Traction type landslide area identification method and system based on InSAR technology

Abstract

The invention discloses a traction type landslide area identification method and system based on an InSAR technology, belongs to the technical field of geological disaster dynamic identification and monitoring, and effectively overcomes the limitation of a traditional method by obtaining surface deformation information through radar remote sensing data. The InSAR technology can cover a larger area, provides planar deformation characteristics, and reflects the overall deformation rule of the slope more completely. Meanwhile, the InSAR technology has the advantages of being rapid and efficient, a large amount of data can be obtained in a short time, and the traction type landslide recognition efficiency is remarkably improved. Compared with field geological survey and geophysical exploration, the InSAR technology is not limited by terrains, deformation information of areas difficult to reach can be obtained, data are objective and reliable, and influences of human factors are reduced. By comprehensively analyzing the multi-temporal and multi-track InSAR data, the boundary, form and deformation characteristics of the traction type landslide can be identified more accurately, and more accurate data support is provided for risk assessment and prevention of the traction type landslide.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic identification and monitoring of geological disasters, and specifically relates to a method and system for identifying traction landslide areas based on InSAR technology. Background Art

[0002] Landslide deformation refers to the phenomenon that slope rock and soil masses undergo displacements, ruptures, topplings, etc. under certain geological conditions and external factors. Traction landslide deformation refers to a secondary landslide deformation phenomenon caused by the dragging effect of the sliding of the landslide body on the rock and soil masses at its lower part or front edge. This type of landslide deformation usually occurs after a large-scale sliding of the main landslide body. Due to its special formation mechanism and triggering conditions, traction landslides are unique in terms of morphology, scale, and hazard degree. Methods for monitoring and identifying traction landslide deformation include ground geological surveys, displacement monitoring, geophysical exploration, and remote sensing technology, etc. Measures for preventing and controlling traction landslide deformation mainly include: one is to reduce the sliding energy of the main landslide body, and reduce the sliding potential of the landslide body through measures such as drainage, load reduction, and reinforcement; the second is to enhance the shear strength of the rock and soil masses at the lower part or front edge, and improve the stability of the soil body by using anti-slide piles, anchoring, grouting, etc.; the third is to control groundwater activities, and reduce the influence of groundwater on the landslide body by setting drainage ditches, infiltration wells, etc. The research on traction landslide deformation is of great significance for understanding the landslide chain reaction mechanism, evaluating the landslide disaster risk, and formulating effective landslide prevention and control strategies.

[0003] However, common methods for identifying traction landslide areas rely on the geological experience of investigators and are highly subjective. It is impossible to obtain deep information: it is difficult to obtain the deep deformation characteristics of the landslide body through field investigations. At the same time, the line-of-sight displacement data provided by single-track InSAR observations are limited and difficult to support kinematic analysis of landslides. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for identifying traction landslide areas based on InSAR technology to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: A method for identifying traction landslide areas based on InSAR technology, the method is used for a system for identifying traction landslide areas based on InSAR technology, and the method includes the following steps:

[0006] S1: Collect radar remote sensing data covering the study area, including SAR images with different times, different orbits, and different incident angles; obtain high-precision terrain data of the study area, including DEM, slope, and aspect information; collect geological data of the study area, including stratigraphic distribution, lithology, and tectonic information;

[0007] S2: Preprocess the SAR images, including radiometric calibration, geometric correction, and denoising; construct interferometric pairs, generate interferograms, and unwrap them; calculate differential interferograms to obtain the line-of-sight displacement field in the landslide area;

[0008] S3: Two-dimensional displacement decomposition. Use SAR data from multiple orbits to calculate the annual average deformation in different line-of-sight directions, register the deformation in different line-of-sight directions at the pixel level, and register it with the terrain data; use the formula to calculate the horizontal and vertical deformation rates in the landslide area.

[0009] S4: Invert the thickness of the landslide body based on the mass conservation equation; subtract the thickness of the landslide body from the high-precision terrain data to obtain the shape of the landslide body; judge the shape of the landslide body, which is divided into concave and planar types.

[0010] S5: Use high-precision terrain data to draw the shadow map of the landslide body; divide the landslide body into three parts: the rear edge of the landslide, the front edge of the landslide, and the landslide body according to the geomorphic distribution characteristics.

[0011] S6: Statistically analyze the horizontal and vertical average deformation rates of different parts of the landslide; analyze the distribution characteristics of the deformation rates to judge the deformation mode of the landslide. The identification characteristics of a translational landslide are: the slip surface of the landslide body is planar, along the direction of the maximum slope drop, the horizontal annual average deformation rate changes slightly, and the vertical annual average deformation rate shows a linear growth characteristic; the difference between the maximum and minimum values of the horizontal and vertical annual average deformation rates is relatively large, and the maximum vertical deformation is located at the front edge of the landslide.

[0012] S7: Use field geological surveys and GNSS monitoring means to verify the accuracy of the InSAR identification results; analyze the identification results and evaluate the potential risks of translational landslides.

[0013] S8: According to the deformation characteristics of translational landslides: establish a risk warning model, release warning information in a timely manner, and at the same time formulate corresponding prevention and control measures according to the deformation characteristics and risk levels of translational landslides, and then the entire identification process of translational landslide areas based on InSAR technology can be completed.

