Geological disaster susceptibility evaluation method coupled with InSAR technology

Abstract

The invention relates to a geological disaster susceptibility evaluation method coupled with an InSAR technology, which combines InSAR earth surface deformation monitoring data with multi-source geological environment data, performs analysis based on an information amount model and an analytic hierarchy process (AHP), and realizes susceptibility zoning and early warning of geological disasters. The method comprises the following steps: firstly, acquiring high-precision earth surface deformation data by using an InSAR technology, and collecting and integrating multi-source data; secondly, the contribution degree of each influence factor to the disaster is quantified through an information amount model, weights are distributed by adopting an AHP method, and the total information amount value of each grid unit is calculated; and finally, according to the total information amount, dividing the total information amount into five susceptibility grades of extremely low, low, medium, high and extremely high, setting an early warning threshold value, and providing early warning when the earth surface deformation is abnormal. The method improves the real-time performance and accuracy of disaster risk evaluation, is suitable for dynamic monitoring and prevention and control of various geological disasters such as landslide and debris flow, and has a wide application prospect.

Description

Technical Field

[0001] The invention relates to the technical field of geological disaster assessment, and in particular to a method for assessing the susceptibility of geological disasters by coupling InSAR technology. Background Art

[0002] Geological disasters (such as landslides, debris flows, and ground subsidence) are one of the main natural disasters that threaten mountainous areas, urban fringe areas, and rapidly urbanizing areas. These disasters pose a huge threat to human life and property, infrastructure, and the ecological environment. In view of the frequency and high destructiveness of geological disasters, it is of great significance to accurately assess the susceptibility of geological disasters and establish an effective early warning system to reduce disaster risks and enhance prevention and control capabilities.

[0003] At present, the assessment of geological hazard susceptibility mainly relies on the following traditional methods:

[0004] Through on-site geological surveys and topographic mapping in disaster-prone areas, the geological background and topographic characteristics of landslides, mud-rock flows and other disasters are analyzed. This type of method can better describe the geological conditions of the disaster area.

[0005] Traditional multi-factor statistical models use geological and environmental factors such as topography, lithology, fault distribution, climate and hydrology to evaluate the probability of disasters in a static analysis. Commonly used methods include the analytic hierarchy process (AHP), multivariate regression analysis, and information volume models, which usually rely on existing regional geological and meteorological data for calculation.

[0006] Using remote sensing images to evaluate the susceptibility of disasters can obtain surface feature information of a large area, including slope, vegetation cover, road distribution, etc. Remote sensing image interpretation is mainly achieved through optical images, combined with geological data and environmental factors to conduct risk assessment of geological disasters.

[0007] Although traditional methods have certain application effects in geological disaster assessment, they also have significant shortcomings, which are mainly reflected in the following aspects:

[0008] Geological surveys and remote sensing interpretation require a long data collection and processing cycle, which makes it difficult to update the evaluation results in real time. Especially in mountainous areas, remote areas or areas with inconvenient transportation, the data update speed is limited, making it difficult to timely reflect the dynamic changes of the surface and to effectively monitor and predict geological disasters in real time.

[0009] Traditional methods mostly rely on static topographic geological data (such as lithology and slope) or historical disaster distribution data, which can only reflect the long-term trend of geological disasters and cannot identify small surface deformations. Therefore, these methods have limitations in accurately locating high-risk areas for geological disasters such as landslides, resulting in low prediction accuracy.

[0010] Geological surveys and remote sensing interpretations are often concentrated in specific areas, and it is difficult to cover large areas of mountainous areas and urbanized fringe areas. Especially for large areas prone to geological disasters, these methods have bottlenecks in spatial coverage and are difficult to meet the needs of comprehensive monitoring.

[0011] The occurrence of geological disasters is often accompanied by surface deformation or local displacement, and traditional static analysis methods are difficult to capture these deformation signals. Relying only on historical geological data and static environmental factors to analyze disaster risks is difficult to track the dynamic changes of geological disasters in real time, so there is a lack of early warning capabilities and it is impossible to effectively support disaster warning and rapid response.

[0012] In order to overcome the above shortcomings, InSAR (Interferometric Synthetic Aperture Radar) technology has been introduced in recent years as a dynamic monitoring method, which can detect small displacements of the surface with high precision. InSAR technology uses radar satellites to perform multi-time series imaging of the surface, combined with interferometric analysis of the images, it can identify millimeter-level surface deformation, thereby detecting potential disaster signals such as landslides and ground subsidence. The main advantages of InSAR technology in geological disaster monitoring are:

[0013] High-precision monitoring: Able to detect millimeter-level surface displacements, suitable for identifying tiny geological changes.

