Geological disaster risk evaluation method and device fused with ascending and descending track time sequence InSAR deformation

Abstract

The invention provides a geological disaster risk evaluation method and device fusing ascending and descending orbit time sequence InSAR deformation, and the method comprises the steps: carrying out the deformation inversion based on ascending and descending orbit time sequence InSAR data, obtaining an ascending orbit surface deformation quantity and a descending orbit surface deformation quantity, and obtaining a dynamic deformation factor corresponding to each grid in a research region after fusion; determining a multi-dimensional static environment factor corresponding to each grid in the research area based on the optical remote sensing data and the environment data; traversing each grid in the research area, and screening the dynamic deformation factors and the multi-dimensional static environment factors corresponding to the grids to determine a plurality of target evaluation factors corresponding to the grids; and based on the contribution degree of the target evaluation factor corresponding to each grid to the geological disaster event occurring in the research area, generating a geological disaster risk evaluation result corresponding to the research area. According to the method, geological disaster deformation can be represented more comprehensively and accurately, so that the geological disaster risk evaluation precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of risk assessment, and in particular to a method and device for assessing geological disaster risk by integrating ascending and descending orbit time-series InSAR deformation. Background Art

[0002] At present, the traditional geological hazard assessment method mainly relies on on-site investigation and empirical judgment of geological experts. However, this assessment method has many limitations. On the one hand, in the face of complex and changeable geological environments, some methods show weak adaptability and it is difficult to fully and accurately capture the interaction between various factors and their dynamic changes, thus affecting the comprehensiveness and foresight of the assessment. On the other hand, the difficulty of data acquisition and the relatively simple processing methods have become one of the bottlenecks restricting the performance improvement of the assessment method. Insufficient data, single processing methods, and strong subjectivity in the selection of evaluation factors often lead to reduced accuracy of the evaluation results, which in turn limits its practicality and guiding value in actual disaster prevention and mitigation. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method and device for geological hazard risk assessment integrating ascending and descending orbit time-series InSAR deformation, which can characterize geological hazard deformation more comprehensively and accurately to improve the accuracy of geological hazard risk assessment.

[0004] In a first aspect, the present invention provides a method for assessing geological hazards by integrating ascending and descending orbit time-series InSAR deformation, comprising: Obtain ascending and descending orbit time-series InSAR data corresponding to the study area, perform deformation inversion based on the ascending and descending orbit time-series InSAR data to obtain ascending orbit surface deformation variables and descending orbit surface deformation variables respectively, and fuse the ascending orbit surface deformation variables and descending orbit surface deformation variables to obtain the dynamic deformation factor corresponding to each grid in the study area; and, obtaining optical remote sensing data and environmental data corresponding to the study area, and determining the multidimensional static environmental factors corresponding to each grid in the study area based on the optical remote sensing data and environmental data; Traverse each grid in the study area, screen the dynamic deformation factors and multi-dimensional static environmental factors corresponding to the grid, and determine the multiple target evaluation factors corresponding to the grid; Based on the contribution of the target evaluation factor corresponding to each grid to the geological disaster events occurring in the study area, the geological disaster risk assessment results corresponding to the study area are generated.

[0005] In one implementation, the ascending orbit surface deformation variable and the descending orbit surface deformation variable are fused to obtain the dynamic deformation factor corresponding to each grid in the study area, including: For any pixel in each grid in the study area, the ascending orbit surface deformation variable and its corresponding satellite local incidence angle and satellite heading angle at the pixel, and the descending orbit surface deformation variable and its corresponding satellite local incidence angle and satellite heading angle at the pixel are respectively substituted into the predetermined three-dimensional vector relationship between the surface deformation variable and the surface deformation component to construct a vector equation group for the ascending and descending orbit joint three-dimensional deformation modeling; The surface deformation component at the pixel is solved by using the vector equation group; Based on the surface deformation component at each pixel, the dynamic deformation factor corresponding to each grid in the study area is determined.

[0006] In one embodiment, the expression of the vector equation group for the lifting rail combined with three-dimensional deformation modeling is as follows: ; in, is the pixel under the ascending orbit condition ( ) at the ascending orbit surface deformation, is the pixel under descending orbit condition ( ), is a pixel ( ) along the vertical surface deformation component, is a pixel ( ) along the north-south direction, is a pixel ( ) along the east-west direction, is the local incident angle under orbit raising conditions, is the satellite heading angle under orbit raising conditions, is the local incident angle under descending orbit conditions, is the satellite heading angle under descending orbit conditions, is the system deviation under orbit raising condition, is the system deviation under descending orbit condition.

[0007] In one embodiment, the multidimensional static environmental factors include one or more of terrain relief, soil type, land use type, vegetation coverage, stratum lithology, rainfall, distance from fault zone, distance from water system, and slope. The multidimensional static environmental factors corresponding to different grids are the same or different, and the multiple target evaluation factors corresponding to different grids are the same or different. The dynamic deformation factors and multidimensional static environmental factors corresponding to the grids are screened to determine the multiple target evaluation factors corresponding to the grids, including: Determine the correlation coefficient between any two factors in the dynamic deformation factor and the multidimensional static environmental factor corresponding to the grid, and screen the dynamic deformation factor and the multidimensional static environmental factor corresponding to the grid in descending order of the correlation coefficient to determine multiple target evaluation factors corresponding to the grid; Among them, the correlation coefficient is used to describe the degree of linear correlation between two factors.

[0008] In one embodiment, before generating a geological hazard risk assessment result corresponding to the study area based on the contribution of the target assessment factor corresponding to each grid to the geological hazard events occurring in the study area, the method includes: Perform the following operations for any factor in the target evaluation factor corresponding to each grid: Determine whether the target evaluation factor corresponding to each grid in the study area contains the factor, and determine the number of the first grid containing the factor in the study area based on the determination result; And, judging whether the target evaluation factor corresponding to the grid where the geological disaster event occurred in the study area includes the factor, and determining the number of the second grids where the geological disaster occurred in the study area and included the factor based on the judgment result; Based on the number of the first grid, the number of the second grid, the total number of grids occupied by geological disaster events in the study area, and the total number of grids contained in the study area, the contribution of this factor to the geological disaster events occurring in the study area is determined.

