Method for evaluating susceptibility of geological disasters

Abstract

The invention discloses a method for evaluating the susceptibility of geological disasters. The method comprises the following steps: dividing an evaluation area into a plurality of grid evaluation units; obtaining an evaluation factor and a deformation rate of the evaluation area, wherein the evaluation factors of the grid evaluation units of the evaluation area are the same; removing the corresponding evaluation factors in the evaluation area according to the relation strength among the evaluation factors in the evaluation area; determining the weight of each evaluation factor in the evaluation area according to an entropy weight method; determining a positive membership degree and a negative membership degree of each grid evaluation unit in the evaluation region according to a ratio between the deformation rate and the evaluation factor of each grid evaluation unit in the evaluation region through a double extreme value fuzzy set algorithm; and determining a risk value of each grid evaluation unit according to the weight, the positive membership degree and the negative membership degree of the evaluation factor of each grid evaluation unit, so as to improve the accuracy of geological disaster susceptibility evaluation.

Description

Technical Field

[0001] The present application relates to the technical field of geological disaster assessment, and in particular to a method for assessing the susceptibility of geological disasters. Background Art

[0002] Geological disaster susceptibility assessment is an indispensable part of railway early warning and disaster prevention and mitigation. It can not only identify risks in advance and optimize railway design, but also provide scientific response strategies when disasters occur, effectively protect people’s lives and property, and ensure the safe operation of railways.

[0003] There are great differences in the formation mechanism and damage mode of geological disasters. The mechanism of disasters is complex and is controlled by the joint action of multiple internal and external factors. Therefore, with the changes in the geographical environment, disasters will show nonlinear and unstable change characteristics, and the influence of various factors on the induced disasters is different, which makes it difficult to determine the influencing factors and contribution values ​​of geological disasters. This will increase the difficulty of geological disaster susceptibility assessment and easily lead to low accuracy of assessment results.

[0004] Therefore, there is an urgent need to provide a method for geological disaster susceptibility assessment in order to improve the accuracy of geological disaster susceptibility assessment results.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The embodiment of the present application aims to solve the technical problem of inaccurate geological disaster susceptibility assessment by providing a method for geological disaster susceptibility assessment.

[0007] To achieve the above objectives, the present application provides a method for assessing the susceptibility of geological disasters. The method for assessing the susceptibility of geological disasters includes the following:

[0008] Divide the assessment area into multiple grid assessment units;

[0009] Acquiring an evaluation factor and a deformation rate of the evaluation area, wherein the evaluation factors of the grid evaluation units in the evaluation area are the same;

[0010] According to the relationship strength between the evaluation factors in the evaluation area, eliminating the corresponding evaluation factors in the evaluation area;

[0011] Determine the weight of each evaluation factor in the evaluation area according to the entropy weight method;

[0012] Determine the positive membership and negative membership of each grid evaluation unit in the evaluation area according to the deformation rate of each grid evaluation unit in the evaluation area and the proportion of the evaluation factor by a bi-extreme fuzzy set algorithm;

[0013] The risk value of each grid evaluation unit is determined according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit.

[0014] Optionally, the step of eliminating the corresponding evaluation factors according to the relationship strength between the factors to be evaluated includes:

[0015] Determine the absolute value of the Pearson correlation coefficient between the evaluation factors;

[0016] Eliminate one of the evaluation factors whose absolute value of the Pearson correlation coefficient is greater than a first preset threshold;

[0017] Performing multicollinearity analysis on the evaluation factors to determine the VIF values ​​and TOL values ​​between the evaluation factors;

[0018] Eliminate one of the evaluation factors whose VIF value is greater than the second preset threshold and whose TOL value is less than the third preset threshold;

[0019] Among them, the first preset threshold is 0.5, the second preset threshold is 5, and the third preset threshold is 0.2.

[0020] Optionally, the step of determining the risk value of each grid evaluation unit according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit includes:

[0021] Substituting the weight of the evaluation factor and the positive membership of the grid evaluation unit into a first preset formula to obtain a comprehensive score of the positive membership of the grid evaluation unit, and substituting the weight of the evaluation factor and the negative membership of the grid evaluation unit into a second preset formula to obtain a comprehensive score of the negative membership of the grid evaluation unit;

[0022] Substituting the comprehensive score of the positive membership and the comprehensive score of the negative membership of the grid evaluation unit into a third preset formula to obtain the risk value of the grid evaluation unit;

[0023] Wherein, the first preset formula is:

[0024]

[0025] The second preset formula is:

[0026]

[0027] The third preset formula is:

[0028]

[0029] Among them, R1 is the comprehensive score of positive membership, R2 is the comprehensive score of negative membership, W is the weight of each evaluation factor, u i is the positive membership of the ith grid evaluation unit, vi is the negative membership of the ith grid evaluation unit, and S is the risk value of the grid evaluation unit.

[0030] Optionally, before dividing the evaluation area into a plurality of grid evaluation units, the step includes:

[0031] Dividing the assessment area into at least two sub-assessment areas according to the topographical features of the assessment area;

[0032] The sub-evaluation areas are updated to the evaluation areas, so as to respectively evaluate the sub-evaluation areas.

[0033] Optionally, after the step of determining the risk value of each grid evaluation unit according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit, the step further includes:

[0034] Classifying the risk values ​​of each of the grid evaluation units using a natural breakpoint method to divide the evaluation area into risk areas with different risk levels;

[0035] The risk area includes at least one of an extremely high risk area, a high risk area, a medium risk area, a low risk area and an extremely low risk area.

[0036] Optionally, after the step of classifying the risk values ​​of the grid evaluation units by using a natural breakpoint method to divide the evaluation area into risk areas with different risk levels, the method further includes:

[0037] Obtain the number of disaster points in the risk area from the geological disaster database;

[0038] Determine whether the number of disaster points matches the risk level of the risk area, and output the matching result.

