals of the cave, inputting the horizontal electric field and vertical magnetic field signals into a forward model, and obtaining resistivity and polarizability results; the forward model is obtained by training the original forward model using a training set, and the training set includes: time domain features; using the training set to train the original forward model includes: inputting the time domain features into the forward model, obtaining predicted data, setting prior constraints, converting the constraints into a multi-data volume inversion objective function, performing Taylor expansion on the multi-data volume inversion objective function, and ignoring high-order terms, and obtaining the first-order and second-order partial derivatives of the Taylor-expanded multi-data volume inversion objective function to obtain a data update model, and according to the data update model, iteratively correcting the original forward model using the time domain features and the predicted data.

[0045] Furthermore, obtaining the horizontal electric field and vertical magnetic field signals of the cave includes: obtaining the depth of the cave, and determining the grounding wire source and offset distance based on the depth of the cave, so as to simultaneously arrange multiple-point observation devices around the cave; conducting forward simulation based on the multiple-point observation devices, calculating the abnormal responses caused by caves of different sizes, burial depths, and shapes, establishing a curve set, and inferring the acquisition time range from the time when the abnormal response is caused, and using a timed sampling method to obtain the horizontal electric field and vertical magnetic field signals in different time periods.

[0046] Specifically, the horizontal electric field and vertical magnetic field signals of the cave are collected:

[0047] In this step, accurate acquisition of electromagnetic signals from underground caves is the basis for determining their properties. The following is a detailed description of the acquisition process:

[0048] 1. Equipment layout:

[0049] Ground Wire Source: Analyze the approximate depth of the detection target based on the results of the preliminary geological survey, and select an appropriate ground wire source and offset distance to ensure wide coverage within the target area.

[0050] Transient electromagnetic multi-component observation device: Multiple observation devices are arranged simultaneously around the cave to capture the equatorial electric and magnetic field signals. The horizontal electric field must be strictly parallel to the transmitting source, and the receiving electrodes must be arranged strictly according to the design. The magnetic field is collected using a magnetic probe, which must be completely perpendicular to the ground surface.

[0051] 2. Signal acquisition:

[0052] The forward modeling results in a curve set: the responses caused by caves of different sizes, depths, and shapes are calculated, and a curve set is established.

[0053] Time range selection: Based on the depth and geological environment of the target cave, forward modeling is performed to infer the acquisition time range from the time when the abnormal response occurs. Generally, signals with low fundamental frequencies are more suitable for deep exploration, while signals with high fundamental frequencies provide higher resolution.

[0054] Data acquisition strategy: A timed sampling approach is used to ensure that sufficient signal data is obtained in different time periods for comprehensive analysis.

[0055] 3. Signal processing:

[0056] Noise filtering: After signal acquisition, digital filtering technology is used to remove background noise, including environmental noise and interference generated by the device itself.

[0057] Signal enhancement: Through signal enhancement algorithms, the strength and clarity of useful signals are improved for subsequent analysis.

[0058] 4. Data recording and storage:

[0059] Real-time monitoring: During the signal acquisition process, the quality and integrity of the data are monitored in real time to ensure that important data is not lost.

[0060] Data storage: The collected electric and magnetic field signals are saved in a data storage system in an appropriate format and structure for subsequent analysis and processing.

[0061] Furthermore, obtaining time domain features includes: removing background noise from the original horizontal electric field and vertical magnetic field signals and improving signal strength; background noise includes: environmental noise and interference generated by the device itself; normalizing and segmenting the signals after intensity improvement to obtain signal features of different time periods, and extracting time domain features using gradient changes.

[0062] Furthermore, after extracting the time domain features, the method includes: comparing and analyzing the time domain features with the curve set. If a negative value appears on the data curve, it is analyzed whether it is an irregular change caused by noise or a regular change caused by underground caves. If the time domain features show regular changes, it is used to identify the potential nature of the cave.