[0014] In a preferred embodiment, in step S1,

[0015] The radar remote sensing data includes: Time series SAR images: Collect SAR images covering the study area, including images obtained at different times, in order to analyze the change trend of the landslide over time; Multi-orbit data: Obtain SAR images from different orbits, including images with different radar beam incident angles, in order to observe the landslide deformation from different angles; Multi-source data: If conditions permit, collect SAR images obtained by different sensors, including: L-band, C-band, X-band, to obtain more abundant information.

[0016] High-precision topographic data includes: Digital Elevation Model: Obtain high-precision DEM data of the study area to analyze the topographic relief, slope, and aspect information of the landslide; Topographic map: Collect the topographic map of the study area to understand the geographical location and surrounding environment of the landslide; Geological map: Obtain the geological map of the study area to understand the geological structure and stratigraphic distribution of the landslide area;

[0017] Geological data includes: Stratigraphic distribution: Collect the stratigraphic distribution data of the study area to understand the lithology and soil conditions of the landslide area; Tectonic information: Collect the tectonic information of the study area, including faults and folds, to analyze the geological background of the landslide; Hydrogeological data: Collect the hydrogeological data of the study area, including groundwater distribution and surface water systems, to analyze the hydrogeological environment of the landslide.

[0018] In a preferred embodiment, in step S2, the specific steps include:

[0019] S2-1: Generate an intensity map, set the topographic factors in the Range direction and Azimuth direction to 5 and 1 respectively, and generate an intensity map reflecting the actual topographic ratio of the study area;

[0020] S2-2: Image registration and resampling. Select the radar intensity image obtained in the autumn and winter seasons as the main image, register the intensities of the other images to this main image, and require the registration error to be less than one-quarter of a pixel; Use the polynomial coefficients obtained by the intensity map registration iteration as the standard to register the original single-look complex raw data and obtain the resampled single-look complex data (rslc); Calculate the resampled intensity map (rmli) based on the resampled single-look complex data;

[0021] S2-3: Construct an interference combination, set the temporal baseline and spatial baseline to construct an interference combination, with a maximum temporal baseline of 24 days and a spatial baseline of 100 meters;

[0022] S2-4: Interferogram generation. Perform pixel registration on the rslc that constitutes the interference combination, and then substitute the pixel values for conjugate multiplication to obtain the interference calculation result; Further substitute the topographic parameters of the study area in the radar coordinate format to obtain the differential interferometric phase;

[0023] S2-5: Unwrapping and superposition. Use the minimum cost flow method to unwrap the differential interferometric data; Superpose multiple differential interferometric phases and calculate the phase within the superposition time period. The superposition calculation formula is: Finally, calculate the phase information as deformation information to obtain the annual average deformation of the study area;

[0024] S2-6: Multi-source data calculation. Collect radar data with different incident angles and calculate according to the above method in turn to obtain the deformation values of different lines of sight covering the same time period.

[0025] In a preferred embodiment, in step S3, the calculated annual average deformation information at different incident angles is recorded as dLos1 and dlos2 respectively, and the dLos1, dlos2, aspect and slope raster data are substituted into the following formula using the raster calculator tool in ArcGIS to calculate the actual two-dimensional deformation field of the surface;

[0026]

[0027] where d H and d V are the lateral and vertical surface deformation rates respectively, α represents the angle between the satellite flight direction and the true north, β is the angle between the maximum slope drop direction and the true north, and θ represents the satellite incidence angle.

[0028] In a preferred embodiment, in step S3, the thickness of the accumulation landslide body pinches out at the boundaries on both sides, and the soil landslide body presents uniform rheological characteristics in space. The landslide depth and the vertical deformation obtained by InSAR are solved using the following formula:

[0029]

[0030] Among them, h is the thickness of the landslide, t is the time, f is the rheological coefficient, Vsurf is the surface deformation rate, and the decomposed two-dimensional displacement field information is substituted into it, and the thickness information of the landslide slope can be obtained using the least squares method.

[0031] In a preferred embodiment, in step S4, the thickness of the accumulation landslide body pinches out at the boundaries on both sides, and the soil landslide body presents uniform rheological characteristics in space. The landslide depth and the vertical deformation obtained by InSAR are solved using the following formula:

[0032]

[0033] Among them, h is the thickness of the landslide, t is the time, f is the rheological coefficient, Vsurf is the surface deformation rate, and the decomposed two-dimensional displacement field information is substituted into it, and the thickness information of the landslide slope can be obtained using the least squares method.

[0034] In a preferred embodiment, in step S4, detailed terrain information of the landslide area is obtained in combination with high-precision aerial survey data; the geometric shape of the landslide is extracted from the current surface elevation by subtracting the sliding body thickness obtained by inversion; the spatial distribution of the landslide thickness, especially the maximum thickness and the thickness change of the surrounding area is analyzed; the area is delineated with the maximum thickness as the center point and twice the maximum thickness as the radius, and the degree of change of the landslide thickness in the area is evaluated; if the landslide thickness in the area is reduced by no more than one-quarter of the original thickness, the sliding surface morphology is determined to be planar; if the reduction exceeds one-quarter, it is determined to be concave.