[0039] Optionally, before the step of obtaining the number of disaster points in the risk area from the geological disaster database, the step further includes:

[0040] Identifying potential active disaster points in the assessment area according to the deformation rate of the assessment area;

[0041] Determine whether the assessment area has the potential active disaster point according to the latest image of the assessment area;

[0042] If not, the potential active disaster point is eliminated;

[0043] Eliminate the potential active disaster sites that are historical active disaster sites;

[0044] The potential active disaster points that have not been eliminated are added to the geological disaster database.

[0045] Optionally, the step of obtaining the deformation rate of the evaluation area includes:

[0046] Acquire ascending SAR images and descending SAR images of the assessment area collected during a preset duration;

[0047] The ascending SAR image and the descending SAR image are subjected to SBAS-InSAR processing to obtain the deformation rate of the evaluation area.

[0048] Optionally, the ascending SAR image and the descending SAR image are SLC format data obtained by the Sentinel-1A satellite using an interferometric wide-band mode and a VV polarization mode.

[0049] Optionally, before performing SBAS-InSAR processing on the ascending SAR image and the descending SAR image to obtain the deformation rate of the assessment area, the method further includes:

[0050] Preprocessing the ascending SAR image and the descending SAR image; wherein the preprocessing includes at least one of the following steps:

[0051] Using the precise orbit data to register the orbit-raising SAR image, and using the precise orbit data to register the orbit-descending SAR image, so as to correct the orbit errors of the orbit-raising SAR image and the orbit-descending SAR image;

[0052] If the ascending SAR image and / or the descending SAR image does not completely cover the assessment area, multiple ascending SAR images of the assessment area acquired at the same time are stitched together to obtain the ascending SAR image that completely covers the assessment area, and / or multiple descending SAR images of the assessment area acquired at the same time are stitched together to obtain the descending SAR image that completely covers the assessment area;

[0053] If there is a non-evaluation area in the ascending SAR image and / or the descending SAR image, the ascending SAR image is cropped to obtain the ascending SAR image containing only the evaluation area, and / or the descending SAR image is cropped to obtain the descending SAR image containing only the evaluation area.

[0054] The present application proposes a method for assessing the susceptibility of geological disasters, which divides the assessment area into multiple grid assessment units, obtains the assessment factors and deformation rates of the assessment area, wherein the assessment factors of the various grid assessment units in the assessment area are the same, and then eliminates the corresponding assessment factors in the assessment area according to the strength of the relationship between the various assessment factors in the assessment area. Then, the weights of the various evaluation factors in the assessment area are determined according to the entropy weight method, and the positive and negative memberships of the various grid assessment units in the assessment area are determined according to the proportion between the deformation rate and the assessment factor of each grid assessment unit in the assessment area through the bi-extreme fuzzy set algorithm. Finally, the risk value of each grid assessment unit is determined according to the weight, positive membership and negative membership of the evaluation factors of each grid assessment unit, so as to improve the accuracy of the assessment of the susceptibility of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flowchart of the steps of a method for assessing the susceptibility of geological disasters proposed in Example 1 of the present application;

[0056] Figure 2 A flowchart of a method for assessing the susceptibility of geological disasters proposed in the second embodiment of the application;

[0057] Figure 3 This is a partial interferometric result diagram obtained by SBAS-InSAR processing;

[0058] Figure 4 This is the deformation rate result map of the evaluation area output after SBAS-InSAR processing;

[0059] Figure 5 It is the Pearson correlation coefficient diagram between the evaluation factors of the northern sub-evaluation area and the southern sub-evaluation area;

[0060] Figure 6 This is the result of multicollinearity analysis between the evaluation factors in the northern sub-assessment area and the southern sub-assessment area;

[0061] Figure 7 This is the result map of geological disaster susceptibility assessment;

[0062] Figure 8 It is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present application.

[0063] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] In order to better understand the above technical solution, exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0066] Embodiment 1

[0067] Reference Figure 1 , Figure 1 This is a flowchart of a method for assessing the susceptibility of geological disasters proposed in Example 1 of the present application. The method for assessing the susceptibility of geological disasters provided in the present application includes the following steps S10 to S60:

[0068] Step S10: Divide the evaluation area into a plurality of grid evaluation units;

[0069] In some embodiments, the size of the grid evaluation unit can be determined based on a grid empirical formula. The grid empirical formula is the following formula (1):

[0070] (1)

[0071] Among them, Gs is the size of the grid evaluation unit, and S is the denominator of the accuracy of the original contour data.

[0072] In some embodiments, to facilitate subsequent calculations, Gs may be an integer.

[0073] In other embodiments, the assessment area may also be divided into administrative division assessment units or slope assessment units, which are not specifically limited in this embodiment. Those skilled in the art may make flexible choices based on the content and teachings disclosed in this application.

[0074] Step S20: obtaining an evaluation factor and a deformation rate of the evaluation area, wherein the evaluation factors of the grid evaluation units in the evaluation area are the same;

[0075] In some embodiments, the evaluation factor refers to a key factor that may cause a disaster to occur in the evaluation area. As an example, the evaluation factor of the evaluation area can be determined in advance by professional and technical personnel. Specifically, the professional and technical personnel can comprehensively analyze and determine the evaluation factor based on factors such as the topographical features, climatic conditions, soil types, vegetation coverage, and historical disaster records of the evaluation area.

[0076] In some embodiments, the deformation rate of the assessment area can be obtained by acquiring ascending SAR images and descending SAR images of the assessment area collected during a preset duration, and then performing SBAS-InSAR (Small BaselineSubset Interferometric Synthetic Aperture Radar) processing on the ascending SAR images and the descending SAR images. In some embodiments, the ascending SAR images and the descending SAR images can be SLC format data collected by the Sentinel-1A satellite using the interferometric wide swath (IW) mode and VV polarization mode. The length of the preset duration is at least one year.

