Karst surface collapse susceptibility evaluation method and system

Abstract

The invention relates to the technical field of karst collapse evaluation, in particular to a karst surface collapse susceptibility evaluation method and system.The method comprises the steps that stratum information of a to-be-evaluated area is obtained to determine evaluation indexes, the subjective weight of each evaluation index is determined according to a comprehensive judgment matrix, and according to conflict and volatility between the subjective weights of all the evaluation indexes, the evaluation indexes of the to-be-evaluated area are evaluated. Objective weights of the evaluation indexes are determined, and subjective and objective combination weights are further obtained; setting karst collapse susceptibility grades and evaluation index value ranges corresponding to the grades, and extracting specific values corresponding to the evaluation indexes in the stratum information; converting each evaluation index into raster data, constructing a membership function of each evaluation index, and obtaining the distribution position, area and severity of the karst surface collapse susceptible region by using the same evaluation index and the specific magnitude corresponding to each evaluation index based on a comprehensive index method and a fuzzy mathematical method. And subjective weight and objective weight are combined to prevent subjective evaluation prejudice.

Description

Technical Field

[0001] The present invention relates to the technical field of karst collapse evaluation, and in particular to a method and system for evaluating the susceptibility of karst ground collapse. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] Karst ground collapse refers to the sudden subsidence and destruction of the loose rock and soil mass covering the solution caves in soluble rock areas under the action of natural or artificial factors, forming collapse pits. Karst ground collapse is controlled by various comprehensive factors such as lithological conditions, rock and soil layer structures, groundwater conditions, rainfall, earthquakes, underground resource exploitation, and underground engineering construction, so its formation mechanism is complex.

[0004] Through geophysical exploration such as high-density electrical method and ground penetrating radar method, the karst development in the target area can be basically determined, and through groundwater level and rainfall monitoring, etc., the dynamic law of the groundwater level and rainfall in the target area can be mastered. However, it is impossible to accurately predict the possibility of karst collapse by using these single factors or partial factors. In addition, it is difficult to accurately predict and forecast ground collapse in advance through deformation monitoring such as leveling measurement and settlement marks, and InSAR satellite remote sensing and other monitoring means.

[0005] Some existing technologies predict karst ground collapse by comprehensively analyzing water level and water quality to judge the gestation process of karst ground collapse. However, the prediction range is relatively small and the prediction time is short, which is not applicable to the prevention of large-area ground collapse and construction planning. There are also some existing technologies that issue early warnings of possible karst collapse by comparing monitoring information, that is, by comparing the monitoring data of different aspects in the target area with standard data. However, this method is one-sided, and the obtained prediction results are on the conservative side. Exceeding the standard of individual indicators does not necessarily form karst collapse.

[0006] There are also studies that have constructed an evaluation index system for the susceptibility of karst collapse based on the analytic hierarchy process and carried out the evaluation of the susceptibility of karst collapse. However, most of the constructed index systems are not comprehensive enough, lacking either natural geological factors or human factors. In addition, the subjectivity is relatively strong in index weighting, and it is impossible to objectively evaluate the various factors affecting karst collapse, lacking a certain degree of reliability and objectivity. As a result, the evaluation results of the susceptibility of karst collapse deviate from the actual situation, making it difficult to accurately predict the susceptible areas of karst collapse, and thus making accurate judgments and targeted measures. Summary of the Invention

[0007] To solve the technical problems existing in the above-mentioned background art, the present invention provides a method and system for evaluating the susceptibility of karst ground collapse, which more comprehensively covers the main factors and indicators affecting the susceptibility of karst ground collapse, and based on multi-source data sources such as geological drilling, physical and mechanical experiments, high-density resistivity, microtremor spectral ratio, electromagnetic wave CT, and on-site photography, comprehensively analyzes and obtains the quantitative data of the established evaluation index system.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] The first aspect of the present invention provides a method for evaluating the susceptibility of karst ground collapse, including the following steps:

[0010] Obtain the stratigraphic information of the area to be evaluated;