[0009] In one embodiment, based on the number of first grids, the number of second grids, the total number of grids occupied by geological disaster events in the study area, and the total number of grids included in the study area, the contribution of the factor to the geological disaster events occurring in the study area is determined, including: The contribution of this factor to geological disaster events occurring in the study area is determined according to the following formula: ; in, Factor The geological disasters occurred in the study area The degree of contribution is the number of the second grid, is the total number of grids occupied by geological disaster events in the study area, is the number of the first grid, The total number of rasters contained in the study area.

[0010] In one embodiment, based on the contribution of the target evaluation factor corresponding to each grid to the geological disaster events occurring in the study area, a geological disaster risk evaluation result corresponding to the study area is generated, including: Calculate the sum of the contribution of the target evaluation factors corresponding to each grid to the geological disaster events occurring in the study area to obtain the comprehensive contribution degree, and generate the geological disaster risk assessment results corresponding to the study area based on the comprehensive contribution degree; Among them, the geological hazard risk assessment results are used to describe the susceptibility of geological disasters in the study area, and the comprehensive contribution degree is positively correlated with the susceptibility of geological disasters.

[0011] In a second aspect, the present invention further provides a geological disaster risk assessment device integrating ascending and descending orbit time-series InSAR deformation, comprising: The dynamic factor determination module is used to obtain the ascending and descending orbit time-series InSAR data corresponding to the study area, perform deformation inversion based on the ascending and descending orbit time-series InSAR data to obtain the ascending orbit surface deformation variable and the descending orbit surface deformation variable respectively, and fuse the ascending orbit surface deformation variable and the descending orbit surface deformation variable to obtain the dynamic deformation factor corresponding to each grid in the study area; A static factor determination module is used to obtain optical remote sensing data and environmental data corresponding to the study area, and determine the multidimensional static environmental factors corresponding to each grid in the study area based on the optical remote sensing data and environmental data; The factor screening module is used to traverse each grid in the study area and screen the dynamic deformation factors and multidimensional static environmental factors corresponding to the grid to determine multiple target evaluation factors corresponding to the grid; The hazard assessment module is used to generate the geological hazard assessment results corresponding to the study area based on the contribution of the target evaluation factor corresponding to each grid to the geological disaster events occurring in the study area.

[0012] In a third aspect, the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement any one of the methods provided in the first aspect.

[0013] In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0014] The present invention provides a geological hazard risk assessment method and device integrating ascending and descending orbit time-series InSAR deformation. First, ascending and descending orbit time-series InSAR data corresponding to a study area are obtained, deformation inversion is performed based on the ascending and descending orbit time-series InSAR data to respectively obtain ascending orbit surface deformation variables and descending orbit surface deformation variables, and the ascending orbit surface deformation variables and the descending orbit surface deformation variables are fused to obtain a dynamic deformation factor corresponding to each grid in the study area; and optical remote sensing data and environmental data corresponding to the study area are obtained, and multidimensional static environmental factors corresponding to each grid in the study area are determined based on the optical remote sensing data and the environmental data; then, each grid in the study area is traversed, and the dynamic deformation factors and multidimensional static environmental factors corresponding to the grid are screened to determine multiple target evaluation factors corresponding to the grid; finally, based on the contribution of the target evaluation factor corresponding to each grid to the geological hazard events occurring in the study area, a geological hazard risk assessment result corresponding to the study area is generated. The above method proposes to jointly solve the ascending and descending orbit time-series InSAR data, which can characterize the geological hazard deformation more comprehensively and accurately; at the same time, the target evaluation factor corresponding to each grid is screened out from the dynamic deformation factor and the multidimensional static environmental factor, and the geological hazard risk assessment is carried out according to the contribution of the target evaluation factor to the geological hazard events occurring in the study area, which can further improve the accuracy of geological hazard risk assessment.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic flow chart of a method for evaluating geological hazards by integrating ascending and descending orbit time-series InSAR deformation provided by an embodiment of the present invention; Figure 2 A technical framework diagram of a geological disaster risk assessment method integrating ascending and descending orbit time-series InSAR deformation provided by an embodiment of the present invention; Figure 3 A schematic diagram of a SAR side-view imaging geometry (taking orbit-raising imaging as an example) and angle parameters in a three-dimensional coordinate system provided by an embodiment of the present invention; Figure 4 A geometric plane projection relationship and angle parameters of an ascending and descending orbit test imaging provided by an embodiment of the present invention; Figure 5 A schematic diagram of the structure of a geological disaster risk assessment device integrating ascending and descending orbit time-series InSAR deformation provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are 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.

[0020] At present, traditional geological hazard assessment methods mostly rely on the experience of geological experts, or are based on limited geological survey data, historical disaster records, remote sensing images and other information for analysis. There are problems such as strong subjectivity, single data, and low evaluation accuracy. In recent years, with the development of remote sensing technology, InSAR technology has been widely used in geological hazard monitoring and hazard assessment. However, in terms of evaluation factors, in addition to traditional static evaluation factors, existing hazard assessment methods mostly use single-track InSAR deformation results as dynamic deformation features. In terms of evaluation models, although methods such as logistic regression, fuzzy and gray clustering, and GIS-assisted models have been used to a certain extent, due to the large subjectivity in the selection and weight distribution of evaluation factors, and the lack of effective accuracy verification methods, the accuracy and reliability of the evaluation results need to be improved.

[0021] Based on this, the present invention provides a method and device for geological hazard assessment that integrates ascending and descending orbit time-series InSAR deformation, which can characterize geological hazard deformation more comprehensively and accurately to improve the accuracy of geological hazard assessment.