[0077] SBAS-InSAR is a technology that uses synthetic aperture radar (SAR) image data to monitor ground deformation. SBAS is a differential interferometric synthetic aperture radar (D-InSAR) technology based on the small baseline subset method. It uses multi-temporal image data to monitor small deformations of the surface, and is particularly suitable for long-term monitoring and ground deformation analysis in large areas. This method can effectively reduce the effects of atmospheric delay and orbital errors, thereby obtaining high-precision deformation rate information. This embodiment does not specifically give the detailed process of SBAS-InSAR processing, and those skilled in the art can refer to the prior art for implementation.

[0078] It should be noted that for the parameter settings of SBAS-InSAR processing, such as the time baseline threshold, the space baseline threshold, the number of interferometric image pairs, the multi-view ratio, the interferometric processing method, and the method of removing the terrain phase caused by terrain undulation factors, those skilled in the art can flexibly adjust them according to actual conditions in order to obtain an accurate deformation rate.

[0079] Furthermore, in some embodiments, before performing SBAS-InSAR processing on the ascending SAR images and the descending SAR images to obtain the deformation rate of the evaluation area, the ascending SAR images and the descending SAR images may be pre-processed to provide accurate input data for subsequent SBAS-InSAR processing, thereby obtaining a more accurate deformation rate. The preprocessing may include at least one of the following steps: using precise orbit data to register the ascending SAR image, and using precise orbit data to register the descending SAR image to correct the orbit errors of the ascending SAR image and the descending SAR image; if the ascending SAR image and / or the descending SAR image do not completely cover the evaluation area, then stitching multiple ascending SAR images of the evaluation area acquired at the same time to obtain an ascending SAR image that completely covers the evaluation area, and / or stitching multiple descending SAR images of the evaluation area acquired at the same time to obtain a descending SAR image that completely covers the evaluation area; if there is a non-evaluation area in the ascending SAR image and / or the descending SAR image, then cropping the ascending SAR image to obtain an ascending SAR image that only contains the evaluation area, and / or cropping the descending SAR image to obtain a descending SAR image that only contains the evaluation area.

[0080] Step S30: according to the relationship strength between the evaluation factors in the evaluation area, eliminating the corresponding evaluation factors in the evaluation area;

[0081] In some embodiments, linear correlation analysis and double collinearity analysis can be used to determine the strength of the relationship between the evaluation factors, so as to decide whether to remove certain evaluation factors. Specifically, according to the strength of the relationship between the evaluation factors in the evaluation area, the step of removing the corresponding evaluation factors in the evaluation area may include: determining the absolute value of the Pearson correlation coefficient between the evaluation factors, and removing one of the evaluation factors whose absolute value of the Pearson correlation coefficient is greater than a first preset threshold.

[0082] Then, multicollinearity analysis is performed on the evaluation factors to determine the VIF value and TOL value between the evaluation factors, and one of the evaluation factors whose VIF value is greater than the second preset threshold and whose TOL value is less than the third preset threshold is eliminated. Among them, the first preset threshold is 0.5, the second preset threshold is 5, and the third preset threshold is 0.2. Through the above operations, evaluation factors with strong correlation and multicollinearity problems can be eliminated, thereby improving the reliability and effectiveness of the evaluation factors.

[0083] The Pearson correlation coefficient between the evaluation factors can be calculated by the following formula (2):

[0084]

[0085] Where r is the Pearson correlation coefficient, is the I-th observation value of the evaluation factor x, is the I-th observation value of the evaluation factor Y, is the mean value of the evaluation factor x, is the mean value of the evaluation factor Y.

[0086] In some embodiments, SPSS 26 mathematical analysis software can be used to perform multicollinearity analysis on the evaluation factors to obtain VIF values ​​and TOL values ​​between the evaluation factors. Those skilled in the art can also use other methods to perform multicollinearity analysis on the evaluation factors, which is not specifically limited in this embodiment.

[0087] Step S40: determining the weight of each evaluation factor in the evaluation area according to the entropy weight method;

[0088] The Entropy Weight Method is an objective weighting method, which is widely used in multi-attribute decision making (MCDM). It quantifies the information content of each indicator by using the concept of information entropy, thereby assigning a weight to each indicator. Those skilled in the art can determine the weight of each evaluation factor in the evaluation area based on the existing technical framework and the Entropy Weight Method. The specific details will not be repeated here.

[0089] In some embodiments, the weight of the evaluation factor can be determined by the relevant software according to the entropy weight method. For example, the weight of the evaluation factor can be determined by the passpro software according to the entropy weight method. In this way, the evaluation factor only needs to be input into the passpro software, and the passpro software can determine the weight of the evaluation factor according to the entropy weight method.

[0090] Step S50: determining the positive membership and negative membership of each grid evaluation unit in the evaluation area according to the deformation rate of each grid evaluation unit in the evaluation area and the proportion of the evaluation factor by a bi-extreme fuzzy set algorithm;

[0091] The bi-extreme fuzzy set algorithm is a concept expanded by Zhang on the traditional fuzzy set. The biggest difference between it and the traditional fuzzy set is that the domain of the fuzzy set is expanded from [0,1]x[0,1] to [0,1]x[-1,0]. The incompatible polarity is successfully introduced into the fuzzy set theory. The determination of the positive and negative extreme values ​​in the bi-extreme fuzzy set often depends on expert experience or historical data. If the parameters are set improperly, the final evaluation results will be affected. However, the deformation rate can reflect the rate and situation of the disaster, and can make up for the influence of the timeliness of the previous data and the subjectivity of the experts. Therefore, this embodiment determines the positive membership and negative membership according to the ratio between the deformation rate and the evaluation factor to obtain a more accurate evaluation result.

[0092] In some embodiments, based on the principle of bi-extreme fuzzy sets, it can be known that the corresponding positive membership and negative membership can be obtained through the proportion mapping between the deformation rate and the evaluation factor.