[0014] Wide coverage: Suitable for large-scale monitoring, especially for geological disaster monitoring in remote areas with inconvenient transportation.

[0015] Strong real-time performance: It can obtain data at a fixed period and provide real-time monitoring capabilities for dynamic geological disaster assessment.

[0016] Although InSAR technology has improved the accuracy and real-time performance of surface deformation monitoring, its application in geological disaster susceptibility assessment still has the following problems:

[0017] InSAR technology only provides information on surface deformation, and cannot provide environmental factors such as geological structure, climate and hydrology required for disaster susceptibility assessment. Therefore, InSAR technology needs to be combined with multi-source geological and environmental data to achieve a comprehensive disaster susceptibility assessment.

[0018] The geological conditions and causes of disasters in different regions are different, and the contribution of various influencing factors to disasters is also different. The existing InSAR monitoring and multi-factor analysis models lack rationality in determining factor weights, making it difficult for the evaluation model to adapt to the actual conditions of different regions.

[0019] InSAR technology can provide deformation information, but lacks an effective mathematical model to quantify different factors. The Information Value Model quantifies the risk contribution of multiple factors by calculating the role of each factor in the probability of disaster occurrence, and is an important method for traditional disaster risk assessment. InSAR monitoring data still needs to be combined with the information value model to achieve a more scientific evaluation of disaster susceptibility.

[0020] Therefore, we urgently need to design a geological disaster susceptibility assessment method coupled with InSAR technology to solve the above problems. Summary of the invention

[0021] The purpose of the present invention is to provide a method for evaluating the susceptibility of geological disasters by coupling InSAR technology to address the deficiencies of the prior art, so as to solve the problems raised in the background technology.

[0022] To achieve the above object, the present invention provides the following technical solution: a method for evaluating the susceptibility of geological disasters by coupling InSAR technology, comprising the following steps:

[0023] InSAR data acquisition and preprocessing:

[0024] Use InSAR (Interferometric Synthetic Aperture Radar) technology to monitor the surface deformation of the target area at a high frequency and obtain the surface deformation data of the area. InSAR technology detects millimeter-level surface displacement through interferometric analysis of radar images;

[0025] The obtained surface deformation data are preprocessed, including denoising, unwrapping and removing flat ground effects, to ensure the accuracy of the surface deformation data.

[0026] Collection and integration of multi-source data: Collection of multi-source geological and environmental data of the target area, including but not limited to the following data types: topographic data, lithology and fault data, climate and hydrology data, and human activity data;

[0027] The above multi-source data are integrated through a unified coordinate system and spatial analysis platform so that they can be used for susceptibility analysis together with InSAR surface deformation data.

[0028] Quantitative processing of geological disaster impact factors:

[0029] Divide the target area into grid units of uniform standards for data analysis and calculation; quantify the main influencing factors of geological disasters (such as terrain slope, lithology, rainfall, etc.), and calculate the relative density value D of each influencing factor to represent the activity intensity of geological disasters;

[0030] The calculation formula of relative density value D is as follows:

[0031]

[0032] Among them, D is the relative density, which indicates the distribution intensity of geological hazard points in each category of influencing factors; S is the total area of ​​the study area, S i is the area of ​​a certain influencing factor category, N is the total number of geological disasters in the study area, and N i is the number of geological hazards within the category of the impact factor;

[0033] Construction of information model

[0034] Based on the acquired data of each influencing factor, the information volume model is used to quantify each influencing factor. The information volume model calculates the contribution of each influencing factor to geological hazards, and the formula is:

[0035]

[0036] Among them, P(y|x) is the probability of geological disasters occurring under the influencing factor condition x, and P(y) is the overall probability of geological disasters occurring. This information value is used to quantify the impact of each factor on the susceptibility of geological disasters.

[0037] AHP weight distribution

[0038] The analytic hierarchy process (AHP) is used to assign weights to each influencing factor. The specific steps include constructing an influencing factor comparison matrix, calculating and normalizing the eigenvector, and obtaining the weight w of each influencing factor. i , to ensure the rationality of the relative importance of each factor in the susceptibility evaluation.