[0035] In a preferred embodiment, in step S5, a three-dimensional topographic map of the landslide body is created through a high-precision surface elevation model, and a hillshade map is generated; the hillshade map can clearly display the terrain undulation and geomorphic features of the landslide body, which helps to more comprehensively understand the spatial distribution and deformation characteristics of the landslide body; during the landslide zoning process, it is determined that the rear edge of the landslide is the highest point of the landslide body, the front edge of the landslide is the lowest point of the landslide body, and the landslide body is located between the rear edge and the front edge of the landslide; the vertical and horizontal deformation rates of the three parts of the landslide: the rear edge, the front edge, and the landslide body are analyzed to reveal the deformation characteristics and differences in different regions.

[0036] In a preferred embodiment, in step S7, the verification and risk assessment include:

[0037] 1. Field geological survey verification: Organize a professional geological survey team to conduct on-site inspections of the potential landslide areas identified by InSAR to verify the boundaries, morphology, and deformation characteristics of the landslides; use geological drilling and trenching excavation methods to obtain the internal structure and geotechnical properties of the landslide body to further verify the accuracy of the InSAR identification results;

[0038] 2. GNSS monitoring verification: Install GNSS monitoring points within the potential landslide areas identified by InSAR to monitor the deformation of the landslide body in real time; compare and analyze the GNSS monitoring data with the InSAR identification results to verify the accuracy of the InSAR identification results and evaluate the deformation rate and trend of the landslide body;

[0039] 3. Risk assessment: Based on the InSAR identification results, field investigations, and GNSS monitoring data, assess the potential risks of the translational landslide; analyze the deformation characteristics, deformation rate, landslide volume, and landslide velocity parameters of the landslide body to evaluate the potential threats and influence ranges of the landslide; combine the geographical location, surrounding environment, and socio-economic conditions of the landslide body to evaluate the potential risks of the landslide to people, property, and infrastructure.

[0040] In a preferred embodiment, a translational landslide area identification system based on InSAR technology is characterized in that: the system includes:

[0041] Data acquisition module: Responsible for collecting radar remote sensing data covering the study area, including SAR images at different times, different orbits, and different incident angles; obtaining high-precision terrain data of the study area, including DEM, slope, and aspect information; collecting geological data of the study area, including stratigraphic distribution, lithology, and tectonic information; collecting meteorological data, land use data, and socio-economic data of the study area;

[0042] Data preprocessing module: Preprocess the SAR images, including radiometric calibration, geometric correction, and noise removal; process the high-precision terrain data, including DEM generation, slope and aspect calculation; process the geological data, including generation of stratigraphic distribution maps, lithologic maps, and structural maps.

[0043] InSAR data processing module: Construct interferometric pairs, generate and unwrap interferograms; calculate differential interferograms to obtain the line-of-sight displacement field in the landslide area; use multi-source orbit SAR data to calculate the annual average deformation in different line-of-sight directions; register the deformations in different line-of-sight directions at the pixel level and register them with the terrain data; use formulas to calculate the horizontal and vertical displacement fields in the landslide area.

[0044] Slip surface morphology inversion module: Based on the mass conservation equation, invert the thickness of the landslide body; use high-precision terrain data, subtract the thickness of the landslide body to obtain the morphology of the landslide body; judge the morphology of the landslide body, which is divided into concave and planar types.

[0045] Landslide zoning module: Use high-precision terrain data to draw the shadow map of the landslide body; divide the landslide body into three parts: the rear edge of the landslide, the front edge of the landslide, and the landslide body according to the geomorphic distribution characteristics.

[0046] Deformation mode recognition module: Analyze the horizontal and vertical deformation rates of different parts of the landslide to reveal the deformation characteristics and differences in different regions; analyze the distribution characteristics of the deformation rates to judge the deformation mode of the landslide.

[0047] Result verification module: Use field geological surveys and GNSS monitoring means to verify the accuracy of the InSAR identification results.

[0048] Risk assessment module: Based on the InSAR identification results and field survey and GNSS monitoring data, evaluate the potential risks of translational landslides; analyze the deformation characteristics, deformation rates, landslide volume, and landslide speed parameters of the landslide body to evaluate the potential threats and influence ranges of the landslide; combine the geographical location, surrounding environment, and socio-economic conditions of the landslide body to evaluate the potential risks of the landslide to people, property, and infrastructure.

[0049] Prevention and control measures module: According to the deformation characteristics and risk levels of translational landslides, formulate corresponding prevention and control measures; carry out engineering treatment, monitoring and early warning, and personnel evacuation work.

[0050] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0051] 1. In the present invention, for the method of identifying traction landslide areas based on InSAR technology, by using radar remote sensing data to obtain surface deformation information, the limitations of traditional methods are effectively overcome. Compared with GNSS monitoring, InSAR technology can cover a larger area, provide areal deformation characteristics, and more comprehensively reflect the overall deformation law of slopes. At the same time, InSAR technology is fast and efficient, capable of obtaining a large amount of data in a short time, significantly improving the efficiency of identifying traction landslides. Compared with field geological surveys and geophysical explorations, InSAR technology is not restricted by terrain, can obtain deformation information in difficult-to-reach areas, and the data is objective and reliable, reducing the influence of human factors. By comprehensively analyzing multi-temporal and multi-orbit InSAR data, the boundaries, shapes, and deformation characteristics of traction landslides can be more accurately identified, providing more accurate data support for the risk assessment and prevention of traction landslides.