[0093] Step S60: determining the risk value of each grid evaluation unit according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit.

[0094] In this embodiment, the risk value represents the probability of geological disasters occurring at the location corresponding to the grid evaluation unit. The greater the risk value, the greater the probability of geological disasters occurring, and the smaller the risk value, the smaller the probability of geological disasters occurring.

[0095] It can be understood that the bimodal fuzzy set algorithm determines the risk value of each grid evaluation unit based on the weight, positive membership and negative membership of the evaluation factor of each grid evaluation unit. Specifically, in some embodiments, the weight and positive membership of the evaluation factor of the grid evaluation unit can be substituted into the first preset formula to obtain the comprehensive score of the positive membership of the grid evaluation unit, and the weight and negative membership of the evaluation factor of the grid evaluation unit can be substituted into the second preset formula to obtain the comprehensive score of the negative membership of the grid evaluation unit. Then, the comprehensive score of the positive membership and the comprehensive score of the negative membership of the grid evaluation unit are substituted into the third preset formula to obtain the risk value of the grid evaluation unit.

[0096] The first preset formula is the following formula (3):

[0097] (3)

[0098] The second preset formula is the following formula (4):

[0099] (4)

[0100] The third preset formula is the following formula (5):

[0101] (5)

[0102] Among them, R1 is the comprehensive score of positive membership, R2 is the comprehensive score of negative membership, W is the weight of each evaluation factor, u i is the positive membership of the ith grid evaluation unit, vi is the negative membership of the ith grid evaluation unit, and S is the risk value of the grid evaluation unit.

[0103] In some embodiments, in order to further improve the accuracy of the evaluation results, the grid evaluation units can be graded, and the positive and negative memberships of each level can be determined by using the bi-extreme fuzzy set algorithm according to the ratio between the deformation rate of the level and the evaluation factor. Finally, the risk value of each level is determined according to the weight, positive and negative membership of the evaluation factors of each level.

[0104] In the technical solution provided in this embodiment, the assessment area is divided into a plurality of grid assessment units so that each assessment unit can be independently assessed for the susceptibility of geological disasters, and the accuracy of the assessment can be improved. Then, by obtaining the assessment factor and deformation rate of the assessment area, the assessment factors of each grid assessment unit in the assessment area are the same, and then according to the relationship strength between each assessment factor in the assessment area, the corresponding assessment factor in the assessment area is eliminated, which further improves the accuracy of the assessment and can also improve the efficiency of the assessment. Then, the weight of each evaluation factor in the assessment area is determined according to the entropy weight method, and the positive membership and negative membership of each grid assessment unit in the assessment area are determined according to the deformation rate and the proportion of the evaluation factor of each grid assessment unit in the assessment area through the bi-extreme fuzzy set algorithm. Finally, according to the weight, positive membership and negative membership of the evaluation factor of each grid assessment unit, the risk value of each grid assessment unit is determined, thereby improving the accuracy of the assessment of the susceptibility of geological disasters.

[0105] Embodiment 2

[0106] Reference Figure 2 , Figure 2 This is a flowchart of a method for assessing the susceptibility of geological disasters proposed in the second embodiment of the application. In the second embodiment, based on the first embodiment, the step S60 further includes a step S70:

[0107] Step S70: Classify the risk values ​​of each of the grid evaluation units using a natural breakpoint method to divide the assessment area into risk areas with different risk levels; wherein the risk areas include at least one of an extremely high risk area, a high risk area, a medium risk area, a low risk area and an extremely low risk area.

[0108] In this embodiment, the risk values ​​are classified by the natural breakpoint method, which can ensure a more scientific and reasonable division of the risk levels of geological disaster susceptibility, and facilitate targeted management and prevention of geological disaster risks in various risk areas.

[0109] In the technical solution provided in this embodiment, after determining the risk value of each grid evaluation unit in the assessment area, the risk value of each grid evaluation unit is further classified using the natural breakpoint method to divide the assessment area into risk areas with different risk levels, thereby ensuring a more scientific and reasonable division of the risk level of geological disaster susceptibility, and facilitating targeted management and prevention of geological disaster risks in each risk area.

[0110] Embodiment 3

[0111] In the third embodiment, based on any of the above embodiments, the step S10 includes the following steps: step S80 and step S90:

[0112] Step S80: dividing the assessment area into at least two sub-assessment areas according to the topographical features of the assessment area;

[0113] Step S90: updating the sub-evaluation area to the evaluation area, so as to evaluate the sub-evaluation area respectively.

[0114] In this embodiment, when the assessment area is large and the topography and geomorphology of different sections are significantly different, in order to improve the accuracy of the assessment of geological disaster susceptibility, reasonable regional division can be performed according to the topography and geomorphology characteristics. Specifically, the assessment area can be divided into multiple sub-assessment areas based on factors such as the undulation of the terrain, slope, soil type, and lithology distribution. For example, different types of geomorphic areas such as mountains, hills, and plains can be divided into different sub-assessment areas, and the terrain characteristics within each sub-assessment area are relatively consistent, so as to facilitate a more accurate geological disaster risk analysis. As for whether it is necessary to divide the assessment area into multiple sub-assessment areas, technicians in this field can make a flexible choice based on the actual situation and needs of the assessment area.

[0115] In the technical solution provided in this embodiment, the assessment area is divided into at least two sub-assessment areas according to the topographical features of the assessment area, and then the sub-assessment areas are updated to the assessment areas, so that the aforementioned steps S10 to S60 are performed separately for each sub-assessment area, thereby further improving the accuracy of the geological hazard susceptibility assessment.

[0116] Embodiment 4

[0117] In the fourth embodiment, based on any of the above embodiments, the step S70 further includes steps S100 and S110:

[0118] Step S100: Acquire the number of disaster points in the risk area from the geological disaster database;

[0119] Step S110: Determine whether the number of disaster points matches the risk level of the risk area, and output the matching result.