[0011] Determine the evaluation indicators according to the stratigraphic information, determine the subjective weights of each evaluation indicator according to the comprehensive judgment matrix, and determine the objective weights of each evaluation indicator according to the conflict and volatility between the subjective weights of each evaluation indicator, and further obtain the combined subjective and objective weights;

[0012] Set the susceptibility levels of karst collapse and the corresponding value ranges of evaluation indicators for each level, and extract the specific values of each evaluation indicator in the stratigraphic information;

[0013] Convert each evaluation indicator into raster data, and at the same time construct the membership function of each evaluation indicator. Respectively based on the comprehensive index method and the fuzzy mathematics method, using the same evaluation indicators, the corresponding specific values of the evaluation indicators, and the combined subjective and objective weights, calculate the distribution location, area, and severity of the areas susceptible to karst ground collapse.

[0014] Furthermore, the stratigraphic information includes geological conditions, hydrogeological conditions, climatic conditions, human activities, karst density, borehole karst rate, aquifer richness of the stratum, bedrock surface depth, overlying soil layer structure, overlying soil layer thickness, overlying soil permeability, groundwater fluctuation range, water level fluctuation rate, groundwater flow velocity, rainfall, distance from fracture structure, groundwater exploitation intensity, goaf degree of mineral resources, and land use type.

[0015] Furthermore, determining the evaluation indicators according to the stratigraphic information is specifically as follows: Based on the analytic hierarchy process, N experts compare the importance between each evaluation indicator to obtain a comparative judgment matrix C for constructing the evaluation indicators based on the comparison criteria, as shown in the following formula:

[0016] C R =C I / R I and C I =(λmax - n) / (n - 1);

[0017] Wherein, C Iis the consistency test index, R I is the average random consistency index, λmax is the largest eigenvalue of the comparison judgment matrix C, and n is the order of the matrix C. C R When <0.1, it is considered that the consistency of the judgment matrix C can be accepted, otherwise adjustments or deletions are made.

[0018] Furthermore, based on the comprehensive judgment matrix, the subjective weights of each evaluation index are determined. Specifically: for the comparison judgment matrix C that passes the consistency test, a comprehensive judgment matrix S of the evaluation index is constructed, and the largest eigenvalue λmax and eigenvector W of the comprehensive judgment matrix S are solved. The eigenvector W is the subjective weight v of each evaluation index in the evaluation index system j , as shown in the following formula:

[0019]

[0020] where S ij is the comprehensive judgment matrix between the i-th and j-th evaluation indexes, C ij is the comparison judgment matrix between the i-th and j-th evaluation indexes, λmax is the largest eigenvalue, W is the eigenvector, and k is the order of the comparison judgment matrix.

[0021] Furthermore, based on the conflict and volatility between the subjective weights of each evaluation index, the objective weights of each evaluation index are determined, as shown in the following formula:

[0022]

[0023] where r ij is the correlation coefficient between evaluation indexes, and S' j is the conflict between indexes.

[0024] Furthermore, the subjective and objective combined weights are obtained, as shown in the following formula:

[0025]

[0026] where the obtained subjective weight v j is substituted into the above formula to obtain the subjective and objective combined weights of each evaluation index.

[0027] Furthermore, the specific quantitative values corresponding to each evaluation index in the formation information are extracted. Specifically:

[0028] For qualitative indexes, by establishing a numerical mapping relationship corresponding to the attribute data, the conversion from qualitative to quantitative data is completed;

[0029] For quantitative indexes, quantization is achieved based on the index meaning and multi-source data obtained from the formation information.

[0030] Further, based on the comprehensive index method, the distribution location, area, and severity of karst ground collapse-prone areas are obtained. Specifically: convert each evaluation index into raster data, and according to the set karst collapse susceptibility levels, convert it into a zoning map corresponding to each evaluation index. Using the obtained subjective and objective combined weights and the specific values of each evaluation index, conduct an evaluation based on a geographic information platform (such as the ArcGIS platform) to determine the distribution location, area, and severity of karst ground collapse-prone areas.