[0063] Specifically, processing the horizontal electric field and vertical magnetic field signals includes:

[0064] After completing signal acquisition, the next step is to conduct in-depth analysis of the horizontal electric field and vertical magnetic field signals to determine the nature of the underground cave. The following is a detailed description of this step:

[0065] 1. Data preprocessing:

[0066] Signal normalization: Normalize the collected electric and magnetic field signals to eliminate the impact of signal strength at different locations and ensure the comparability of analysis results.

[0067] Data segmentation: Continuous signal data is segmented to facilitate comparison and analysis of signal characteristics in different time periods.

[0068] 2. Feature extraction:

[0069] Time-domain feature extraction: When there are no geological bodies such as caves, the observed response signal is monotonic, so the gradient of the observed data is positive. However, when affected by target bodies such as caves, the calculated response gradient becomes negative. Gradient changes are used to extract the signal's time-domain features, such as the maximum, minimum, mean, and variance, with particular attention paid to sign reversal features. These features are then compared and analyzed with the curve set from the previous forward modeling. When negative values ​​appear in the data curve, the error bars of the data are analyzed to determine the nature of the negative value, whether it is an irregular change caused by noise or a regular change caused by underground caves. When the data shows regular changes, it can be used to identify potential cave properties.

[0070] 3. Determination of cave properties:

[0071] Data joint inversion: Inversion obtains the resistivity and polarizability characteristics of underground geological bodies, and obtains information such as the burial depth and distribution space of caves.

[0072] Furthermore, the prior constraints include:

[0073] P a (m)=φ(m)+as(m)

[0074] Among them, P a (m) is the overall objective function, a is the regularization factor, φ(m) is the sum of squares of the differences between the observed data and the predicted data, and s(m) is the stabilizer.

[0075] Furthermore, the expression of the multi-volume inversion objective function is:

[0076] P α (m)=||W1[d obs1 -F1(m)]|| 2 +||W2[d obs2 -F2(m)]|| 2 +…+||W n [d obsn -F n (m)]|| 2 +α||mm ref || 2

[0077] Among them, d obsn is the measured data of different field quantities or observation area responses, F n (m) is the response function, W n The weight coefficient matrix of the measured data, m ref is the prior model, α is the regularization factor, W1 and W2 are the weight coefficient matrices of the first and second iterations of the measured data, and m is the model corresponding to the measured data.

[0078] Furthermore, in the process of obtaining the first-order and second-order partial derivatives of the Taylor-expanded multi-data volume inversion objective function, the derivative of Δm in the Taylor-expanded multi-data volume inversion objective function is obtained:

[0079]

[0080] Among them, Δm is the residual error of model parameters, Pα(m k ) is the objective function.

[0081] Furthermore, obtaining the data update model includes:

[0082]

[0083] Among them, J is the sensitivity matrix, m k is the iterative model, n is the number of iterations, W n is the weight coefficient matrix of the measured data at the nth iteration, α is the regularization factor, and T is the transposition operation.

[0084] Furthermore, the method also includes: presenting the resistivity and polarizability results in the form of a chart or report to demonstrate the determined cave properties and geological characteristics.

[0085] Specifically, for inversion problems, the non-uniqueness of solutions stems from the finiteness of observational data and the inherent errors in these data. Extending the inversion of a single data volume to the simultaneous inversion of multiple data volumes, and obtaining a geophysical model that satisfies these multiple data volumes, improves the reliability of the results. In principle, these data volumes can be of any type and can be used to interpret the same model.

[0086] A comprehensive study of the joint inversion of a pair of grounded conductor source transient electromagnetic multi-component data was conducted. To improve the stability and non-uniqueness issues, a regularized inversion method was introduced. By adding a priori constraints, the stability of the inversion process is enhanced and the non-uniqueness of the inversion results is reduced.

[0087] P a (m)=φ(m)+as(m)

[0088] Where, P a (m) is the overall objective function: a is the regularization factor; φ(m) is the sum of the squares of the differences between the observed data and the predicted data (data objective function); s(m) is the stabilizer (model constraint objective function).