[0120] In this embodiment, the geological disaster database records the disaster point information monitored in each area. The geological disaster database can be an official agency or a public database pre-built by itself, which usually contains a large amount of historical data on the occurrence, distribution, type and impact of geological disasters. Therefore, by obtaining the number of disaster points in the risk area from the geological disaster database, it is determined whether the recorded disaster point data matches the risk level of the risk area, and the matching result is output. According to the matching result, the accuracy of this disaster susceptibility assessment can be evaluated, and it can be verified whether it meets the set accuracy requirements, thereby effectively improving the reliability of the disaster susceptibility assessment.

[0121] In some embodiments, if the disaster susceptibility assessment results of the assessment area do not meet the set accuracy requirements, the assessment method and / or the data required for the assessment can be further optimized to improve the accuracy of the disaster risk assessment.

[0122] Most of the records in the geological disaster database are historical disaster point information, but the occurrence of geological disasters is a dynamic process. New disasters may occur during the assessment process. However, the newly occurring disasters are not recorded in the geological disaster database, which will lead to the lack of timeliness and accuracy of the assessment results. To solve this defect, in some embodiments, the step of obtaining the number of disaster points in the risk area from the geological disaster database may also include: first, according to the deformation rate of the assessment area, identifying the potential active disaster points in the assessment area. As an example, in some embodiments, the potential active disaster points can be identified according to the deformation rate of the assessment area. Specifically, by setting a deformation rate threshold, the position greater than the deformation rate threshold can be identified as a potential active disaster point. Afterwards, in order to determine the authenticity and reliability of the potential active disaster point, the latest image and historical image of the assessment area are further compared to verify whether there are potential active disaster points in the assessment area. If not, the potential active disaster point is eliminated. Then the potential active disaster points belonging to the historical active disaster points are eliminated. Finally, the potential active disaster points that are not eliminated are added to the geological disaster database. The higher the resolution of the latest image and historical image used for comparison, the better. For example, the latest image and the historical image may adopt Google satellite image, which has high resolution.

[0123] In this embodiment, before obtaining the number of disaster points in the risk area from the geological disaster database, the newly emerged potential active disaster points in the assessment area are first identified and added to the geological disaster database, so that the geological disaster database not only records the historical disaster points in the assessment area, but also records the newly emerged potential active disaster points, thereby ensuring the timeliness and accuracy of the matching of the assessment results.

[0124] In other embodiments, in order to further verify the authenticity and reliability of potential active disaster points, after comparing the latest image and historical images of the assessment area to verify whether there are potential active disaster points in the assessment area, a preset number of target potential active disaster points can be selected to conduct on-site inspections of the target potential active disaster points to verify whether the target potential active disaster points are actually potential active disaster points. If not, the target potential active disaster points are re-identified or eliminated.

[0125] In the technical solution provided in this embodiment, the number of disaster points in the risk area is obtained from the geological disaster database, it is determined whether the number of disaster points and the risk level of the risk area match, and the matching result is output, so that the accuracy of the disaster susceptibility assessment can be evaluated based on the matching result, and it can be verified whether it meets the set accuracy requirements, thereby effectively improving the reliability of the disaster susceptibility assessment.

[0126] Embodiment 5

[0127] This embodiment will provide Example 1 as a specific application scenario, but this example does not impose any specific limitation on this application.

[0128] Example 1:

[0129] Assuming that a geological disaster susceptibility assessment is conducted on the railway located in the southwest of Yunnan Province, the area where the railway is located is taken as the assessment area. Among them, the scope of the assessment area can be expanded outward from the railway line to cover a certain range of surrounding geographical areas, so as to comprehensively assess the geological disaster risks that may affect the safety of the railway. Since the scope of the assessment area is large and the terrain and geomorphic features are significantly different, the southern area of ​​Mengyang Town is divided into northern and southern sub-assessment areas according to the terrain and geomorphic features along the railway, and the northern sub-assessment area and the southern sub-assessment area are evaluated respectively. Next, the assessment of the northern sub-assessment area and the southern sub-assessment area will be described in detail, while the assessment methods of other sub-assessment areas are the same as those of these two sub-areas and will not be described in detail. In short, the focus is on the detailed analysis of the northern and southern sub-assessment areas, while other areas follow the same assessment method.

[0130] First, the denominator of the original contour accuracy of the assessment area map is known to be 50000. Substituting it into the above formula (1), we can get the size of the grid evaluation unit Gs as 32.853m. To simplify subsequent calculations, the size of the grid evaluation unit can be an integer, 30m. Therefore, the size of the grid evaluation unit can be determined as 30mx30m, and then the northern sub-assessment area and the southern sub-assessment area are divided into multiple grid evaluation units of 30mx30m respectively.

[0131] Furthermore, the northern sub-assessment area selected elevation, slope, aspect, distance to water system, distance to fault, stratum lithology, soil type, land use type, vegetation coverage, precipitation, and soil moisture as evaluation factors. The southern sub-assessment area selected elevation, distance to water system, distance to fault, stratum lithology, soil type, land use type, vegetation coverage, precipitation, and soil moisture as evaluation factors.