[0031] Further, based on the fuzzy mathematics method, the distribution location, area, and severity of karst ground collapse-prone areas are obtained. Specifically: use the membership function of each evaluation index, combine the obtained formation information to solve the membership matrix, substitute the obtained subjective and objective combined weights, conduct the evaluation calculation of karst ground collapse susceptibility, and by combining the karst ground collapse susceptibility evaluation zoning map, divide the area to be evaluated into the corresponding levels to determine the distribution location, area, and severity of karst ground collapse-prone areas.

[0032] The second aspect of the present invention provides a karst ground collapse susceptibility evaluation system, including:

[0033] A data acquisition unit, configured to: obtain the formation information of the area to be evaluated;

[0034] A weight determination unit, configured to: determine the evaluation indexes according to the formation information, determine the subjective weights of each evaluation index according to the comprehensive judgment matrix, and determine the objective weights of each evaluation index according to the conflict and volatility between the subjective weights of each evaluation index, and further obtain the subjective and objective combined weights;

[0035] A data quantification unit, configured to: set the karst collapse susceptibility levels and the value ranges of the evaluation indexes corresponding to each level, and extract the specific values of each evaluation index corresponding in the formation information;

[0036] An evaluation output unit, configured to: convert each evaluation index into raster data, and at the same time construct the membership function of each evaluation index. Respectively based on the comprehensive index method and the fuzzy mathematics method, using the same evaluation indexes and the specific values of each evaluation index corresponding thereto, obtain the distribution location, area, and severity of karst ground collapse-prone areas.

[0037] Compared with the prior art, the above one or more technical solutions have the following beneficial effects:

[0038] 1. During the evaluation period, the method of combining subjective weights and objective weights is adopted, which not only reduces the situation of over-reliance on expert experience in the traditional method, prevents subjective evaluation biases, but also can reflect the objective influence degree of the evaluation indexes, and can effectively improve the objectivity and accuracy of the evaluation index weights.

[0039] 2. The membership functions of various evaluation indicators are used to improve the fuzzy mathematics method to obtain the evaluation zoning map. The comprehensive index method is also used to convert each evaluation indicator into raster data to obtain the evaluation zoning map. The susceptibility evaluation of karst ground collapse is calculated using different methods. By comprehensively comparing the calculation results obtained by the two methods, the characteristics such as the location distribution and area of the susceptibility of karst ground collapse are comprehensively analyzed. It can solve the situation of errors and biases in the evaluation results of a single model, making the evaluation results of the susceptibility of karst ground collapse more accurate and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The illustrative embodiments and descriptions thereof of the invention are used to explain the invention and do not constitute an improper limitation of the invention.

[0041] Figure 1 It is a schematic diagram of the susceptibility evaluation process of karst ground collapse provided by one or more embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0044] The following embodiments start from the karst collapse mechanism and comprehensively consider natural factors such as lithological conditions, rock and soil layer structures, groundwater conditions, rainfall, earthquakes, and human factors such as underground resource exploitation and underground engineering construction to construct an evaluation index system for the susceptibility of karst ground collapse based on multi-source data. In terms of index weighting, a method combining subjective weight and objective weight is adopted to reduce subjective evaluation bias and improve the objectivity and accuracy of the evaluation. Finally, a method for evaluating and zoning the susceptibility of karst ground collapse based on multi-source data is proposed in combination with the ArcGIS analysis platform, providing a method reference for engineering construction and disaster prevention and control in karst areas.

[0045] Example 1:

[0046] The karst ground collapse susceptibility evaluation method given in this embodiment comprehensively considers natural factors such as lithological conditions, rock and soil layer structures, groundwater conditions, rainfall, and earthquakes, as well as human factors such as underground resource exploitation and underground engineering construction. It uses a method of combining subjective and objective weighting to construct a karst collapse susceptibility evaluation index system and corresponding evaluation method. It can comprehensively consider various factors affecting karst collapse, such as natural geology, climate dynamics, human activities, and collapse history, and apply the method of combining subjective and objective weighting to solve the drawback that the weights of evaluation indicators overly rely on experts' subjective experience, making the evaluation and prediction results more scientific and accurate. The process of constructing the evaluation index system is as Figure 1 shown.