[0052] 2. In the present invention, by combining the InSAR identification results with multi-source data such as field geological surveys and GNSS monitoring, the deformation patterns, movement characteristics, and inducing factors of traction landslides can be more comprehensively analyzed. This helps to more accurately assess the potential risks of traction landslides, including the stability, deformation rate, and potential impact range of the traction landslide body. Based on these assessment results, more scientific and effective prevention and control measures can be formulated, including engineering treatment, monitoring and early warning, and evacuation of personnel, to minimize the losses caused by traction landslide disasters. In addition, this method can also provide technical support for the long-term monitoring and early warning of traction landslide disasters, realizing the early detection and prevention of traction landslide disasters, and further enhancing the scientificity and effectiveness of geological disaster prevention and control work. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the principle of the method flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] Refer to Figure 1 ,

[0056] Embodiment:

[0057] A method for identifying traction landslide areas based on InSAR technology, the method is used for a system for identifying traction landslide areas based on InSAR technology, and the method includes the following steps:

[0058] S1: Collect radar remote sensing data covering the study area, including SAR images at different times, different orbits, and different incident angles. Obtain high-precision topographic data of the study area, including information such as DEM, slope, and aspect. Collect geological data of the study area, including information such as stratigraphic distribution, lithology, and structure;

[0059] S2: Preprocess the SAR images, including radiometric calibration, geometric correction, and denoising. Construct interferometric pairs and generate and unwrap interferograms. Calculate differential interferograms to obtain the line-of-sight displacement field in the landslide area;

[0060] S3: Two-dimensional displacement decomposition. Use SAR data from multiple source orbits to calculate the annual average deformation in different line-of-sight directions, register the deformations in different line-of-sight directions at the pixel level, and register them with the topographic data. Use the formula to calculate the horizontal and vertical deformation rates in the landslide area;

[0061] S4: Based on the mass conservation equation, invert the thickness of the landslide body. Subtract the thickness of the landslide body from the high-precision topographic data to obtain the shape of the landslide body. Judge the shape of the landslide body, which is divided into concave and planar types;

[0062] S5: Use high-precision topographic data to draw the shadow map of the landslide body. According to the geomorphic distribution characteristics, divide the landslide body into three parts: the rear edge of the landslide, the front edge of the landslide, and the landslide body;

[0063] S6: Statistically analyze the horizontal and vertical average deformation rates of different parts of the landslide. Analyze the distribution characteristics of the deformation rates to judge the deformation mode of the landslide. The identification characteristics of a translational landslide are: the slip surface shape of the landslide body is planar, along the direction of the maximum slope drop, the horizontal annual average deformation rate changes insignificantly, and the vertical annual average deformation rate shows a linear growth characteristic; the difference between the maximum and minimum values of the horizontal and vertical annual average deformation rates is large, and the maximum vertical deformation is located at the front edge of the landslide;

[0064] S7: Use field geological surveys, GNSS monitoring and other means to verify the accuracy of the InSAR identification results. Analyze the identification results and evaluate the potential risks of translational landslides;

[0065] S8: According to the deformation characteristics of translational landslides: establish a risk warning model, issue warning information in a timely manner, and at the same time, formulate corresponding prevention and control measures according to the deformation characteristics and risk levels of translational landslides, and then the entire identification process of translational landslide areas based on InSAR technology can be ended.

[0066] In step S1,

[0067] The radar remote sensing data includes:

[0068] Time - series SAR images: Collect SAR images covering the study area, including images acquired at different times, in order to analyze the temporal variation trend of landslides.

[0069] Multi - orbit data: Obtain SAR images of different orbits, including images with different radar beam incident angles, in order to observe landslide deformation from different angles.

[0070] Multi - source data: If conditions permit, collect SAR images obtained by different sensors, such as L - band, C - band, X - band, etc., to obtain more abundant information.

[0071] High - precision terrain data includes:

[0072] Digital Elevation Model (DEM): Obtain high - precision DEM data of the study area in order to analyze information such as the topographic undulation, slope, and aspect of the landslide.

[0073] Topographic map: Collect the topographic map of the study area in order to understand the geographical location of the landslide and its surrounding environment.

[0074] Geological map: Obtain the geological map of the study area in order to understand the geological structure and stratigraphic distribution of the landslide area.

[0075] Geological data includes:

[0076] Stratigraphic distribution: Collect the stratigraphic distribution data of the study area in order to understand the lithology and soil conditions of the landslide area.

[0077] Tectonic information: Collect the tectonic information of the study area, such as faults, folds, etc., in order to analyze the geological background of the landslide.

[0078] Hydro - geological data: Collect the hydro - geological data of the study area, such as groundwater distribution, surface water systems, etc., in order to analyze the hydro - geological environment of the landslide.

[0079] In step S2, the specific steps include:

[0080] S2 - 1: Generate an intensity map. Set the terrain factors in the Range direction and Azimuth direction to 5 and 1 respectively, and generate an intensity map reflecting the actual terrain ratio of the study area.

[0081] S2 - 2: Image registration and resampling. Select the radar intensity image acquired in autumn and winter as the main image, and register the intensities of the remaining images to this main image, with the registration error required to be less than one - quarter of a pixel. Using the polynomial coefficients obtained from the intensity map registration iteration as the standard, register the original single - look complex (SLC) raw data to obtain the resampled single - look complex data (rslc). Calculate the resampled intensity map (rmli) based on the resampled single - look complex data.