[0039] Generation of geological disaster susceptibility zoning maps

[0040] The quantitative information value of all influencing factors in each grid unit is accumulated to obtain the total information value I total , and combined with the weight distribution results, the study area is divided into five susceptibility levels: very low, low, medium, high, and very high according to the amount of total information, and a geological disaster susceptibility zoning map is generated.

[0041] As a preferred technical solution of the present invention, in the quantitative processing of geological disaster impact factors, the reclassification function in ArcGIS is used to classify the geological disaster impact factors, and the raster calculator is used to count the number of raster cells in each category and the number of geological disaster points contained therein to support the calculation of relative density values.

[0042] As a preferred technical solution of the present invention, an information volume model is used to quantify the impact factors of various geological disasters, and the calculation formula of the information volume value I(x) is:

[0043]

[0044] Among them, P(y|x) is the probability of geological disasters occurring when the influencing factor condition x exists, and P(y) is the overall probability of geological disasters occurring. The information value I(x) indicates the contribution of a specific influencing factor to the occurrence of geological disasters. The larger the value, the stronger the induction of the factor to geological disasters.

[0045] As a preferred technical solution of the present invention, the information amount of all geological disaster influencing factors of each grid unit is accumulated to obtain the total information amount I of each unit. total , the calculation formula is as follows:

[0046]

[0047] Where n is the number of geological disaster impact factors, I(x i ) is the quantitative information of the i-th influencing factor. The larger the total information value is, the higher the geological disaster susceptibility of the unit is.

[0048] As a preferred technical solution of the present invention, the total information volume I total During the calculation process, the conditional probability formula is used to calculate the probability P(y|x) of each influencing factor when a geological disaster occurs and the probability P(y) of the overall geological disaster. The specific formula is as follows:

[0049]

[0050] As a preferred technical solution of the present invention, the analytic hierarchy process (AHP) is used to assign weights to the influencing factors of various geological disasters, and the weights are obtained by constructing a factor matrix and calculating the eigenvector. The specific steps are as follows: construct a pairwise comparison matrix of the influencing factors; calculate the eigenvector and normalize it to obtain the weight w of each factor. i ; Ensure the consistency of the matrix and check the consistency of the matrix by calculating the consistency ratio (CR). If CR < 0.1, the weight distribution is considered reasonable.

[0051] As a preferred technical solution of the present invention, the target area is divided into different susceptibility levels according to the total information volume and weight distribution results of each grid unit, including five susceptibility levels of very low, low, medium, high and very high. The specific division formula is:

[0052] L=I total × i

[0053] Where L is the susceptibility level, I total is the total information content of the unit, w i is the weight of each factor.

[0054] As a preferred technical solution of the present invention, it further includes a model verification step, in which the accuracy of the model is verified by known historical geological disaster event data, and the accuracy of the model prediction is calculated using the following error formula:

[0055]

[0056] Where E is the error percentage, L predicted is the susceptibility level predicted by the model, L actual is the actual susceptibility level.

[0057] As a preferred technical solution of the present invention, the real-time monitoring data based on InSAR technology is used to continuously monitor high-risk areas and trigger early warning when abnormal surface deformation occurs. The warning conditions are:

[0058] ΔI>T

[0059] Where ΔI is the total amount of information change within a certain period of time, and T is the preset warning threshold.

[0060] As a preferred technical solution of the present invention, multi-time series InSAR data is introduced to track the trend of surface deformation, and the probability of geological disasters is predicted in combination with historical geological disaster event data. The specific formula for calculating the probability of geological disasters P(t) is:

[0061] P(t)=1-e -λt

[0062] Where λ is the frequency of geological disasters, and t is the observation time period.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention provides a high-precision, real-time method for assessing the susceptibility of geological disasters. It uses InSAR technology to obtain surface deformation data and combines it with multi-source geological environment data. It uses information volume model and analytic hierarchy process (AHP) to quantitatively analyze and weight the influencing factors, significantly improving the accuracy of disaster susceptibility assessment. Compared with traditional methods, the present invention can effectively identify small deformations and comprehensively consider a variety of geological and environmental factors, making the assessment of disaster risks more scientific and accurate.