[0089] The multi-volume inversion objective function can be expressed as:

[0090] P α (m)=||W1[d obs1 -F1(m)]|| 2 +||W2[d obs2 -F2(m)]|| 2 +…+||W n [d obsn -F n (m)]|| 2 +α||mm ref || 2

[0091] Where, d obsn is the measured data of different field quantities or observation area responses, F n (m) is the response function, W nThe weight coefficient matrix of the measured data, m ref is the prior model.

[0092] In order to linearize the nonlinear objective function, Taylor expansion is performed on the objective function and the high-order terms are omitted:

[0093]

[0094] Where m k is the k-th iteration value of the model.

[0095] Taking the derivative of Δm, we get the inversion iterative update formula:

[0096]

[0097] Calculate the first-order and second-order partial derivatives of the objective function and finally obtain the data update formula:

[0098]

[0099] Where J is the sensitivity matrix. Use data to update formula m k+1 Through iteration, the forward model m is continuously revised, and finally a model that meets the accuracy requirements (the sum of squares or residuals between the observed data and the predicted data can be used to judge the model accuracy) is used to simulate the actual geological conditions.

[0100] Joint inversion of multiple data volumes can improve the reliability of the results overall. The multiple data volumes provided contain complementary information. The resistivity and polarizability results obtained, such as Figure 2 shown.

[0101] Uncertainty analysis: Evaluate the uncertainty of the decision results and analyze possible sources of error. Improve the reliability of the decision through multiple tests and cross-validation.

[0102] Result visualization: The analysis results are presented in the form of charts or reports to clearly show the determined cave properties and their possible geological characteristics.

[0103] This example also carried out forward simulation of the transient electromagnetic horizontal electric field and vertical magnetic field responses of different caves. Through response characteristics and RMS analysis, it was found that the horizontal electric field is very sensitive to both low-resistance and high-polarization water caves and high-resistance dry caves, while the vertical magnetic field is only sensitive to low-resistance and high-polarization water caves. Figure 3 (a)-(b) show the low-resistance, high-polarization water-bearing caves and the high-resistance dry caves. Water-bearing caves can cause distortions in the vertical magnetic and horizontal electric field responses, even sign reversals, while dry caves only cause sign reversals in the horizontal electric field response. This distinction can be used to determine the water content of underground caves.

[0104] In high-resistance dry caves, the presence of a high-resistance layer causes a sign reversal in the horizontal electric field. This is due to charge accumulation at the low-resistance-high-resistance electrical interface, which results in a distortion and sign reversal of the electric field response. However, the vertical magnetic field exhibits no significant distortion or sign reversal. Both low-resistance, high-polarization water caves and high-resistance dry caves induce sign reversal in the horizontal electric field, while vertical magnetic field sign reversal occurs only in low-resistance, high-polarization water caves.

[0105] When a low-resistance polarized water cave exists, the time derivatives of the horizontal electric field and the vertical magnetic field both show a sign reversal phenomenon. Compared with the vertical magnetic field component, the sign reversal of the horizontal electric field occurs before 1e-3s ( Figure 4 a), while the sign reversal of the vertical magnetic field occurs around 1e-1s ( Figure 4 b), the low-resistance polarization layer has a greater impact on the horizontal electric field.

[0106] The study area is located on the northeastern margin of the North China Craton. The stratigraphic development within the region is relatively complete, encompassing the Archean, Proterozoic, Paleozoic, Mesozoic, and Cenozoic eras. The Archean rock groups include the Anshan Group, while the Proterozoic includes the Ji'an and Laoling Groups, as well as the Sinian System. The Paleozoic comprises the Cambrian, Ordovician, Carboniferous, and Permian systems. The Mesozoic comprises the Triassic, Jurassic, and Cretaceous systems. The Cenozoic encompasses the Neogene and Quaternary systems.