[0132] By acquiring the ascending SAR images and descending SAR images of the evaluation area collected from December 2021 to August 2023, the ascending SAR images and descending SAR images are SLC format data collected by the Sentinel-1A satellite using the interferometric wideband (IW) mode and VV polarization mode. Then, 36 ascending SAR images and 50 descending SAR images after registration and cropping for two years after the opening of the railway (December 2021-August 2023) are selected from the above SAR images as the input data for SBAS-InSAR processing. It can be understood that the stitching, registration and cropping correspond to the preprocessing described above. The parameter settings for SBAS-InSAR processing can include: time baseline threshold of 108 days and 84 days, space baseline threshold of 10%, generating 143 pairs and 203 pairs of interferometric image pairs, multi-view ratio of 1:4, using Minimum Cost Flow unwrapping method and Goldstein unwrapping method for interferometric processing, and using SRTM (NASA) data to remove terrain phase caused by terrain undulation factors. Figure 3 , Figure 3 This is a partial interference result diagram obtained by SBAS-InSAR processing. Figure 3 Each figure number is followed by a time stamp. Figure 3 The time after (a) is 20211210-20211222. Figure 3 It can be seen that after preprocessing and then SBAS-InSAR processing, an interference result map with higher interference quality can be obtained, so that a higher-precision deformation rate can be finally obtained. Figure 4 , Figure 4 This is the deformation rate result diagram of the evaluation area output after SBAS-InSAR processing. Figure 4 The figure (a) in the middle corresponds to the deformation rate result diagram of the ascending SAR image. Figure 4 The middle figure (b) corresponds to the deformation rate result diagram of the descending SAR image.

[0133] Then, the Pearson correlation coefficients between the evaluation factors in the northern sub-evaluation area and the southern sub-evaluation area are calculated according to the above formula (2). Figure 5 , Figure 5It is the Pearson correlation coefficient diagram between the evaluation factors of the northern sub-assessment area and the southern sub-assessment area. Figure 5 Figure (a) in the middle shows the Pearson correlation coefficient between the evaluation factors in the northern sub-assessment area. Figure 5 Figure (b) shows the Pearson correlation coefficients between the evaluation factors in the southern sub-assessment area. It can be understood that the horizontal and vertical axes in the figure are the English abbreviations of the evaluation factors. One of the two evaluation factors with an absolute value of the Pearson correlation coefficient greater than 0.5 is eliminated.

[0134] Then, we continued to conduct multicollinearity analysis on the remaining evaluation factors in the northern sub-evaluation area and the southern sub-evaluation area using SPSS 26 mathematical analysis software, and calculated the VIF value and TOL value between the evaluation factors. Figure 6 , Figure 6 This is the result of multicollinearity analysis between the evaluation factors in the northern and southern sub-assessment areas. Figure 6 Figure (a) in the middle shows the results of multicollinearity analysis among the evaluation factors in the northern sub-assessment area. Figure 6 Figure (b) shows the results of the multilinear analysis between the evaluation factors in the southern sub-assessment area. Then, one of the evaluation factors with a VIF value greater than 5 and a TOL value less than 0.2 was eliminated.

[0135] After the above operations, the evaluation factor for soil moisture in the northern sub-assessment area was eliminated, while the evaluation factor in the southern sub-assessment area was not eliminated.

[0136] Next, the Spaspro software was used to calculate the weights of each evaluation factor in the northern sub-assessment area and the southern sub-assessment area according to the entropy weight method. Refer to Tables 1 and 2 below. Table 1 shows the weights of each evaluation factor in the northern sub-assessment area, and Table 2 shows the weights of each evaluation factor in the southern sub-assessment area.

[0137] Table 1. Weights of each evaluation factor in the northern sub-assessment area

[0138] Table 2. Weights of each evaluation factor in the southern sub-assessment area

[0139] Furthermore, the positive and negative memberships of each grid evaluation unit in the northern sub-assessment area and the southern sub-assessment area are calculated by using the bi-extreme fuzzy set algorithm, based on the ratio between the deformation rate and the evaluation factor of each grid evaluation unit in the northern sub-assessment area and the southern sub-assessment area. Based on the bi-extreme fuzzy set principle, the corresponding positive and negative memberships can be obtained by mapping the ratio between the deformation rate and the evaluation factor. Due to the large amount of data involved, it is not published here.

[0140] Then, based on the above formulas (3), (4) and (5), the risk value of each grid evaluation unit is determined according to the weight, positive membership and negative membership of the evaluation factor of each grid evaluation unit. Then, the risk value of each grid evaluation unit is classified using the natural breakpoint method, and the southern area of ​​Mengyang Town, which consists of the northern sub-assessment area and the southern sub-assessment area, is divided into extremely high-risk areas, high-risk areas, medium-risk areas, low-risk areas and extremely low-risk areas. Figure 7 , Figure 7 This is the result of geological disaster susceptibility assessment. Figure 7 Figure (a) in the middle is a geological disaster susceptibility assessment result diagram of the southern area of ​​Mengyang Town obtained by a method for geological disaster susceptibility assessment provided in this application.

[0141] In order to compare the effect of the method of the present application (a method for assessing the susceptibility of geological disasters) with the BP neural network model in the prior art, the BP neural network model was used to assess the susceptibility of geological disasters in the southern area of ​​Mengyang Town. Figure 7 Figure (b) in the middle shows the geological disaster susceptibility assessment results of the southern area of ​​Mengyang Town obtained through BP neural network assessment.

[0142] The receiver operating characteristic curve (ROC curve) is a graphical method for evaluating the effectiveness of classification, in which the horizontal axis is the false positive rate (1-specificity), the vertical axis is the true positive rate (sensitivity), and the area under the curve (AUC) is often used to evaluate the accuracy of the model. The larger the AUC value, the higher the accuracy of the model. The ROC curve was drawn by importing the evaluation data into SPSS 21.0 software, in which the vertical axis sensitivity is the proportion of units with geological disasters that are correctly predicted, and the horizontal axis specificity is the proportion of units without geological disasters that are correctly predicted. Using SPSS software for analysis, the AUC value of the northern sub-assessment area of ​​the railway obtained by the method of this application is 0.913, and the AUC value of the BP neural network model is 0.854. The AUC value of the southern sub-assessment area of ​​the railway obtained by the method of this application is 0.896, and the AUC value of the BP neural network model is 0.823.

[0143] According to the ROC curve accuracy results, the AUC values ​​of the northern and southern sub-assessment areas of the railway obtained by the method of this application are 0.913 and 0.896, which are 0.059 and 0.073 higher than those of BP. Therefore, compared with the BP neural network model, the method of this application can effectively solve the problem of uncertainty in the contribution of evaluation factors in the evaluation process, which can fully prove the superiority of the method of this application.