[0082] S2-3: Construct the interference combination, set the temporal baseline and spatial baseline to construct the interference combination, with the maximum temporal baseline being 24 days and the spatial baseline being 100 meters.

[0083] S2-4: Generate the interferogram. Perform pixel registration on the RSLCs that make up the interference combination, and then substitute the pixel values for conjugate multiplication to obtain the interference calculation result. Further substitute the terrain parameters of the study area in the radar coordinate format to obtain the differential interferometric phase.

[0084] S2-5: Unwrap and superimpose. Use the minimum cost flow method to unwrap the differential interferometric data. Superimpose multiple differential interferometric phases and calculate the phase within the superimposed time period. The superimposition calculation formula is: Finally, calculate the phase information as deformation information to obtain the annual average deformation amount of the study area.

[0085] S2-6: Calculate multi-source data. Collect radar data with different incident angles and calculate successively according to the above method to obtain the deformation values of different lines of sight covering the same time period.

[0086] In step S3, the annual average deformation information obtained by calculation for different incident angles is denoted as dLos1 and dlos2 respectively. Use the raster calculator tool in Arcgis to substitute the raster data of dLos1, dlos2, Aspect, and slope into the following formula to calculate the actual two-dimensional deformation field of the surface;

[0087]

[0088] where d H and d V are the surface deformation rates in the horizontal and vertical directions respectively, α represents the angle between the satellite flight direction and the due north direction, β is the angle between the maximum slope direction of the slope and the due north direction, and θ represents the satellite incident angle.

[0089] In step S3, the thickness of the sliding mass of the accumulation landslide tapers off at both side boundaries, and the sliding mass of the soil landslide shows a uniform rheological characteristic in space. The landslide depth and the vertical deformation obtained by InSAR are solved using the following formula

[0090]

[0091] where h is the landslide thickness, t is the time, f is the rheological coefficient, and Vsurf is the surface deformation rate. Substitute the decomposed two-dimensional displacement field information and use the least squares method to obtain the information on the thickness of the sliding mass slope surface.

[0092] In step S4, the thickness of the sliding mass of the accumulation landslide tapers off at both side boundaries, and the sliding mass of the soil landslide shows a uniform rheological characteristic in space. The landslide depth and the vertical deformation obtained by InSAR are solved using the following formula

[0093]

[0094] Among them, h is the landslide thickness, t is the time, f is the rheological coefficient, and Vsurf is the surface deformation rate. Substituting the decomposed two-dimensional displacement field information, the landslide slope thickness information can be obtained by using the least square method.

[0095] In step S4, combined with high-precision aerial survey data, detailed topographic information of the landslide area is obtained. By subtracting the landslide thickness obtained by inversion, the geometric shape of the landslide is extracted from the current surface elevation. Analyze the spatial distribution of the landslide thickness, especially the thickness change in the area around the maximum thickness and its vicinity. Taking the maximum thickness as the center point and twice the maximum thickness as the radius to delimit the area, and evaluate the degree of change of the landslide thickness within this area. If the reduction of the landslide thickness within this area does not exceed one-fourth of the original thickness, the landslide surface shape is determined to be planar; if the reduction exceeds one-fourth, it is determined to be concave.

[0096] In step S5, through a high-precision surface elevation model, a three-dimensional topographic map of the landslide body is created, and a hillshade map is generated. The hillshade map can clearly display the topographic undulation and geomorphic features of the landslide body, which helps to more comprehensively understand the spatial distribution and deformation characteristics of the landslide body. During the landslide zoning process, it is determined that the rear edge of the landslide is the highest point of the landslide body, the front edge of the landslide is the lowest point of the landslide body, and the landslide body is located between the rear edge and the front edge of the landslide. Analyze the vertical and horizontal deformation rates of the three parts of the landslide: the rear edge, the front edge, and the landslide body, so as to reveal the deformation characteristics and differences in different regions.

[0097] In step S7, the verification and risk assessment include:

[0098] 1. Field geological survey verification: Organize a professional geological survey team to conduct on-site inspections on the potential landslide areas identified by InSAR to verify the boundaries, shapes, and deformation characteristics of the landslides. By means of geological drilling, trench excavation, etc., obtain the internal structure and geotechnical properties of the landslide body, and further verify the accuracy of the InSAR identification results.

[0099] 2. GNSS monitoring verification: Install GNSS monitoring points within the potential landslide areas identified by InSAR to conduct real-time monitoring of the deformation of the landslide body. Compare and analyze the GNSS monitoring data with the InSAR identification results to verify the accuracy of the InSAR identification results, and evaluate the deformation rate and trend of the landslide body.

[0100] 3. Risk assessment: Based on the InSAR identification results, field surveys, and GNSS monitoring data, assess the potential risks of translational landslides. Analyze parameters such as the deformation characteristics, deformation rate, landslide volume, and landslide speed of the landslide mass, and evaluate the potential threats and influence ranges of the landslides. Considering factors such as the geographical location, surrounding environment, and socioeconomic conditions of the landslide mass, evaluate the potential risks of the landslide to personnel, property, and infrastructure.

[0101] A translational landslide area identification system based on InSAR technology. When the system is in use, it runs a translational landslide area identification method based on InSAR technology.