[0065] In addition, the present invention realizes rapid early warning and hierarchical management of high-risk disaster areas through real-time monitoring and dynamic early warning mechanisms, has the ability to respond in a timely manner, can issue alarms at the early stage of disasters, and support rapid prevention and control decisions of governments and emergency departments. This method is applicable to various types of geological disasters such as landslides and mud-rock flows, and is widely used in mountainous areas, urbanized marginal areas and other areas prone to geological disasters, providing important technical support and scientific basis for disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flowchart of the method for evaluating the susceptibility of geological disasters using the coupled InSAR technology proposed in the present invention;

[0067] Figure 2 This is a result diagram of Example 2 of the geological disaster susceptibility assessment method coupled with InSAR technology proposed in the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0069] The following is combined with Figure 1 , Figure 2 With reference to the accompanying drawings and a number of embodiments, specific embodiments of the present invention are described in detail.

[0070] The present invention provides a method for evaluating the susceptibility of geological disasters by coupling InSAR technology, and combines InSAR surface deformation monitoring, information volume model, analytic hierarchy process (AHP), multi-source data integration and other means to achieve susceptibility zoning and dynamic early warning of geological disasters such as landslides and debris flows. The following provides multiple embodiments to explain in detail the operating steps, data processing and analysis processes of the present invention to adapt to different geographical environments and application requirements.

[0071] Example 1: Landslide susceptibility assessment in mountainous areas

[0072] Data collection and preprocessing: InSAR technology is used to obtain surface deformation data of a mountainous area for one year. Radar images are collected once a month to detect millimeter-level surface displacement. Image processing includes denoising, unwrapping and removing flattening effects to ensure high accuracy of surface deformation data. Geological data (including lithology, fault distribution), topographic data (slope, slope aspect), climate data (annual rainfall, evaporation) and human activity data (roads, construction land, etc.) of the target area are collected, and the data are imported into ArcGIS for integration and grid processing. The size of each grid unit is 50 meters × 50 meters.

[0073] Quantitative calculation of landslide influencing factors: For different influencing factors, the relative density value D is used to represent the distribution intensity of landslides within each factor category.

[0074] The specific quantitative formula is:

[0075]

[0076] Where S represents the total area of ​​the study area, S i is the total area of ​​the influencing factor category, N is the total number of landslides in the study area, and Ni is the number of landslides within a specific influencing factor category. Based on historical landslide data, factors such as different slopes, lithologies, and rainfall are divided into several levels, and the relative density value of each level is calculated.

[0077] Information volume model construction: Based on the information volume model, the quantitative information volume I(x) of each influencing factor is calculated, and the formula is:

[0078]

[0079] Among them, P(y|x) is the probability of landslide occurrence in this influencing factor category, and P(y) is the overall probability of landslide occurrence. The total information value I(x) of each grid cell is obtained by summing up the information value I(x) of all influencing factors. total :

[0080]

[0081] Weight allocation by analytic hierarchy process (AHP): Use analytic hierarchy process to allocate weights to each influencing factor, construct a pairwise comparison matrix between influencing factors through expert scoring method, calculate the eigenvector of each factor and normalize it to obtain the weight w i Verify the consistency ratio (CR) to ensure the consistency of the matrix. If CR < 0.1, the weight distribution is considered reasonable and the weight is applied to the susceptibility calculation formula:

[0082] L=I total × i

[0083] Landslide susceptibility zoning and risk assessment: According to the total information value and weight of each grid unit, its susceptibility classification is calculated and divided into five susceptibility level areas: very low, low, medium, high, and very high, and a susceptibility zoning map is generated. To verify the accuracy of the model, the deviation between the predicted and actual landslide distribution is calculated using the following error formula:

[0084]

[0085] Example 2: Debris flow susceptibility assessment in coastal mountains

[0086] Data collection: Multi-temporal InSAR monitoring of the coastal mountains was conducted for 12 consecutive months, including the rainy and dry seasons, to capture seasonal surface deformation.

[0087] Radar interferometry data is used to monitor millimeter-level displacements in coastal mountain areas.

[0088] Topographic (slope, aspect), geological (lithology, faults), climate (precipitation), and land use (vegetation cover, human activities) data were collected and spatially aligned and gridded using ArcGIS.

[0089] Quantification of influencing factors and construction of information model: Based on the gridding of influencing factors such as terrain slope, fault and rainfall, the relative density D of landslide occurrence is calculated and quantitative analysis is performed based on the information model.

[0090] According to the information model formula:

[0091]

[0092] The information value of each influencing factor is accumulated to obtain the total information value of each grid I total , as an indicator of the possibility of debris flow.