[0047] Taking the regional karst collapse susceptibility as the target layer, geological conditions, hydrological conditions, climate conditions, and human activities as the criterion layers, and karst density, borehole karst rate, formation water richness, bedrock surface depth, overlying soil layer structure, overlying soil layer thickness, overlying soil permeability, groundwater fluctuation range, water level volatility, groundwater flow velocity, rainfall, distance from fracture structure, groundwater exploitation intensity, goaf degree of mineral resources, and land use type as the influence layers to construct a regional karst collapse susceptibility evaluation index system.

[0048] Determine the subjective weights of the evaluation indicators. Based on the analytic hierarchy process, invite N experts in the field to evaluate each evaluation indicator in the constructed index system, compare the importance of each indicator one by one, and obtain the comparative judgment matrix C for constructing the evaluation indicators based on the defined comparison criteria. Use the formula C R =C I / R I and C I =(λmax - n) / (n - 1) to conduct a consistency test on multiple groups of comparative judgment matrices constructed by N experts. Among them, C I is the consistency test index, R I is the average random consistency index, which can be obtained by looking up the table, λmax is the maximum eigenvalue of the comparative judgment matrix C, and n is the order of the matrix C. When C R <0.1, it is considered that the consistency of the judgment matrix C is acceptable, otherwise adjust or delete it.

[0049] Based on the above comparative judgment matrix C that passes the consistency test, use the geometric mean method (root method) to construct the comprehensive judgment matrix S of the evaluation indicators. The calculation formula is Then use the calculation formulas and to solve the maximum eigenvalue λmax and eigenvector W of the comprehensive judgment matrix S.

[0050] Among them, the eigenvector W is the subjective weight v j, where \(i\) and \(j\) are the \(i\)-th and \(j\)-th evaluation indicators respectively, and \(k\) is the order of the comparison judgment matrix; the maximum eigenvalue represents the maximum scaling factor when the matrix acts on the eigenvector, and its meaning has no special meaning for the index weights, but only plays an auxiliary role in the process of solving the eigenvector.

[0051] Determine the objective weights of the evaluation indicators. By calculating and analyzing the conflict and volatility of the subjective weights among the evaluation indicators, the objectivity of the index weights is considered. Here, taking the improved structural CRITIC method as an example, the formula is used to calculate and analyze the conflict and volatility of each evaluation indicator, where \(r\) ij is the correlation coefficient between evaluation indicators, and \(S'\) j is the conflict index between indicators.

[0052] Determine the subjective and objective combined weights of the evaluation indicators. Substitute the obtained subjective weight \(v\) j into the formula to solve for the subjective and objective combined weights of each evaluation indicator. Through the subjective and objective combined weights, not only the expert experience is retained, but also the evaluation process is not overly dependent on expert experience and has subjective biases.

[0053] In this embodiment, the subjective and objective combined weighting method can also use methods such as game theory, minimum relative information entropy, and multiplication synthesis method for subjective and objective combined weighting.

[0054] Game theory combined weighting is to play against two or more weighting methods to seek a compromise point between different weights, minimizing the deviation between the comprehensive weight and the basic weight. When using linear combination weights, adding the idea of game theory can overcome the drawbacks of traditional equal distribution of weights.

[0055] Minimization of relative information entropy is a commonly used method in dealing with and optimizing problems. The purpose is to find an optimal solution or decision to minimize the information entropy, which can reduce uncertainty to a certain extent.

[0056] The multiplication synthesis method is a simple and efficient combined weighting method, which is obtained by multiplying and normalizing the weights of the criterion layer and the index layer. The weights of the criterion layer are calculated by the analytic hierarchy process, and the weights of the index layer are calculated by the improved CRITIC method.

[0057] Evaluation results and corresponding index grading. The regional karst collapse susceptibility level is divided into four levels: high susceptibility (Level I), medium susceptibility (Level II), low susceptibility (Level III), and non-susceptibility (Level IV). At the same time, combined with standards in fields such as karst ground collapse, engineering geology, and geotechnical mechanics and relevant technical specifications, each index in the evaluation index system is graded, and the value ranges corresponding to different gradings of the evaluation indicators are clarified.