[0022] To facilitate understanding of this embodiment, a geological disaster risk assessment method integrating ascending and descending orbit time series InSAR deformation disclosed in an embodiment of the present invention is first described in detail. Figure 1 The flowchart of a method for evaluating geological hazards by integrating the time-series InSAR deformation of ascending and descending orbits is shown. The method mainly includes the following steps S102 to S108: Step S102: Obtain ascending and descending orbit time-series InSAR data corresponding to the study area, perform deformation inversion based on the ascending and descending orbit time-series InSAR data to obtain ascending orbit surface deformation variables and descending orbit surface deformation variables respectively, and fuse the ascending orbit surface deformation variables and the descending orbit surface deformation variables to obtain the dynamic deformation factor corresponding to each grid in the study area.

[0023] Among them, the ascending and descending orbit time-series InSAR data are also referred to as the ascending orbit time-series InSAR data and the descending orbit time-series InSAR data; the ascending orbit surface shape variable is also referred to as the surface shape variable obtained by deformation inversion of the ascending orbit time-series InSAR data, the descending orbit surface shape variable is also referred to as the surface shape variable obtained by deformation inversion of the descending orbit time-series InSAR data, the ascending orbit surface shape variable and the descending orbit surface shape variable can also be referred to as LOS (Line-of-Sight) deformation detection results; the dynamic deformation factors include surface deformation components along the east-west direction, along the north-south direction, and along the vertical direction; the above-mentioned ascending orbit surface shape variable, descending orbit surface shape variable, and surface deformation components are all time-series data.

[0024] In one example, first, the ascending orbit time-series InSAR data and the descending orbit time-series InSAR data in the study area are obtained; then, the SABA-InSAR (Small Baseline Subset InSAR) technology is used to perform deformation inversion on the ascending orbit time-series InSAR data and the descending orbit time-series InSAR data, respectively, to obtain the corresponding ascending orbit surface deformation variables and descending orbit surface deformation variables; finally, the three-dimensional vector relationship between the surface deformation variables and the surface deformation components is analyzed, and the ascending orbit surface deformation variables and the descending orbit surface deformation variables are respectively substituted into the three-dimensional vector relationship to perform three-dimensional deformation solution of the ascending and descending orbit time-series InSAR data fusion, and the surface deformation components along the east-west direction, the north-south direction, and the vertical direction are obtained.

[0025] Step S104, obtaining optical remote sensing data and environmental data corresponding to the study area, and determining the multidimensional static environmental factors corresponding to each grid in the study area based on the optical remote sensing data and environmental data.

[0026] Among them, the optical remote sensing data can be Landsat 8 optical remote sensing data, and the environmental data can include but are not limited to DEM (Digital Elevation Model), stratum lithology, fault zone, land use type, geological structure, soil type, rainfall, water system, road, etc. In one example, after obtaining the optical remote sensing data and environmental data, they are pre-processed by denoising, filtering, geo-registration, etc., and then the multidimensional static environmental factors corresponding to each grid are determined according to the pre-processed optical remote sensing data and environmental data. The multidimensional static environmental factors include one or more of terrain relief, soil type, land use type, vegetation coverage, stratum lithology, rainfall, distance from fault zone, distance from water system, and slope. It should be noted that the multidimensional static environmental factors corresponding to different grids are the same or different. For example, a grid where no rainfall occurs will not contain the static environmental factor of rainfall, and a grid where no water body exists will not contain the static environmental factor of distance from water system.

[0027] Step S106, traverse each grid in the study area, and screen the dynamic deformation factors and multi-dimensional static environmental factors corresponding to the grid to determine multiple target evaluation factors corresponding to the grid.

[0028] Among them, the multiple target evaluation factors corresponding to different grids are the same or different. In one example, for each grid in the study area, the correlation coefficient between any two factors in the dynamic deformation factor and multidimensional static environmental factor corresponding to the grid can be analyzed, and the multiple target evaluation factors corresponding to the grid factor can be screened out according to the correlation coefficient.

[0029] Step S108, generating a geological hazard risk assessment result corresponding to the study area based on the contribution of the target assessment factor corresponding to each grid to the geological hazard events occurring in the study area.

[0030] Among them, the geological hazard risk assessment results are used to describe the susceptibility of geological disasters in the study area. The susceptibility of geological disasters is positively correlated with the comprehensive contribution degree, which is the sum of the contribution degrees of the target evaluation factors corresponding to each grid to the geological disaster events occurring in the study area.

[0031] In one example, for any factor in the target evaluation factor corresponding to each grid, the number of first grids containing the factor in the study area, the number of second grids containing the factor in the study area where geological disasters occur, the total number of grids occupied by geological disaster events in the study area, and the total number of grids contained in the study area can be determined, and then the contribution of the factor to the geological disaster events occurring in the study area can be evaluated based on the above quantities; the sum of the contribution of all factors to the geological disaster events occurring in the study area can be calculated to obtain the geological hazard risk assessment result corresponding to the study area.

[0032] The geological hazard risk assessment method integrating ascending and descending orbit time-series InSAR deformation provided in the embodiment of the present invention proposes to jointly solve the ascending and descending orbit time-series InSAR data, which can characterize the geological hazard deformation more comprehensively and accurately; at the same time, the target evaluation factor corresponding to each grid is screened out from the dynamic deformation factor and the multi-dimensional static environmental factor, and the geological hazard risk assessment is performed according to the contribution degree of the target evaluation factor to the geological hazard events occurring in the study area, which can further improve the accuracy of geological hazard risk assessment.

[0033] For ease of understanding, the present invention provides a specific implementation of a method for evaluating geological hazards by integrating the time-series InSAR deformation of ascending and descending orbits. Figure 2 The technical framework diagram of a geological hazard assessment method integrating ascending and descending orbit time series InSAR deformation is shown, including: (I) Data collection: the collected data include ascending and descending orbit time-series InSAR data, Landsat 8 optical remote sensing data, DEM, stratigraphic lithology, fault zones, land use type, geological structure, soil type, rainfall, water system, and roads. The ascending and descending orbit time-series InSAR data may be ascending and descending orbit Sentinel-1A data.

[0034] Furthermore, the collected data are preprocessed, including denoising, filtering, and georeferencing, to ensure the quality and consistency of the data.

[0035] (II) Ascending and descending orbit time series InSAR deformation inversion and deformation fusion.

[0036] (2.1) The deformation inversion of ascending and descending orbits in time series InSAR includes data preprocessing, differential interferometry calculation, deformation estimation in time and space domains, and deformation inversion.