[0093] Susceptibility zoning and dynamic warning: Based on the accumulated information value, a debris flow susceptibility zoning map is generated, and the study area is divided into five susceptibility levels: very low, low, medium, high, and very high. Set a threshold T, and when the InSAR data shows that the total information change ΔI of the surface deformation exceeds T, the warning is triggered:

[0094] ΔI>T

[0095] Issue early warning information to local governments and residents in a timely manner, identify prone areas, and reduce the potential risk of debris flow disasters.

[0096] Example 3: Geological Hazard Assessment and Building Protection in Urbanized Mountain Areas

[0097] See also Figure 2, Data collection and analysis of human activity impact: For urbanized mountainous areas, regular InSAR monitoring is carried out to evaluate the impact of construction on surface stability. Multi-source data including topography, lithology, rainfall, human activities (building density, road distribution), etc. are collected and imported into GIS for processing. In order to accurately analyze the impact of urbanization on geological stability, the foundation deformation of buildings is monitored, and possible disaster factors such as landslides and foundation settlement are analyzed.

[0098] Information model construction and weight allocation: Calculate the relative density value D of human activity factors such as building density and road density, and quantify their impact on geological disasters:

[0099]

[0100] Based on the information model formula, the quantitative information of each factor I(x) is calculated and the total information I is accumulated. totalo The weights of human activity factors are assigned using the hierarchical analysis method, and the weights of influencing factors are reasonably assigned through expert scoring and consistency testing. i .

[0101] Risk zoning and building protection recommendations: The urban area is divided into five susceptibility level areas: extremely low, low, medium, high, and extremely high. Protective measures are proposed for buildings in high-susceptibility areas, such as adding drainage facilities, strengthening foundation stability design, and restricting high-slope construction, to reduce the risk of building damage.

[0102] Example 4: Landslide Early Warning System in Mountain Tourism Area

[0103] Data collection and real-time monitoring: A real-time InSAR monitoring system is deployed in mountain tourist areas to conduct 24-hour dynamic monitoring of surface deformation. The collected data include surface deformation, meteorological information (rainfall, temperature), and human traffic. The surface deformation data in each monitoring period is imported into the information volume model, and the total information volume value I of each influencing factor is updated in real time. total。

[0104] Information volume model and warning threshold setting: Calculate the relative density value D and information volume I(x) of the surface deformation influencing factors, and add them up to get the total information volume value I totalo Set the warning threshold T. When the total information change ΔI exceeds the threshold T, the landslide warning is triggered:

[0105] ΔI>T

[0106] Early warning information is transmitted to scenic area management departments and tourists, high-risk areas are marked, and tourists are guided to evacuate quickly. Application of early warning system: Through this real-time warning system, management departments can take targeted emergency measures according to the current surface deformation state to reduce the impact of landslides on tourists and facilities. The early warning system can also provide disaster prevention references for surrounding villages.

[0107] Through these embodiments, the present invention demonstrates how to utilize the combination of InSAR technology, multi-source data and information volume model to achieve geological disaster risk zoning and dynamic early warning in various environments.

[0108] These specific implementation methods are applicable to various types of geological disasters such as landslides, mud-rock flows, and foundation subsidence, and have broad application prospects in disaster prevention, mitigation, and risk management.

[0109] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for assessing the susceptibility of geological hazards using coupled InSAR technology, characterized in that: The following steps are involved: InSAR data acquisition and preprocessing: Use InSAR technology to conduct high-frequency surface deformation monitoring of the target area and obtain surface deformation data of the area. InSAR technology detects millimeter-level surface displacement through interferometric analysis of radar images; The obtained surface deformation data are preprocessed, including denoising, unwrapping and removing flat ground effect steps; Collection and integration of multi-source data: Collection of multi-source geological and environmental data of the target area, including but not limited to the following data types: topographic data, lithology and fault data, climate and hydrology data, and human activity data; Integrate the above multi-source data through a unified coordinate system and spatial analysis platform; Quantitative processing of geological hazard influencing factors: Divide the target area into grid units of uniform standards; Quantify the main influencing factors of geological disasters and calculate the relative density value D of each influencing factor to indicate the activity intensity of geological disasters; The calculation formula of relative density value D is as follows: Among them, D is the relative density, which indicates the distribution intensity of geological hazard points in each category of influencing factors; S is the total area of ​​the study area, S i is the area of ​​a certain influencing factor category, N is the total number of geological disasters in the study area, and N i is the number of geological hazards within the category of the impact factor; Construction of information quantity model: Based on the acquired data of each impact factor, use the information quantity model to quantify each impact factor; AHP weight allocation: Use AHP to allocate weights to each influencing factor; Generation of geological disaster susceptibility zoning map: The quantitative information value of all influencing factors in each grid unit is accumulated to obtain the total information value I total , and combined with the weight distribution results, the study area is divided into five susceptibility levels: very low, low, medium, high, and very high according to the amount of total information, and a geological disaster susceptibility zoning map is generated.