[0102] The system includes:

[0103] Data acquisition module: Responsible for collecting radar remote sensing data covering the study area, including SAR images with different times, different orbits, and different incident angles. Obtain high-precision terrain data of the study area, including information such as DEM, slope, and aspect. Collect geological data of the study area, including information such as stratigraphic distribution, lithology, and structure. Collect meteorological data, land use data, socioeconomic data, etc. of the study area.

[0104] Data preprocessing module: Preprocess the SAR images, including radiometric calibration, geometric correction, noise removal, etc. Process the high-precision terrain data, including DEM generation, slope and aspect calculation, etc. Process the geological data, including the generation of stratigraphic distribution maps, lithology maps, and structure maps.

[0105] InSAR data processing module: Construct interferometric pairs, generate and unwrap interferograms. Calculate differential interferograms to obtain the line-of-sight displacement field of the landslide area. Use multi-source orbit SAR data to calculate the annual average deformation amount in different line-of-sight directions. Register the deformation amounts in different line-of-sight directions at the pixel level and register them with the terrain data. Use formulas to calculate the horizontal and vertical displacement fields of the landslide area.

[0106] Slip surface morphology inversion module: Invert the thickness of the landslide mass based on the mass conservation equation. Use high-precision terrain data, subtract the thickness of the landslide mass to obtain the morphology of the landslide mass. Judge the morphology of the landslide mass, which is divided into concave type and planar type.

[0107] Landslide zoning module: Use high-precision terrain data to draw the shadow map of the landslide mass. According to the geomorphic distribution characteristics, divide the landslide mass into three parts: the rear edge of the landslide, the front edge of the landslide, and the landslide mass.

[0108] Deformation mode recognition module: Analyze the horizontal and vertical deformation rates of different parts of the landslide to reveal the deformation characteristics and differences in different regions. Analyze the distribution characteristics of the deformation rate and judge the deformation mode of the landslide.

[0109] Result verification module: Verify the accuracy of the InSAR recognition results by means of field geological surveys, GNSS monitoring, etc.

[0110] Risk assessment module: Based on the InSAR recognition results, field surveys, and GNSS monitoring data, evaluate the potential risks of translational landslides. Analyze parameters such as the deformation characteristics, deformation rate, landslide volume, and landslide speed of the landslide body, and evaluate the potential threats and influence ranges of the landslide. Combine factors such as the geographical location of the landslide body, surrounding environment, and social and economic conditions to evaluate the potential risks of the landslide to personnel, property, and infrastructure.

[0111] Prevention and control measures module: Develop corresponding prevention and control measures according to the deformation characteristics and risk levels of translational landslides. Carry out engineering treatment, monitoring and early warning, and personnel evacuation work.

[0112] As can be seen from the above:

[0113] In the present invention, the method for identifying translational landslide areas based on InSAR technology effectively overcomes the limitations of traditional methods by using radar remote sensing data to obtain surface deformation information. Compared with GNSS monitoring, InSAR technology can cover a larger area, provide areal deformation characteristics, and more comprehensively reflect the overall deformation law of slopes. At the same time, InSAR technology is fast and efficient, can obtain a large amount of data in a short time, and significantly improves the efficiency of landslide identification. Compared with field geological surveys and geophysical explorations, InSAR technology is not restricted by terrain, can obtain deformation information in inaccessible areas, and the data is objective and reliable, reducing the influence of human factors. By comprehensively analyzing multi-temporal and multi-orbit InSAR data, the boundaries, shapes, and deformation characteristics of landslides can be identified more accurately, providing more accurate data support for landslide risk assessment and prevention and control.

[0114] In the present invention, by combining the InSAR recognition results with multi-source data such as field geological surveys and GNSS monitoring, the deformation patterns, movement characteristics, and inducing factors of landslides can be analyzed more comprehensively. This helps to more accurately evaluate the potential risks of landslides, including the stability of the landslide body, deformation rate, potential influence range, etc. Based on these evaluation results, more scientific and effective prevention and control measures can be developed, such as engineering treatment, monitoring and early warning, and personnel evacuation, to minimize the losses caused by landslide disasters. In addition, this method can also provide technical support for the long-term monitoring and early warning of landslide disasters, realize the early detection and prevention of landslide disasters, and further improve the scientificity and effectiveness of geological disaster prevention and control work.

[0115] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the said element.