[0037] In the specific implementation, the process of the ascending and descending track time-series InSAR deformation inversion of the embodiment of the present invention adopts SBAS-InSAR technology. SBAS-InSAR technology groups all SAR images covering the same area into several subsets, the image baseline distance (including time baseline distance and space baseline distance) within the subset is small, and the baseline distance between the subsets is large, and one of the subsets can be understood as an interference image pair. After such a simple and effective merger, all available small baseline interference images are obtained. This merger is based on the minimum deformation rate standard, and this minimum deformation rate is easily obtained using the singular value decomposition (SVD) method. SBAS-InSAR technology limits the geometric decoherence caused by the long baseline, and allows more SAR images to participate in the deformation calculation, increasing the sampling in time. The SBAS-InSAR technology is proposed to solve the large-scale deformation phenomenon under low resolution. The unwrapped phase map of the subsequent phase unwrapping operation based on the multi-view differential interference image set is used to analyze the surface deformation to reduce the influence of time and space decoherence factors, and the average deformation speed of the monitoring area can be obtained.

[0038] Assume existence Scene SAR images, according to the imaging time sequence, set the general resolution unit At the moment of imaging hour The cumulative deformation phase is , if the critical baseline condition is met, then there exists Different pairs of interference images can be generated Differential interference image: Formula 1: ; The deformation phase corresponding to any resolution unit on the interference image can be obtained by the cumulative deformation phase of the main and auxiliary images that generate the interference image in the observation sequence Indicates that: Formula 2: ; ;in, General resolution unit At the moment of imaging The accumulated deformation phase at General resolution unit At the moment of imaging The accumulated deformation phase at .

[0039] Then the relationship between the cumulative deformation phase and the interference phase sequence is: Formula 3: ; make: , ; Then we can get: Formula 4: ; Formula 5: ; Formula 6: ; If all SAR images belong to a short baseline set, the deformation phase can be obtained by least squares ; Formula 7: ; But in reality, this possibility is small. For multiple short baseline sets, is a singular matrix. Using the singular value decomposition (SVD) method, we can solve The generalized inverse matrix of : Formula 8: ; in: , the diagonal elements of G are singular values , and are the left and right singular vector matrices of the corresponding matrix, For the matrix The rank of . Then the least squares phase estimate in the sense of minimum norm is: Formula 9: ;, Assuming the deformation phase at the initial moment is 0, we can get The cumulative deformation is: Formula 10: ; Convert phase to average phase velocity ,Right now: Formula 11: ; Substituting the phase in Equation 2, we get: Formula 12: ; ; This results in a new matrix equation: Formula 13: ; in, is a The matrix of rows, columns between the acquisition times of the primary and secondary images, ,other , in this case, applying the SVD decomposition to the matrix , we can get the velocity vector The minimum norm solution of .

[0040] Taking into account the surface deformation, elevation error, atmospheric influence and loss of correlation, the differential interference phase composition of each coherent point on each differential interferogram can be expressed as follows: Formula 14: ;in, , is the residual terrain phase caused by the inaccurate DEM data used, where is the vertical baseline, is the radar wavelength, is the distance from the satellite to the earth's surface (slant range), is the radar incident angle, is the elevation error; , is the time baseline of the interference pattern, is the average deformation rate of the surface during this period of time, It is the non-deformation phase; is the orbit error phase introduced in the process of de-flattening the Earth phase under inaccurate orbit parameters; and are the phases introduced by atmosphere and noise, respectively.

[0041] Based on the composition of differential interferometry phase, the deformation rate and elevation error are established The equation model is: Formula 15: ;in, is the coefficient matrix associated with the spatial baseline, is the differential interference phase, from which the DEM error can be obtained.

[0042] In addition, based on the linear model, the atmospheric phase and the nonlinear deformation phase can be separated by continuing to filter the residual phase in space and time.

[0043] By superimposing the nonlinear deformation phase onto the linear deformation phase, the total surface deformation phase can be obtained. : Formula 16: ; Then, according to the following formula, the final surface shape variable is obtained: : Formula 17: .

[0044] In specific implementation, the deformation inversion can be performed on the ascending orbit time-series InSAR data and the descending orbit time-series InSAR data respectively according to the above process, so as to obtain the ascending orbit surface deformation variable and the descending orbit surface deformation variable respectively.

[0045] (2.2) The three-dimensional deformation solution process of ascending and descending orbit time series InSAR deformation fusion is as follows: First, the three-dimensional vector relationship between the surface deformation variable and the surface deformation component: In actual implementation, synthetic aperture radar obtains images through the principle of side-view imaging. Figure 3 The figure shows a schematic diagram of a SAR side-view imaging geometry (taking orbit-raising imaging as an example) and angle parameters in a three-dimensional coordinate system. The subsatellite point of the satellite at the time of imaging is taken as the coordinate origin O, and the spatial rectangular coordinate frame is constructed based on the east, north and vertical directions. S is the satellite position at the time of imaging, P is the ground target, the line between S and P is the radar sighting (LOS) direction, the angle θ between the LOS direction and the vertical direction is the radar signal incident angle, the subsatellite point trajectory is the projection of the satellite orbit on the horizontal plane; φ is the satellite heading angle; the line between the subsatellite point O and the target point P is the projection of the LOS direction on the horizontal plane.

[0046] Depend on Figure 3 It can be seen that the LOS deformation variable of point P (expressed by D) is the sum of the projections of the three-dimensional displacement components of the store in the LOS direction, that is: Formula 18: ; In the formula, is the deformation component of point P along the east-west direction; is the deformation component of point P along the north-south direction; is the deformation component of point P along the vertical direction.