2. The method for evaluating geological disaster susceptibility by coupling InSAR technology as claimed in claim 1, characterized in that: In the quantitative processing of geological hazard impact factors, the reclassification function in ArcGIS is used to classify the geological hazard impact factors, and the raster calculator is used to count the number of raster cells in each category and the number of geological hazard points contained in it to support the calculation of relative density values.

3. The method for evaluating geological disaster susceptibility by coupling InSAR technology as claimed in claim 1, characterized in that: The influencing factors of various geological disasters are quantified, and the information value I(x) is calculated using the information value model: It is characterized by that P(y|x) is the probability of geological disasters occurring when the influencing factor condition x exists, P(y) is the overall probability of geological disasters occurring, and the information value I(x) indicates the contribution of a specific influencing factor to the occurrence of geological disasters. The larger the value, the stronger the inducing effect of the factor on geological disasters.

4. The method for evaluating geological disaster susceptibility by coupling InSAR technology as claimed in claim 3, characterized in that: The information of all geological hazard influencing factors of each grid unit is accumulated to obtain the total information of each unit I total , the calculation formula is as follows: Where n is the number of geological disaster impact factors, I(x i ) is the quantitative information of the i-th influencing factor.

5. The method for evaluating geological disaster susceptibility by coupling InSAR technology as claimed in claim 4, characterized in that: Total information I total During the calculation process, the conditional probability formula is used to calculate the probability P(y|x) of each influencing factor when a geological disaster occurs and the probability P(y) of the overall geological disaster. The specific formula is as follows:

6. The method for evaluating geological disaster susceptibility by coupling InSAR technology as claimed in claim 1, characterized in that: The method also includes using the hierarchical analysis method to assign weights to the factors affecting various geological disasters, and obtaining weights by constructing a factor matrix and calculating a eigenvector. The specific steps are as follows: Construct a pairwise comparison matrix of impact factors; Calculate the eigenvector and normalize it to obtain the weight w of each factor i ; To ensure the consistency of the matrix, the consistency of the matrix was checked by calculating the consistency ratio. If CR < 0.1, the weight distribution was considered reasonable.

7. The method for evaluating geological disaster susceptibility by coupling InSAR technology as claimed in claim 4, characterized in that: According to the total amount of information and weight distribution results of each grid unit, the target area is divided into different susceptibility levels, including very low, low, medium, high, and very high. The specific division formula is: L=I total ×w i Where L is the susceptibility level, I total is the total information content of the unit, w i is the weight of each factor.

8. The method for evaluating the susceptibility of geological disasters by coupling InSAR technology as claimed in claim 1, characterized in that: The method further includes a model verification step, in which the accuracy of the model is verified by using known historical geological disaster event data, and the accuracy of the model prediction is calculated using the following error formula: Where E is the error percentage, L predicted is the susceptibility level predicted by the model, L actual is the actual susceptibility level.

9. The method for evaluating geological disaster susceptibility by coupling InSAR technology as claimed in claim 1, characterized in that: The method further includes continuous monitoring of high-risk areas based on real-time monitoring data of InSAR technology, and triggering early warning when abnormal surface deformation occurs, and the warning conditions are: ΔI>T Where ΔI is the total amount of information change within a certain period of time, and T is the preset warning threshold.

10. The method for evaluating the susceptibility of geological disasters by coupling InSAR technology as claimed in claim 1, characterized in that: The method further includes introducing multi-time series InSAR data to track the trend of surface deformation, and combining historical geological disaster event data to predict the probability of geological disasters. The specific formula for calculating the probability of geological disasters P(t) is: P(t)=1-e -λt Where λ is the frequency of geological disasters, and t is the observation time period.

Images