[0058] Quantitative processing of multi-source data in the study area. The study area is the area to be evaluated. By sorting out and analyzing multi-source data such as geological boreholes, physical mechanics, high-density resistivity, microtremor spectral ratio, and electromagnetic wave CT in the study area, and according to the definition of evaluation indicators, the quantitative processing of the attributes corresponding to each evaluation indicator in the evaluation index system is completed, and the specific quantitative values of each evaluation indicator are obtained. For qualitative indicators, the conversion from qualitative to quantitative data is completed by establishing a numerical mapping relationship corresponding to the attribute data; for quantitative indicators, the quantification is directly based on the indicator meaning and multi-source data.

[0059] Carry out the susceptibility assessment of karst collapse based on the ArcGIS comprehensive index method. Use the ArcGIS visualization function to convert each evaluation indicator into raster data, and then obtain the zoning map of each evaluation indicator according to the grading results of the raster data. Use the GIS weighted sum tool to overlay and calculate all the raster data of the evaluation indicators to obtain the susceptibility assessment zoning map of karst ground collapse in the study area based on the comprehensive index method.

[0060] Carry out the susceptibility assessment of karst collapse based on the improved fuzzy mathematics method. Construct the membership function of each evaluation indicator, and solve the membership matrix in combination with the multi-source data in the study area. Substitute the subjective and objective combined weights obtained in step four to carry out the susceptibility assessment calculation of karst ground collapse in the study area based on the improved fuzzy mathematics method, and obtain the susceptibility assessment zoning map of karst ground collapse in the study area based on the improved fuzzy mathematics method.

[0061] According to the susceptibility assessment zoning map of karst ground collapse obtained by the ArcGIS comprehensive index method and the susceptibility assessment results of karst ground collapse obtained by the improved fuzzy mathematics method, carry out the susceptibility analysis of karst ground collapse, clarify the distribution location, main area, severity, etc. of the karst ground collapse susceptible areas in the study area, and give corresponding construction plans and prevention and control suggestions. By retaining the areas with the same evaluation results from the two evaluation perspectives, focus on the comparative analysis of the areas with different evaluation results, and combine the actual natural geology, karst collapse characteristics and other basic data in the study area to judge the susceptibility level and finally determine the evaluation results.

[0062] In the aspect of calculating the weights of the susceptibility assessment indicators of karst ground collapse in the above process, a method combining subjective weights and objective weights is adopted, which not only reduces the subjective evaluation bias of expert experience, but also can reflect the objective influence degree of the evaluation indicators, and can effectively improve the objectivity and accuracy of the evaluation indicator weights.

[0063] In addition, an improved fuzzy mathematics method and an ArcGIS comprehensive index method are used to propose a comprehensive evaluation model for the susceptibility of karst ground collapse. Based on the same evaluation index multi-source data and the results of subjective and objective combined weighting, the susceptibility evaluation calculation of karst ground collapse in the study area is carried out from different perspectives. By comprehensively comparing the calculation results obtained by the two methods, a comprehensive analysis of the characteristics such as the location distribution and area of the susceptibility of karst ground collapse in the study area is carried out. It can solve the situation of errors and biases in the evaluation results of a single model, making the evaluation results of the susceptibility of karst ground collapse in the study area more accurate and reasonable.

[0064] The process of carrying out the susceptibility evaluation of karst ground collapse by using the evaluation index system obtained in the above process includes the following steps:

[0065] Obtain the stratum information of the area to be evaluated (study area), and the stratum information can include geological conditions, hydrological conditions, climate conditions, human activities, karst density, borehole karst rate, stratum water-richness, bedrock surface depth, overlying soil layer structure, overlying soil layer thickness, overlying soil permeability, groundwater fluctuation range, water level volatility, groundwater flow velocity, rainfall, distance from fault structure, groundwater exploitation intensity, goaf degree of mineral resources, and land use type;