[0107] Tectonically, it belongs to the North China stratigraphic area, with carbonate rock strata growing from the Middle Proterozoic to the Lower Paleozoic. Among them, the Majiagou Formation of the Middle Ordovician and the Badaojiang Formation of the Sinian System are relatively thick and have high purity, forming a good material basis for the development of karst caves.

[0108] The Tonghua Caves are located within the Ordovician Majiagou Limestone Formation, a shallow marine carbonate rock. The limestone's thickness, purity, and rigidity facilitate the expansion of joints and fissures, resulting in excellent water permeability, allowing for the development of long and large caves. The formation of karst caves is significantly influenced by geological structure, with the structural features of the rock formations, such as folds, inclinations, or horizontality, significantly influencing cave development. The Yunxia Caves are located within the Hunjiang Fault Fold Belt, a tectonic zone dominated by the northeast-trending Hunjiang syncline in the central Paleozoic of Tonghua. The folds, flanked by northeast-trending faults, are considered extensional faults. Groundwater flows along these faults, developing karst formations and forming underground river channels. The Hunjiang Fault Fold Belt is further fragmented by other structures, such as the north-northeast-trending fault. Consequently, the intersections of these structures are subject to dissolution and collapse, forming karst pools. With the occurrence of neotectonic movement, the earth's crust rises and the groundwater level drops, which is conducive to the continued dissolution of groundwater and the mechanical erosion of groundwater flow. The karstification process, which is mainly horizontal dissolution, is transformed into vertical dissolution and collapse, and the karst pool further expands to form a cave hall.

[0109] Figure 5The distribution of the emission sources and measured lines is given. The length of the emission source represented by red is 600m, and the offset distances between the two measurement lines and the emission source are 380 and 500m respectively.

[0110] like Figure 6 As shown in (a)-(b), at the measuring point 10m away, neither the horizontal electric field nor the vertical magnetic field shows sign reversal. However, at the measuring point 200m away, the horizontal electric field shows sign reversal between 1ms and 10ms, but the vertical magnetic field does not.

[0111] like Figure 7 Figures (a)-(c) show the response curves for typical measurement points on line L12. Three scenarios emerge in the measured data for line 12: At the 70-meter measurement point, neither the horizontal electric field nor the vertical magnetic field exhibits sign reversal. At the 20-meter measurement point, the horizontal electric field exhibits sign reversal, but the vertical magnetic field does not. At the 250-meter measurement point, both the horizontal electric field and the vertical magnetic field exhibit sign reversal.

[0112] In order to better compare the response differences between different measurement points, Figure 8 (a)-(b) show the multi-track curves for the L18 survey line. In the horizontal electric field multi-track curve, sign reversal occurs after 2.5 ms at measurement points ranging from 40 m to 270 m. However, no sign reversal is observed in the vertical magnetic field multi-track curve.

[0113] The vertical magnetic field data without sign reversal phenomenon is inverted. The inversion results are as follows Figure 9 As shown, the shallow yellow and orange layers of low electrical resistivity correspond to the Quaternary and weathered sedimentary layers. The second layer, the thick, light blue, high-resistivity layer with high electrical properties, is inferred to be a reflection of the Ordovician Majiagou Formation limestone. The relatively localized dark blue high-resistivity anomalies are likely related to karst development and weak water content. The deep green layers, ranging from 10 to 170 meters, have relatively medium-to-high electrical resistivity and are inferred to be lower Ordovician and Cambrian strata. The dark blue high-resistivity areas in the image correspond to known dry caves.

[0114] exist Figure 10 In the multi-channel plots (a) and (b), the horizontal electric field exhibits two sign reversals, located within the range of 10-30m and 120-270m from the measurement point. The timing of these two sign reversals is also different: the sign reversal at 10-30m occurs after 2.5ms, while the sign reversal at 120-270m occurs at 1.25ms, an earlier occurrence. In contrast, the multi-channel plots of the vertical magnetic field exhibit only one sign reversal, occurring within the range of 120-270m, corresponding to the horizontal electric field. However, no sign reversal occurs within the range of 10-30m.