[0047] In the process of solving the deformation phase by differential interferometry phase, the phase unwrapping technology must be applied to solve the integer ambiguity number of the phase difference. The current phase unwrapping technology generally adopts the integration of the phase gradient of the adjacent pixel points in the entire image space according to certain principles. However, this process is very different from the integer ambiguity solution in the GPS (Global Positioning System) system. GPS technology realizes the solution of the integer ambiguity number of the range phase based on the time series modeling of the redundant observation results of the target point, while in InSAR technology, the phase observation value sequence of the same target point is a discrete value obtained by the periodic return visit of the satellite. The phase integer number is obtained by the integral operation of the phase gradient based on a specific reference point in the image space. In the absence of prior information, the deformation of the reference point is generally assumed to be 0. After spatial convolution processing, the relative change between each pixel point is accurately solved, but the integer ambiguity number of the real range phase cannot be accurately solved. This ultimately leads to a systematic deviation between the LOS deformation (that is, the surface deformation R) of the entire monitoring area and the real spatial displacement D, which is assumed to be K, that is, D=R+K. The functional relationship between the final LOS deformation and the real space displacement (that is, the three-dimensional vector relationship between the surface deformation and the surface deformation component) is: Formula 19: .

[0048] Then, the ascending orbit surface deformation variables and the descending orbit surface deformation variables are fused to obtain the dynamic deformation factor corresponding to each grid in the study area, including: for any pixel in each grid in the study area, the ascending orbit surface deformation variable and its corresponding satellite local incidence angle, satellite heading angle at the pixel, and the descending orbit surface deformation variable and its corresponding satellite local incidence angle, satellite heading angle at the pixel are respectively substituted into the predetermined three-dimensional vector relationship between the surface deformation variable and the surface deformation component to construct a vector equation group for the ascending and descending orbit joint three-dimensional deformation modeling; the vector equation group is used to solve the surface deformation component at the pixel (specifically: the surface deformation component along the east-west direction and the vertical surface deformation component ); Based on the surface deformation component at each pixel, determine the dynamic deformation factor corresponding to each grid in the study area, that is, the surface deformation component along the east-west direction and the vertical surface deformation component As the dynamic deformation factor corresponding to the grid.

[0049] In actual implementation, most of the current spaceborne SAR systems adopt the working mode of cabinet flight and right-side imaging. On this basis, any sensor has the opportunity to image the common ground targets during the orbit ascending and descending flight. The imaging set relationship of the sensor platform in this process is as follows: Figure 4 As shown, Figure 4 It is a geometric plane projection relationship and angle parameters for ascending and descending orbit test imaging.

[0050] right Figure 4 The projection relationship of the ascending and descending orbit imaging geometry in the horizontal plane is analyzed in depth. It can be seen that although DInSAR in each working mode only monitors the deformation of LOS in one direction, based on the approximately opposite projection directions of LOS in the ascending and descending orbits in the plane, it can be seen that the deformation of LOS in the ascending and descending orbits can be detected in the spatial geometric framework (such as Figure 3 More spatial vector information can be extracted from the image, which creates favorable conditions for the three-dimensional reconstruction of the real surface displacement.

[0051] According to the three-dimensional vector relationship of SAR interferometry established in formula 19, the local incident angles under ascending and descending orbit conditions are respectively substituted , Satellite heading angle , and surface shape variables , we can get the expression of the vector equation group for the joint three-dimensional deformation modeling of the lifting and lowering orbit: Formula 20: ; in,( ) is the row and column number of the target in the image space, For the common ground target in the pixel ( ) at the ascending orbit surface deformation, For the common ground target in the pixel ( ), is the pixel ( ) along the vertical surface deformation component, is a pixel ( ) along the north-south direction, is a pixel ( ) along the east-west direction, is the sensor in the pixel under the condition of orbit raising ( ), is the satellite heading angle under orbit raising conditions, The sensor is in the pixel ( ), is the satellite heading angle under descending orbit conditions, is the system deviation under orbit raising condition, is the system deviation under descending orbit conditions. Among them, the local incidence angle can generally be obtained by interpolating the SAR image header file parameters (with the column number The satellite heading angle remains basically unchanged during the imaging process of a single SAR image.

[0052] (III) Screening target evaluation factors based on Pearson correlation coefficient: Determine the correlation coefficient between any two factors among the dynamic deformation factors and multidimensional static environmental factors corresponding to the grid, and screen the dynamic deformation factors and multidimensional static environmental factors corresponding to the grid in descending order of the correlation coefficient to determine multiple target evaluation factors corresponding to the grid; wherein the correlation coefficient is used to describe the degree of linear correlation between two factors.

[0053] In one example, the Pearson correlation coefficient method can be used to screen multidimensional static environmental factors and dynamic deformation factors to obtain the target evaluation factors that ultimately participate in the hazard assessment corresponding to each grid.

[0054] Considering the objectivity of the selection of geological disaster risk assessment indicators and the independence among various factors, in order to improve the accuracy of model evaluation, it is necessary to evaluate the correlation among various factors. The embodiment of the present invention adopts the Pearson correlation coefficient method to quantify the correlation among factors. Through the multivariate analysis module of the GIS (Geographic Information System) spatial analysis tool, statistical analysis of the band values of the classified factors is carried out, and their covariance matrix is calculated for detailed correlation analysis. The Pearson correlation coefficient r is a statistical indicator used to measure the strength of the linear relationship between two continuous factors. It measures the degree of linear association between two factors, and its value ranges from -1 to 1. The greater the absolute value of r, the stronger the linear correlation between the two factors. r = 1 indicates a perfect positive correlation, that is, the two factors increase linearly as they increase; r = -1 indicates a perfect negative correlation, that is, one factor decreases linearly as the other factor increases; r = 0 indicates no linear relationship, but it does not mean that there is no other relationship between the two factors; 0 < r < 1 indicates a positive correlation, that is, when one factor increases, the other factor also tends to increase, but not necessarily linearly; -1 < r < 0 indicates a negative correlation, that is, when one factor increases, the other factor tends to decrease, but not necessarily linearly.

[0055] There are currently two factor sequences of length n and , then the calculation formula for the correlation coefficient r is: Formula 21: ; where n is the number of samples, and are the means of the samples within the two factor sequences respectively, is the th sample value of . In the embodiment of the present invention, in the judgment of the level of correlation, the absolute value of r is used as a limit. When 0.8 < |r| ≤ 1, the correlation among influencing factors is extremely high; when 0.6 < |r| ≤ 0.8, the correlation among influencing factors is high; when 0.4 < |r| ≤ 0.6, the correlation among influencing factors is medium; when 0.2 < |r| ≤ 0.4, the correlation among influencing factors is weak; when |r| ≤ 0.2, the correlation among influencing factors is extremely weak or uncorrelated.