[0066] Determine the evaluation indexes according to the stratum information, determine the subjective weights of each evaluation index according to the comprehensive judgment matrix, and determine the objective weights of each evaluation index according to the conflict and volatility among the subjective weights of each evaluation index, and further obtain the subjective and objective combined weights;

[0067] Set the susceptibility levels of karst collapse and the value ranges of the evaluation indexes corresponding to each level, and extract the specific quantity values corresponding to each evaluation index in the stratum information;

[0068] Convert each evaluation index into raster data, and convert it into a zoning map corresponding to each evaluation index according to the set susceptibility levels of karst collapse. Using the obtained subjective and objective combined weights and the specific quantity values of each evaluation index, divide the area to be evaluated (study area) into the corresponding levels, and determine the distribution location, area, and severity of the areas susceptible to karst ground collapse in the study area.

[0069] Through the implementation of this method, the main factors and indexes affecting the susceptibility of karst ground collapse can be more comprehensively covered, and based on multi-source data sources such as geological drilling, physical and mechanical experiments, high-density resistivity, microtremor spectral ratio, electromagnetic wave CT, and on-site photography, the quantitative data of the established evaluation index system can be comprehensively analyzed.

[0070] Example 2:

[0071] The susceptibility evaluation system for karst ground collapse includes:

[0072] The data acquisition unit is configured to: obtain the formation information of the area to be evaluated;

[0073] The weight determination unit is configured to: determine the evaluation indexes according to the formation information, determine the subjective weights of the evaluation indexes according to the comprehensive judgment matrix, determine the objective weights of the evaluation indexes according to the conflict and volatility among the subjective weights of the evaluation indexes, and further obtain the combined subjective and objective weights;

[0074] The data quantization unit is configured to: set the karst collapse susceptibility levels and the corresponding value ranges of the evaluation indexes for each level, and extract the specific values of the evaluation indexes corresponding to the formation information;

[0075] The evaluation output unit is configured to: convert each evaluation index into raster data, and convert it into a zoning map corresponding to each evaluation index according to the set karst collapse susceptibility levels, and use the obtained combined subjective and objective weights and the specific values of each evaluation index to divide the area to be evaluated (study area) into the corresponding levels, and determine the distribution location, area and severity of the karst ground collapse susceptible areas in the study area.

[0076] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating the susceptibility of karst ground collapse, characterized in that: The following steps are involved: Obtaining stratigraphic information of the area to be evaluated; Determine the evaluation index according to the formation information, determine the subjective weight of each evaluation index according to the comprehensive judgment matrix, determine the objective weight of each evaluation index according to the conflict and volatility between the subjective weights of each evaluation index, and further obtain the subjective and objective combined weight; Set the karst collapse susceptibility level and the value range of the evaluation index corresponding to each level, and extract the specific value corresponding to each evaluation index in the formation information; Each evaluation index is converted into raster data, and the membership function of each evaluation index is constructed. Based on the comprehensive index method and fuzzy mathematics method, the same evaluation index and the specific value corresponding to each evaluation index, as well as the subjective and objective combined weights, are used to calculate the distribution location, area and severity of areas prone to karst ground collapse.

2. The method for evaluating susceptibility of karst ground collapse according to claim 1, characterized in that: Stratigraphic information includes geological conditions, hydrological conditions, climatic conditions, human activities, karst density, borehole karst rate, stratum water richness, bedrock surface burial depth, overlying soil structure, overlying soil thickness, overlying soil permeability, groundwater fluctuation amplitude, water level fluctuation rate, groundwater flow rate, rainfall, distance from fault structure, groundwater exploitation intensity, mineral resource depletion degree and land use type.

3. The method for evaluating susceptibility of karst ground collapse according to claim 1, characterized in that: The evaluation index is determined according to the formation information. Specifically, based on the hierarchical analysis method, N experts compare the importance of each evaluation index to obtain the comparison judgment matrix C of the structural evaluation index defined based on the comparison standard, as shown in the following formula: C R =C I / R I and C I =(λmax-n) / (n-1); Among them, C I is the consistency test index, R I is the average random consistency index, λmax is the maximum eigenvalue of the comparison judgment matrix C, n is the order of matrix C, C R When <0.1, the consistency of the judgment matrix C is considered acceptable, otherwise it is adjusted or deleted.