[0144] Reference Figure 8 , Figure 8This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present application.

[0145] like Figure 8 As shown, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), a mouse, etc., and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0146] Those skilled in the art will understand that Figure 8 The terminal structure shown in the figure does not constitute a limitation on the terminal, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0147] like Figure 8 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a program for geological hazard susceptibility assessment.

[0148] exist Figure 8 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and perform data communication with the backend server; the processor 1001 can be used to call the program for geological disaster susceptibility assessment stored in the memory 1005 and perform the following operations:

[0149] Divide the assessment area into multiple grid assessment units;

[0150] Acquiring an evaluation factor and a deformation rate of the evaluation area, wherein the evaluation factors of the grid evaluation units in the evaluation area are the same;

[0151] According to the relationship strength between the evaluation factors in the evaluation area, eliminating the corresponding evaluation factors in the evaluation area;

[0152] Determine the weight of each evaluation factor in the evaluation area according to the entropy weight method;

[0153] Determine the positive membership and negative membership of each grid evaluation unit in the evaluation area according to the deformation rate of each grid evaluation unit in the evaluation area and the proportion of the evaluation factor by a bi-extreme fuzzy set algorithm;

[0154] The risk value of each grid evaluation unit is determined according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit.

[0155] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0156] Determine the absolute value of the Pearson correlation coefficient between the evaluation factors;

[0157] Eliminate one of the evaluation factors whose absolute value of the Pearson correlation coefficient is greater than a first preset threshold;

[0158] Performing multicollinearity analysis on the evaluation factors to determine the VIF values ​​and TOL values ​​between the evaluation factors;

[0159] Eliminate one of the evaluation factors whose VIF value is greater than the second preset threshold and whose TOL value is less than the third preset threshold;

[0160] Among them, the first preset threshold is 0.5, the second preset threshold is 5, and the third preset threshold is 0.2.

[0161] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0162] Substituting the weight of the evaluation factor and the positive membership of the grid evaluation unit into a first preset formula to obtain a comprehensive score of the positive membership of the grid evaluation unit, and substituting the weight of the evaluation factor and the negative membership of the grid evaluation unit into a second preset formula to obtain a comprehensive score of the negative membership of the grid evaluation unit;

[0163] Substituting the comprehensive score of the positive membership and the comprehensive score of the negative membership of the grid evaluation unit into a third preset formula to obtain the risk value of the grid evaluation unit;

[0164] Wherein, the first preset formula is:

[0165]

[0166] The second preset formula is:

[0167]

[0168] The third preset formula is:

[0169]

[0170] Among them, R1 is the comprehensive score of positive membership, R2 is the comprehensive score of negative membership, W is the weight of each evaluation factor, u i is the positive membership of the ith grid evaluation unit, vi is the negative membership of the ith grid evaluation unit, and S is the risk value of the grid evaluation unit.

[0171] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0172] Dividing the assessment area into at least two sub-assessment areas according to the topographical features of the assessment area;

[0173] The sub-evaluation areas are updated to the evaluation areas, so as to respectively evaluate the sub-evaluation areas.

[0174] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0175] Classifying the risk values ​​of each of the grid evaluation units using a natural breakpoint method to divide the evaluation area into risk areas with different risk levels;

[0176] The risk area includes at least one of an extremely high risk area, a high risk area, a medium risk area, a low risk area and an extremely low risk area.

[0177] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0178] Obtain the number of disaster points in the risk area from the geological disaster database;

[0179] Determine whether the number of disaster points matches the risk level of the risk area, and output the matching result.

[0180] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0181] Identifying potential active disaster points in the assessment area according to the deformation rate of the assessment area;

[0182] Determine whether the assessment area has the potential active disaster point according to the latest image of the assessment area;

[0183] If not, the potential active disaster point is eliminated;

[0184] Eliminate the potential active disaster sites that are historical active disaster sites;

[0185] The potential active disaster points that have not been eliminated are added to the geological disaster database.

[0186] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0187] Acquire ascending SAR images and descending SAR images of the assessment area collected during a preset duration;

[0188] The ascending SAR image and the descending SAR image are subjected to SBAS-InSAR processing to obtain the deformation rate of the evaluation area.

[0189] The ascending SAR image and the descending SAR image are SLC format data obtained by the Sentinel-1A satellite using an interferometric wide-band mode and a VV polarization mode.

[0190] Furthermore, the processor 1001 may call a program for geological disaster susceptibility assessment stored in the memory 1005, and further perform the following operations:

[0191] Preprocessing the ascending SAR image and the descending SAR image; wherein the preprocessing includes at least one of the following steps:

[0192] Using the precise orbit data to register the orbit-raising SAR image, and using the precise orbit data to register the orbit-descending SAR image, so as to correct the orbit errors of the orbit-raising SAR image and the orbit-descending SAR image;

[0193] If the ascending SAR image and / or the descending SAR image does not completely cover the assessment area, multiple ascending SAR images of the assessment area acquired at the same time are stitched together to obtain the ascending SAR image that completely covers the assessment area, and / or multiple descending SAR images of the assessment area acquired at the same time are stitched together to obtain the descending SAR image that completely covers the assessment area;

[0194] If there is a non-evaluation area in the ascending SAR image and / or the descending SAR image, the ascending SAR image is cropped to obtain the ascending SAR image containing only the evaluation area, and / or the descending SAR image is cropped to obtain the descending SAR image containing only the evaluation area.

[0195] In addition, to achieve the above-mentioned purpose, the present application also provides a terminal device, which includes: a memory, a processor, and a program for geological hazard susceptibility assessment stored on the memory and executable on the processor, and the program for geological hazard susceptibility assessment, when executed by the processor, implements the steps of the method for geological hazard susceptibility assessment as described above.