[0056] (4) Geological disaster risk assessment: All the target assessment factors participating in the risk assessment are introduced into the information amount model for geological disaster risk assessment. Among them, the information amount model is a prediction method based on statistical analysis, which converts the measured values of each influencing factor into information amount values through statistical analysis. The information amount value is used to characterize the contribution degree of each influencing factor to the occurrence of disasters, and then to evaluate the risk of disasters.

[0057] The embodiment of the present invention provides a specific implementation method for determining the contribution of any factor to the geological disaster events occurring in the study area, and the following steps 1 to 3 are performed for any factor in the target evaluation factor corresponding to each grid: Step 1: Determine whether the target evaluation factor corresponding to each grid in the study area contains the factor, and determine the number of the first grid containing the factor in the study area based on the judgment result. ; Step 2: Determine whether the target evaluation factor corresponding to the grid where geological disasters occurred in the study area contains the factor, and determine the number of second grids that contain the factor and have geological disasters in the study area based on the judgment result. ; Step 3, based on the number of first grids , the second grid number , the total number of grids occupied by geological disaster events in the study area and the total number of rasters contained in the study area , determine the contribution of this factor to the geological disaster events occurring in the study area.

[0058] Specifically, the information model mainly uses the frequency or density of geological disasters to convert the factors ( ) on geological disaster events (Y) is converted into information entropy. The greater the information entropy, the greater the influence of the factor. Finally, the information entropy of each factor is spatially superimposed to achieve regional landslide hazard assessment. ) provides information on geological disaster events (Y) It can be expressed as: Formula 22: ; in, Factor The geological disasters occurred in the study area The degree of contribution is the number of the second grid, is the total number of grids occupied by geological disaster events in the study area, is the number of the first grid, The total number of rasters contained in the study area.

[0059] In an embodiment of the present invention, by selecting a certain number of evaluation factors in the study area, the information values ​​of different target evaluation factors are calculated respectively, and the comprehensive information is obtained by weighted superposition. In one embodiment, the sum of the contribution of the target evaluation factors corresponding to each grid to the geological disaster events occurring in the study area is calculated to obtain the comprehensive contribution degree, and the geological disaster risk assessment result corresponding to the study area is generated based on the comprehensive contribution degree; wherein the geological disaster risk assessment result is used to describe the geological disaster susceptibility of the study area, and the comprehensive contribution degree is positively correlated with the geological disaster susceptibility.

[0060] Specifically, the information content of the evaluation unit in the entire study area can be calculated based on the information content of a single indicator factor. The larger the information value, the higher the probability of geological disasters in the evaluation unit. The expression of the comprehensive information content (that is, the comprehensive contribution degree) of the evaluation unit is: ; Where: I represents the comprehensive information value provided by all factors of the evaluation unit, reflecting the degree of geological disaster susceptibility; n represents the number of factors.

[0061] (V) Verification of the accuracy of geological hazard risk assessment: The receiver operating characteristic curve (ROC) is used to verify the accuracy of geological hazard risk zoning.

[0062] In order to evaluate the impact of the results of time-series InSAR fusion on the accuracy of risk assessment results, the embodiment of the present invention uses the ROC curve to compare and analyze the information model and test its accuracy. The ROC curve is a tool for evaluating the performance of classification models, and the accuracy of the model partitioning results is evaluated by calculating the AUC (Area Under Curve) value.

[0063] ROC curve is the abbreviation of receiver operating characteristic curve, which combines the continuous variable indicators of sensitivity and specificity. In the geological hazard risk assessment, the horizontal axis of the ROC curve represents the cumulative area percentage from high to low information content, and the vertical axis represents the cumulative percentage of geological hazard points within the corresponding information content range. The AUC value is used to evaluate the accuracy of the model. It is generally believed that when the AUC is 0.5~0.7, the model accuracy is poor; when the AUC is 0.7~0.9, the model accuracy is good; when the AUC is above 0.9, the model accuracy is excellent; the closer the AUC is to 1, the higher the model accuracy. It has been experimentally verified that the geological hazard risk assessment method that integrates the ascending and descending orbit time series InSAR deformation provided by the embodiment of the present invention has high accuracy.

[0064] In summary, the geological disaster risk assessment method integrating ascending and descending orbit time-series InSAR deformation provided by the embodiment of the present invention fuses the ascending and descending orbit time-series InSAR deformation monitoring results, and then uses the fused deformation information as a dynamic evaluation factor, and uses the Pearson correlation coefficient method to screen out the final risk assessment factor, and then integrates the evaluation factor into the information volume model to obtain the final geological disaster risk assessment partition, and finally uses the ROC curve to verify the accuracy of the risk assessment. The embodiment of the present invention can effectively solve the problem that single orbit (ascending or descending) data cannot fully reflect the real deformation of geological disasters due to the different satellite orbit directions and observation angles; as well as the problems of evaluation factor screening and weight distribution, and has good applicability, and can provide technical support for the fields of geological disaster risk assessment and disaster emergency response. That is, the embodiment of the present invention has at least the following characteristics: (1) The embodiment of the present invention proposes to jointly solve the ascending and descending orbit time series InSAR deformation data to solve the problem that a single orbit deformation cannot fully and accurately characterize the geological disaster deformation; (2) The embodiment of the present invention uses the Pearson correlation coefficient method to screen the geological hazard risk assessment factors, optimizes and screens the strongly correlated assessment factors, and improves the accuracy of the risk assessment model.