[0116] The above description enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying traction-type landslide areas based on InSAR technology, characterized in that: The method is used for a traction-type landslide area identification system based on InSAR technology, and the method comprises the following steps: S1: Collect radar remote sensing data covering the study area, including SAR images at different times, orbits and incident angles; obtain high-precision terrain data of the study area, including DEM, slope and aspect information; collect geological data of the study area, including stratigraphic distribution, lithology and structural information; S2: Preprocess the SAR images, including radiometric calibration, geometric correction, and noise removal; construct interference pairs, generate and unwrap interference patterns; calculate differential interference patterns to obtain the line-of-sight displacement field of the landslide area; S3: Two-dimensional displacement decomposition, using SAR data from multiple source tracks to calculate the average annual deformation in different sight lines, perform pixel-level registration on the deformation in different sight lines, and register it with the terrain data; use the formula to calculate the horizontal and vertical deformation rates of the landslide area; S4: Based on the mass conservation equation, inverse the thickness of the landslide body; using high-precision terrain data, subtract the thickness of the landslide body to obtain the shape of the landslide body; determine the shape of the landslide body, which can be divided into concave type and plane type; S5: Use high-precision terrain data to draw a shadow map of the landslide body; divide the landslide body into three parts: the rear edge of the landslide, the front edge of the landslide, and the landslide body according to the landform distribution characteristics; S6: Statistical analysis of the average horizontal and vertical deformation rates of different parts of the landslide; Analyze the distribution characteristics of the deformation rate and determine the deformation mode of the landslide; The identification characteristics of the traction-type landslide are: the sliding surface of the landslide is planar, along the direction of the maximum slope drop, the horizontal annual average deformation rate does not change significantly, and the vertical annual average deformation rate shows a linear growth characteristic; The maximum values ​​of the horizontal and vertical annual average deformation rates are quite different, and the maximum vertical deformation is located at the front edge of the landslide; S7: Use field geological surveys and GNSS monitoring methods to verify the accuracy of InSAR identification results; analyze the identification results and assess the potential risks of traction-type landslides; S8: Based on the deformation characteristics of traction-type landslides: establish a risk warning model and issue warning information in a timely manner. At the same time, formulate corresponding prevention and control measures based on the deformation characteristics and risk level of traction-type landslides. After that, the entire traction-type landslide area identification process based on InSAR technology can be completed.

2. The method and system for identifying traction-type landslide areas based on InSAR technology according to claim 1, characterized in that: In the step S1, Radar remote sensing data include: Time series SAR images: collect SAR images covering the study area, including images acquired at different times, so as to analyze the changing trend of landslides over time; Multi-track data: obtain SAR images of different tracks, including images of different radar beam incidence angles, so as to observe landslide deformation from different angles; Multi-source data: if conditions permit, collect SAR images acquired by different sensors, including: L-band, C-band, X-band, to obtain richer information; High-precision terrain data include: Digital Elevation Model: Obtain high-precision DEM data of the study area to analyze the terrain undulation, slope and aspect information of the landslide; Topographic Map: Collect topographic maps of the study area to understand the geographical location and surrounding environment of the landslide; Geological Map: Obtain geological maps of the study area to understand the geological structure and stratigraphic distribution of the landslide area; The geological data include: stratigraphic distribution: collect stratigraphic distribution data of the study area in order to understand the lithology and soil conditions of the landslide area; structural information: collect structural information of the study area, including faults and folds, in order to analyze the geological background of the landslide; hydrogeological data: collect hydrogeological data of the study area, including groundwater distribution and surface water system, in order to analyze the hydrological environment of the landslide.

3. The method and system for identifying traction-type landslide areas based on InSAR technology according to claim 1, characterized in that: In step S2, the specific steps include: S2-1: Generate an intensity map, set the terrain factors in the Range and Azimuth directions to 5 and 1 respectively, and generate an intensity map that reflects the actual terrain proportions of the study area; S2-2: Image registration and resampling: select the radar intensity image obtained in autumn and winter as the main image, and register the remaining image intensities to the main image, requiring the registration error to be less than one-quarter of a pixel; use the polynomial coefficients obtained by iterative intensity map registration as the standard to register the original single-view complex raw data to obtain the resampled single-view complex data; calculate the resampled intensity map based on the resampled single-view complex data; S2-3: Construct interference combination, set time baseline and space baseline to construct interference combination, the maximum time baseline is 24 days, and the space baseline is 100 meters; S2-4: Interference pattern generation, the RSLC constituting the interference combination is pixel-aligned, and then the pixel value is substituted for conjugate multiplication to obtain the interference calculation result; the terrain parameters of the study area in radar coordinate format are further substituted to obtain the differential interference phase; S2-5: Unwrapping and superposition, using the minimum cost flow method to unwrap the differential interference data; superimpose multiple differential interference phases, and calculate the phase within the superposition time period. The superposition calculation formula is: Finally, the phase information is calculated as deformation information to obtain the average annual deformation of the study area; S2-6: Multi-source data calculation, collect radar data with different incident angles, and calculate them in turn according to the above method to obtain different line-of-sight phase deformation values ​​covering the same time period.

4. The method and system for identifying traction-type landslide areas based on InSAR technology according to claim 1, characterized in that: In step S3, the calculated annual average deformation information of different incident angles is recorded as dLos1 and dlos2 respectively, and the dLos1, dlos2, aspect and slope raster data are substituted into the following formula using the raster calculator tool in ArcGIS to calculate the actual two-dimensional deformation field of the surface; where d H and d V They represent the lateral and vertical surface deformation rates respectively, α represents the angle between the satellite flight direction and the true north, β is the angle between the maximum slope drop direction and the true north, and θ represents the satellite incidence angle.

5. The method and system for identifying traction-type landslide areas based on InSAR technology as claimed in claim 1, characterized in that: In step S3, the thickness of the accumulation landslide body pinches out at the boundaries on both sides, and the soil landslide body presents uniform rheological characteristics in space. The landslide depth and the vertical deformation obtained by InSAR are solved using the following formula: Among them, h is the thickness of the landslide, t is the time, f is the rheological coefficient, Vsurf is the surface deformation rate, and the decomposed two-dimensional displacement field information is substituted into it, and the thickness information of the landslide slope can be obtained using the least squares method.