[0115] The inversion results are as follows Figure 11 As shown, the shallow layer is a low-resistance electrical layer, corresponding to the Quaternary and weathered sedimentary layers. The second layer, light blue and green, is thick and medium-to-high resistivity, inferred to be a reflection of the Ordovician Majiagou Formation limestone. The dark high-resistivity anomaly appears in the 10-30m range at an elevation of 550m, likely related to karst development and weak water content. Below 500m elevation, the deep relatively low-resistivity anomaly in the 110-270m range is inferred to be a water-bearing cave in the lower Ordovician strata. The low-resistivity areas in the figure correspond to known water-bearing caves.

[0116] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for determining the properties of underground caves, characterized in that: include: Acquiring horizontal electric field and vertical magnetic field signals of the cave, inputting the horizontal electric field and vertical magnetic field signals into a forward model to obtain resistivity and polarizability results; The forward model is obtained by training the original forward model using a training set, wherein the training set includes: time domain features; Training the original forward model using the training set includes: inputting the time domain features into the forward model, obtaining predicted data, setting a priori constraints, converting the constraints into a multi-volume inversion objective function, performing Taylor expansion on the multi-volume inversion objective function and omitting higher-order terms, calculating first-order and second-order partial derivatives of the Taylor-expanded multi-volume inversion objective function to obtain a data update model, and iteratively correcting the original forward model using the time domain features and the predicted data according to the data update model; The prior constraints include: P a (m)=φ(m)+as(m) Among them, P a (m) is the overall objective function, a is the regularization factor, φ(m) is the sum of squares of the differences between the observed data and the predicted data, and s(m) is the stabilizer; The expression of the multi-volume inversion objective function is: P α (m)=||W1[d obs1 -F1(m)]|| 2 +||W2[d obs2 -F2(m)]|| 2 +…+||W n [d obsn -F n (m)]|| 2 +α||m-m ref || 2 Among them, d obsn is the measured data of different field quantities or observation area responses, F n (m) is the response function, m ref is the prior model, W1 and W2 are the weight coefficient matrices of the first and second iterations of the measured data, and m is the model corresponding to the measured data; Acquiring the data update model includes: Among them, J is the sensitivity matrix, m k is the iterative model, n is the number of iterations, W n is the weight coefficient matrix of the measured data at the nth iteration, and T is the transpose operation.

2. The method for determining the properties of underground caves according to claim 1, wherein: Acquiring the horizontal electric field and vertical magnetic field signals of the cave includes: Obtaining the depth of the cave, and determining the source and offset of the grounding wire based on the depth of the cave, for simultaneously deploying multiple observation devices around the cave; Based on the multi-point observation device, forward simulation is carried out to calculate the abnormal responses caused by caves of different sizes, burial depths and shapes, establish a curve set, and infer the acquisition time range from the time when the abnormal response is caused. The horizontal electric field and vertical magnetic field signals in different time periods are obtained by using a timed sampling method.

3. The method for determining the properties of underground caves according to claim 1, wherein: Acquiring the time domain features includes: Remove background noise from the original horizontal electric field and vertical magnetic field signals and improve signal strength; the background noise includes: environmental noise and interference generated by the device itself; The signal after the intensity is increased is normalized and segmented to obtain signal features in different time periods, and the time domain features are extracted using gradient changes.

4. The method for determining the properties of underground caves according to claim 2, wherein: After extracting the time domain features, the following steps are included: The time domain features are compared and analyzed with the curve set. If a negative value appears on the data curve, it is analyzed whether it is an irregular change caused by noise or a regular change caused by underground caves. If the time domain features are regular changes, they are used to identify potential cave properties.

5. The method for determining the properties of underground caves according to claim 1, wherein: The method further includes: presenting the resistivity and polarizability results in the form of a chart or report to demonstrate the determined cave properties and geological characteristics.

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