[0065] Based on the above-mentioned embodiments, the present invention provides a geological disaster risk assessment device integrating the time-series InSAR deformation of the ascending and descending orbits, see Figure 5 The schematic diagram of the structure of a geological disaster risk assessment device integrating ascending and descending orbit time series InSAR deformation is shown, and the device mainly includes the following parts: The dynamic factor determination module 502 is used to obtain ascending and descending orbit time-series InSAR data corresponding to the study area, perform deformation inversion based on the ascending and descending orbit time-series InSAR data to obtain ascending orbit surface deformation variables and descending orbit surface deformation variables, and fuse the ascending orbit surface deformation variables and descending orbit surface deformation variables to obtain the dynamic deformation factor corresponding to each grid in the study area; The static factor determination module 504 is used to obtain optical remote sensing data and environmental data corresponding to the study area, and determine the multidimensional static environmental factor corresponding to each grid in the study area based on the optical remote sensing data and environmental data; The factor screening module 506 is used to traverse each grid in the study area and screen the dynamic deformation factor and multi-dimensional static environmental factor corresponding to the grid to determine multiple target evaluation factors corresponding to the grid; The risk assessment module 508 is used to generate a geological hazard risk assessment result corresponding to the study area based on the contribution of the target assessment factor corresponding to each grid to the geological hazard events occurring in the study area.

[0066] The geological hazard risk assessment device integrating ascending and descending orbit time-series InSAR deformation provided in the embodiment of the present invention proposes to jointly solve the ascending and descending orbit time-series InSAR data, which can characterize the geological hazard deformation more comprehensively and accurately; at the same time, the target evaluation factor corresponding to each grid is screened out from the dynamic deformation factor and the multi-dimensional static environmental factor, and the geological hazard risk assessment is performed according to the contribution degree of the target evaluation factor to the geological hazard events occurring in the study area, which can further improve the accuracy of the geological hazard risk assessment.

[0067] In one implementation, the dynamic factor determination module 502 is specifically configured to: For any pixel in each grid in the study area, the ascending orbit surface deformation variable and its corresponding satellite local incidence angle and satellite heading angle at the pixel, and the descending orbit surface deformation variable and its corresponding satellite local incidence angle and satellite heading angle at the pixel are respectively substituted into the predetermined three-dimensional vector relationship between the surface deformation variable and the surface deformation component to construct a vector equation group for the ascending and descending orbit joint three-dimensional deformation modeling; The surface deformation component at the pixel is solved by using the vector equation group; Based on the surface deformation component at each pixel, the dynamic deformation factor corresponding to each grid in the study area is determined.

[0068] In one embodiment, the expression of the vector equation group for the lifting rail combined with three-dimensional deformation modeling is as follows: ; in, is the pixel under the ascending orbit condition ( ) at the ascending orbit surface deformation, is the pixel under descending orbit condition ( ), is a pixel ( ) along the east-west direction, is a pixel ( ) along the north-south direction, is a pixel ( ) along the vertical surface deformation component, is the local incident angle under orbit raising conditions, is the satellite heading angle under orbit raising conditions, is the local incident angle under descending orbit conditions, is the satellite heading angle under descending orbit conditions, is the system deviation under orbit raising condition, is the system deviation under descending orbit condition.

[0069] In one embodiment, the multidimensional static environmental factors include one or more of terrain relief, soil type, land use type, vegetation coverage, stratum lithology, rainfall, distance from fault zone, distance from water system, and slope. The multidimensional static environmental factors corresponding to different grids are the same or different, and the multiple target evaluation factors corresponding to different grids are the same or different. The factor screening module 506 is specifically used to: Determine the correlation coefficient between any two factors in the dynamic deformation factor and the multidimensional static environmental factor corresponding to the grid, and screen the dynamic deformation factor and the multidimensional static environmental factor corresponding to the grid in descending order of the correlation coefficient to determine multiple target evaluation factors corresponding to the grid; Among them, the correlation coefficient is used to describe the degree of linear correlation between two factors.

[0070] In one embodiment, a contribution evaluation module is further included, which is used to: Perform the following operations for any factor in the target evaluation factor corresponding to each grid: Determine whether the target evaluation factor corresponding to each grid in the study area contains the factor, and determine the number of the first grid containing the factor in the study area based on the determination result; And, judging whether the target evaluation factor corresponding to the grid where the geological disaster event occurred in the study area includes the factor, and determining the number of the second grids where the geological disaster occurred in the study area and included the factor based on the judgment result; Based on the number of the first grid, the number of the second grid, the total number of grids occupied by geological disaster events in the study area, and the total number of grids contained in the study area, the contribution of this factor to the geological disaster events occurring in the study area is determined.

[0071] In one embodiment, the contribution evaluation module is specifically used to: The contribution of this factor to geological disaster events occurring in the study area is determined according to the following formula: ; in, Factor The geological disasters occurred in the study area The degree of contribution is the number of the second grid, is the total number of grids occupied by geological disaster events in the study area, is the number of the first grid, The total number of rasters contained in the study area.

[0072] In one embodiment, the risk assessment module 508 is specifically used to: Calculate the sum of the contribution of the target evaluation factors corresponding to each grid to the geological disaster events occurring in the study area to obtain the comprehensive contribution degree, and generate the geological disaster risk assessment results corresponding to the study area based on the comprehensive contribution degree; Among them, the geological hazard risk assessment results are used to describe the susceptibility of geological disasters in the study area, and the comprehensive contribution degree is positively correlated with the susceptibility of geological disasters.

[0073] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0074] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0075] Figure 6 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0076] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0077] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0078] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0079] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0080] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0081] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0082] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A geological hazard risk assessment method integrating ascending and descending orbit time series InSAR deformation, characterized in that: include: Acquire ascending and descending orbit time-series InSAR data corresponding to the study area, perform deformation inversion based on the ascending and descending orbit time-series InSAR data to obtain ascending orbit surface deformation variables and descending orbit surface deformation variables, respectively, and fuse the ascending orbit surface deformation variables and the descending orbit surface deformation variables to obtain a dynamic deformation factor corresponding to each grid in the study area; And, obtaining optical remote sensing data and environmental data corresponding to the study area, and determining the multidimensional static environmental factor corresponding to each grid in the study area based on the optical remote sensing data and the environmental data; Traversing each of the grids in the study area, screening the dynamic deformation factors and the multi-dimensional static environmental factors corresponding to the grids to determine a plurality of target evaluation factors corresponding to the grids; Based on the contribution of the target evaluation factor corresponding to each grid to the geological disaster events occurring in the study area, a geological disaster risk evaluation result corresponding to the study area is generated.