[0196] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, on which is stored a program for geological hazard susceptibility assessment, and when the program for geological hazard susceptibility assessment is executed by a processor, the steps of the method for geological hazard susceptibility assessment as described above are implemented.

[0197] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0198] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0199] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a computer, mobile phone, tablet computer) to execute the methods described in each embodiment of the present application.

[0200] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for assessing the susceptibility of geological hazards, characterized in that: include: Divide the assessment area into multiple grid assessment units; Acquiring an evaluation factor and a deformation rate of the evaluation area, wherein the evaluation factors of the grid evaluation units in the evaluation area are the same; According to the relationship strength between the evaluation factors in the evaluation area, eliminating the corresponding evaluation factors in the evaluation area; Determine the weight of each evaluation factor in the evaluation area according to the entropy weight method; Determine the positive membership and negative membership of each grid evaluation unit in the evaluation area according to the deformation rate of each grid evaluation unit in the evaluation area and the proportion of the evaluation factor by a bi-extreme fuzzy set algorithm; The risk value of each grid evaluation unit is determined according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit.

2. The method according to claim 1, characterized in that The step of eliminating the corresponding evaluation factors according to the relationship strength between the factors to be evaluated comprises: Determine the absolute value of the Pearson correlation coefficient between the evaluation factors; Eliminate one of the evaluation factors whose absolute value of the Pearson correlation coefficient is greater than a first preset threshold; Performing multicollinearity analysis on the evaluation factors to determine the VIF values ​​and TOL values ​​between the evaluation factors; Eliminate one of the evaluation factors whose VIF value is greater than the second preset threshold and whose TOL value is less than the third preset threshold; Among them, the first preset threshold is 0.5, the second preset threshold is 5, and the third preset threshold is 0.

2.

3. The method according to claim 1, characterized in that The step of determining the risk value of each grid evaluation unit according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit comprises: Substituting the weight of the evaluation factor and the positive membership of the grid evaluation unit into a first preset formula to obtain a comprehensive score of the positive membership of the grid evaluation unit, and substituting the weight of the evaluation factor and the negative membership of the grid evaluation unit into a second preset formula to obtain a comprehensive score of the negative membership of the grid evaluation unit; Substituting the comprehensive score of the positive membership and the comprehensive score of the negative membership of the grid evaluation unit into a third preset formula to obtain the risk value of the grid evaluation unit; Wherein, the first preset formula is: ； The second preset formula is: ； The third preset formula is: ； Among them, R1 is the comprehensive score of positive membership, R2 is the comprehensive score of negative membership, W is the weight of each evaluation factor, u i is the positive membership of the ith grid evaluation unit, vi is the negative membership of the ith grid evaluation unit, and S is the risk value of the grid evaluation unit.

4. The method according to claim 1, characterized in that: The step of dividing the evaluation area into a plurality of grid evaluation units includes: Dividing the assessment area into at least two sub-assessment areas according to the topographical features of the assessment area; The sub-evaluation areas are updated to the evaluation areas, so as to respectively evaluate the sub-evaluation areas.

5. The method according to claim 1, characterized in that After the step of determining the risk value of each grid evaluation unit according to the weight of the evaluation factor, the positive membership and the negative membership of each grid evaluation unit, the method further comprises: Classifying the risk values ​​of each of the grid evaluation units using a natural breakpoint method to divide the evaluation area into risk areas with different risk levels; The risk area includes at least one of an extremely high risk area, a high risk area, a medium risk area, a low risk area and an extremely low risk area.

6. The method according to claim 5, characterized in that After the step of classifying the risk values ​​of the grid evaluation units by using the natural breakpoint method to divide the evaluation area into risk areas with different risk levels, the method further includes: Obtain the number of disaster points in the risk area from the geological disaster database; Determine whether the number of disaster points matches the risk level of the risk area, and output the matching result.

7. The method according to claim 6, characterized in that The step of obtaining the number of disaster points in the risk area from the geological disaster database also includes: Identifying potential active disaster points in the assessment area according to the deformation rate of the assessment area; Determine whether the assessment area has the potential active disaster point according to the latest image of the assessment area; If not, the potential active disaster point is eliminated; Eliminate the potential active disaster sites that are historical active disaster sites; The potential active disaster points that have not been eliminated are added to the geological disaster database.

8. The method according to claim 1, characterized in that The step of obtaining the deformation rate of the evaluation area comprises: Acquire ascending SAR images and descending SAR images of the assessment area collected during a preset duration; The ascending SAR image and the descending SAR image are subjected to SBAS-InSAR processing to obtain the deformation rate of the evaluation area.

9. The method according to claim 8, characterized in that The ascending SAR image and the descending SAR image are SLC format data obtained by the Sentinel-1A satellite using an interferometric wide-band mode and a VV polarization mode.

10. The method according to claim 8, characterized in that Before performing SBAS-InSAR processing on the ascending SAR image and the descending SAR image to obtain the deformation rate of the evaluation area, the method further includes: Preprocessing the ascending SAR image and the descending SAR image; wherein the preprocessing includes at least one of the following steps: Using the precise orbit data to register the orbit-raising SAR image, and using the precise orbit data to register the orbit-descending SAR image, so as to correct the orbit errors of the orbit-raising SAR image and the orbit-descending SAR image; If the ascending SAR image and / or the descending SAR image does not completely cover the assessment area, multiple ascending SAR images of the assessment area acquired at the same time are stitched together to obtain the ascending SAR image that completely covers the assessment area, and / or multiple descending SAR images of the assessment area acquired at the same time are stitched together to obtain the descending SAR image that completely covers the assessment area; If there is a non-evaluation area in the ascending SAR image and / or the descending SAR image, the ascending SAR image is cropped to obtain the ascending SAR image containing only the evaluation area, and / or the descending SAR image is cropped to obtain the descending SAR image containing only the evaluation area.

Images