4. The method for evaluating susceptibility of karst ground collapse according to claim 1, characterized in that: The subjective weight of each evaluation index is determined according to the comprehensive judgment matrix. Specifically, the comprehensive judgment matrix S of the evaluation index is constructed through the comparison judgment matrix C of the consistency test, and the maximum eigenvalue λmax and eigenvector W of the comprehensive judgment matrix S are solved. The eigenvector W is the subjective weight v of each evaluation index in the evaluation index system. j , as shown below: Among them, S ij is the comprehensive judgment matrix between the i-th and j-th evaluation indicators, C ij is the comparison judgment matrix between the i-th and j-th evaluation indicators, λmax is the maximum eigenvalue, W is the eigenvector, and k is the order of the comparison judgment matrix.

5. The method for evaluating susceptibility of karst ground collapse according to claim 1, characterized in that: According to the conflict and volatility between the subjective weights of each evaluation index, the objective weight of each evaluation index is determined as shown in the following formula: Among them, r ij is the correlation coefficient between the i-th and j-th evaluation indicators, S' j The conflict between indicators.

6. The method for evaluating susceptibility of karst ground collapse according to claim 1, characterized in that: The subjective and objective combined weights are as follows: Among them, v j is the subjective weight of the jth evaluation index, r ij is the correlation coefficient between the i-th and j-th evaluation indicators.

7. The method for evaluating susceptibility of karst ground collapse according to claim 1, characterized in that: The specific values ​​corresponding to each evaluation index in the formation information are extracted, specifically: For qualitative indicators, the conversion from qualitative to quantitative data is completed by establishing the numerical mapping relationship corresponding to the attribute data; For quantitative indicators, quantification is achieved based on multi-source data formed by the meaning of indicators and the acquired stratigraphic information.

8. The method for evaluating susceptibility of karst ground collapse according to claim 1, characterized in that: The distribution location, area and severity of areas prone to karst ground collapse are obtained based on the comprehensive index method. Specifically, each evaluation index is converted into raster data, and then converted into a zoning map corresponding to each evaluation index according to the set karst collapse susceptibility level. The area to be evaluated is divided into corresponding levels using the obtained subjective and objective combined weights and the specific values ​​of each evaluation index. The evaluation is carried out based on the geographic information platform to determine the distribution location, area and severity of areas prone to karst ground collapse.

9. The method for evaluating susceptibility of karst ground collapse according to claim 8, characterized in that: The distribution location, area and severity of the areas prone to karst ground collapse are obtained based on the fuzzy mathematics method. Specifically, the membership function of each evaluation index is used to solve the membership matrix in combination with the acquired stratigraphic information, and the subjective and objective combined weights are substituted to perform the karst ground collapse susceptibility evaluation calculation. By combining the karst ground collapse susceptibility evaluation zoning map, the area to be evaluated is divided into corresponding grades to determine the distribution location, area and severity of the areas prone to karst ground collapse.

10. A karst ground collapse susceptibility assessment system, implemented based on the karst ground collapse susceptibility assessment method according to any one of claims 1 to 9, characterized in that: include: The data acquisition unit is configured to: obtain formation information of the area to be evaluated; The weight determination unit is configured to: determine the evaluation index according to the formation information, determine the subjective weight of each evaluation index according to the comprehensive judgment matrix, determine the objective weight of each evaluation index according to the conflict and volatility between the subjective weights of each evaluation index, and further obtain the subjective and objective combined weight; The data quantification unit is configured to: set the karst collapse susceptibility level and the value range of the evaluation index corresponding to each level, and extract the specific value corresponding to each evaluation index in the formation information; The evaluation output unit is configured to: convert each evaluation index into raster data, and construct a membership function of each evaluation index at the same time, based on the comprehensive index method and the fuzzy mathematics method, respectively, using the same evaluation index and the specific value corresponding to each evaluation index, to obtain the distribution location, area and severity of karst ground collapse-prone areas.

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