2. The geological disaster risk assessment method integrating ascending and descending orbit time series InSAR deformation according to claim 1 is characterized in that: The ascending orbit surface deformation variable and the descending orbit surface deformation variable are fused to obtain the dynamic deformation factor corresponding to each grid in the study area, including: For any pixel in each grid in the study area, the ascending orbit surface deformation amount and its corresponding satellite local incidence angle and satellite heading angle at the pixel, and the descending orbit surface deformation amount and its corresponding satellite local incidence angle and satellite heading angle at the pixel are respectively substituted into the predetermined three-dimensional vector relationship between the surface deformation amount and the surface deformation component to construct a vector equation group for ascending and descending orbit joint three-dimensional deformation modeling; Solving the surface deformation component at the pixel by using the vector equation group; Based on the surface deformation component at each pixel, the dynamic deformation factor corresponding to each grid in the study area is determined.

3. The geological disaster risk assessment method integrating ascending and descending orbit time series InSAR deformation according to claim 2 is characterized in that: The expression of the vector equation group for the lifting rail combined with three-dimensional deformation modeling is as follows: ； in, is the pixel under the ascending orbit condition ( ) at the ascending orbit surface deformation, is the pixel under descending orbit condition ( ), is a pixel ( ) along the vertical surface deformation component, is a pixel ( ) along the north-south direction, is a pixel ( ) along the east-west direction, is the local incident angle under orbit raising conditions, is the satellite heading angle under orbit raising conditions, is the local incident angle under descending orbit conditions, is the satellite heading angle under descending orbit conditions, is the system deviation under orbit raising condition, is the system deviation under descending orbit condition.

4. The geological disaster risk assessment method integrating ascending and descending orbit time series InSAR deformation according to claim 1 is characterized in that: The multidimensional static environmental factors include one or more of terrain relief, soil type, land use type, vegetation coverage, stratum lithology, rainfall, distance from fault zone, distance from water system, and slope. The multidimensional static environmental factors corresponding to different grids are the same or different, and the multiple target evaluation factors corresponding to different grids are the same or different. The dynamic deformation factors and the multidimensional static environmental factors corresponding to the grids are screened to determine the multiple target evaluation factors corresponding to the grids, including: Determine the correlation coefficient between any two of the dynamic deformation factors and the multidimensional static environmental factors corresponding to the grid, and screen the dynamic deformation factors and the multidimensional static environmental factors corresponding to the grid in descending order of the correlation coefficients to determine a plurality of target evaluation factors corresponding to the grid; The correlation coefficient is used to describe the degree of linear correlation between two factors.

5. The geological disaster risk assessment method integrating ascending and descending orbit time series InSAR deformation according to claim 1 is characterized in that: Before generating a geological hazard risk assessment result corresponding to the study area based on the contribution of the target assessment factor corresponding to each grid to the geological hazard events occurring in the study area, the method comprises: For any factor of the target evaluation factor corresponding to each grid, perform the following operations: Determine whether the target evaluation factor corresponding to each grid in the study area includes the factor, and determine the number of first grids in the study area that include the factor based on the determination result; And, judging whether the target evaluation factor corresponding to the grid where the geological disaster event occurred in the study area includes the factor, and determining the number of the second grids where the geological disaster occurred in the study area and included the factor based on the judgment result; Based on the number of the first grids, the number of the second grids, the total number of grids occupied by geological disaster events in the study area and the total number of grids contained in the study area, the contribution degree of the factor to the geological disaster events occurring in the study area is determined.

6. The geological disaster risk assessment method integrating ascending and descending orbit time series InSAR deformation according to claim 5 is characterized in that: Based on the number of the first grids, the number of the second grids, the total number of grids occupied by geological disaster events in the study area, and the total number of grids included in the study area, the contribution of the factor to the geological disaster events occurring in the study area is determined, including: The contribution of this factor to the geological disaster events occurring in the study area is determined according to the following formula: ； in, Factor For geological disasters occurring in the study area The degree of contribution is the second grid number, is the total number of grids occupied by geological disaster events in the study area, is the first grid number, The total number of grids contained in the study area.

7. The geological disaster risk assessment method integrating ascending and descending orbit time series InSAR deformation according to claim 1 is characterized in that: Based on the contribution of the target evaluation factor corresponding to each grid to the geological disaster events occurring in the study area, a geological disaster risk evaluation result corresponding to the study area is generated, including: Calculate the sum of the contribution of the target evaluation factor corresponding to each grid to the geological disaster events occurring in the study area to obtain a comprehensive contribution degree, and generate a geological disaster risk assessment result corresponding to the study area based on the comprehensive contribution degree; Among them, the geological disaster risk assessment results are used to describe the susceptibility of geological disasters in the study area, and the comprehensive contribution level is positively correlated with the susceptibility of geological disasters.

8. A geological disaster risk assessment device integrating ascending and descending orbit time series InSAR deformation, characterized in that: include: A dynamic factor determination module is used to obtain ascending and descending orbit time-series InSAR data corresponding to the study area, perform deformation inversion based on the ascending and descending orbit time-series InSAR data to obtain ascending orbit surface deformation variables and descending orbit surface deformation variables, and fuse the ascending orbit surface deformation variables and the descending orbit surface deformation variables to obtain the dynamic deformation factor corresponding to each grid in the study area; A static factor determination module, used for acquiring optical remote sensing data and environmental data corresponding to a study area, and determining a multidimensional static environmental factor corresponding to each grid in the study area based on the optical remote sensing data and the environmental data; A factor screening module, used for traversing each grid in the study area, screening the dynamic deformation factor and the multi-dimensional static environmental factor corresponding to the grid, so as to determine a plurality of target evaluation factors corresponding to the grid; The risk assessment module is used to generate a geological disaster risk assessment result corresponding to the study area based on the contribution degree of the target assessment factor corresponding to each grid to the geological disaster events occurring in the study area.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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