6. The method and system for identifying traction-type landslide areas based on InSAR technology according to claim 1, characterized in that: In step S4, the thickness of the accumulation landslide body pinches out at the boundaries on both sides, and the soil landslide body presents uniform rheological characteristics in space. The landslide depth and the vertical deformation obtained by InSAR are solved using the following formula: Among them, h is the thickness of the landslide, t is the time, f is the rheological coefficient, Vsurf is the surface deformation rate, and the decomposed two-dimensional displacement field information is substituted into it, and the thickness information of the landslide slope can be obtained using the least squares method.

7. The method and system for identifying traction-type landslide areas based on InSAR technology according to claim 1, characterized in that: In step S4, the detailed topographic information of the landslide area is obtained by combining high-precision aerial survey data; the geometric shape of the landslide is extracted from the current surface elevation by subtracting the sliding body thickness obtained by inversion; the spatial distribution of the landslide thickness, especially the maximum thickness and the thickness change of the surrounding area, is analyzed; the area is demarcated with the maximum thickness as the center point and twice the maximum thickness as the radius, and the degree of change of the landslide thickness in the area is evaluated; if the landslide thickness in the area decreases by no more than one-fourth of the original thickness, the sliding surface is determined to be a planar type; If the reduction is more than one quarter, it is judged as concave type.

8. The method and system for identifying traction-type landslide areas based on InSAR technology as claimed in claim 1, characterized in that: In the step S5, a three-dimensional topographic map of the landslide body is created through a high-precision surface elevation model, and a mountain shadow map is generated; the mountain shadow map can clearly show the topographic undulations and geomorphic features of the landslide body, which is helpful for a more comprehensive understanding of the spatial distribution and deformation characteristics of the landslide body; in the landslide zoning process, the rear edge of the landslide is determined to be the highest point of the landslide body, the front edge of the landslide is determined to be the lowest point of the landslide body, and the landslide body is located between the rear edge of the landslide and the front edge of the landslide; the vertical and horizontal deformation rates of the three parts of the landslide: the rear edge, the front edge and the landslide body are analyzed to reveal the deformation characteristics and differences of different regions.

9. The method and system for identifying traction-type landslide areas based on InSAR technology according to claim 1, characterized in that: In step S7, performing verification and risk assessment includes:

1. Field geological survey verification: organize a professional geological survey team to conduct field investigations on the potential landslide areas identified by InSAR to verify the boundaries, morphology and deformation characteristics of the landslides; use geological drilling and trenching to obtain the internal structure and geotechnical properties of the landslide body to further verify the accuracy of the InSAR identification results; 2. GNSS monitoring verification: GNSS monitoring points are deployed in the potential landslide area identified by InSAR to monitor the deformation of the landslide body in real time; the GNSS monitoring data is compared and analyzed with the InSAR identification results to verify the accuracy of the InSAR identification results and evaluate the deformation rate and trend of the landslide body; 3. Risk assessment: Based on InSAR identification results, field surveys and GNSS monitoring data, the potential risks of traction-type landslides are assessed; the deformation characteristics, deformation rate, landslide volume and landslide velocity parameters of the landslide body are analyzed to assess the potential threat and impact range of the landslide; the potential risks of landslides to personnel, property and infrastructure are assessed in combination with the geographical location, surrounding environment and socio-economic conditions of the landslide body.

10. A traction-type landslide area identification system based on InSAR technology, characterized in that: When in use, the system runs the method for identifying traction-type landslide areas based on InSAR technology as described in any one of claims 1 to 9; The system comprises: Data acquisition module: responsible for collecting radar remote sensing data covering the study area, including SAR images at different times, different orbits, and different incident angles; obtaining high-precision terrain data of the study area, including DEM, slope, and aspect information; collecting geological data of the study area, including stratum distribution, lithology, and structural information; collecting meteorological data, land use data, and socio-economic data of the study area; Data preprocessing module: preprocess SAR images, including radiation calibration, geometric correction, and noise removal; process high-precision terrain data, including DEM generation, slope and aspect calculation; process geological data, including the generation of stratigraphic distribution maps, lithology maps, and structural maps; InSAR data processing module: construct interference pairs, generate and unwrap interference patterns; calculate differential interference patterns to obtain the line-of-sight displacement field of the landslide area; use SAR data from multi-source orbits to calculate the average annual deformation in different line-of-sight directions; perform pixel-level registration of deformation in different line-of-sight directions and register them with terrain data; use formulas to calculate the horizontal and vertical displacement fields of the landslide area; Slide surface morphology inversion module: Based on the mass conservation equation, the thickness of the landslide body is inverted; the landslide body thickness is subtracted from the high-precision terrain data to obtain the shape of the landslide body; the shape of the landslide body is determined and divided into concave type and plane type; Landslide zoning module: Use high-precision terrain data to draw a shadow map of the landslide body; divide the landslide body into three parts: the rear edge of the landslide, the front edge of the landslide, and the landslide body according to the landform distribution characteristics; Deformation pattern recognition module: Analyze the horizontal and vertical deformation rates of different parts of the landslide to reveal the deformation characteristics and differences of different areas; analyze the distribution characteristics of the deformation rate to determine the deformation pattern of the landslide; Result verification module: Use field geological surveys and GNSS monitoring methods to verify the accuracy of InSAR identification results; Risk assessment module: Based on InSAR identification results, field surveys, and GNSS monitoring data, the potential risk of traction-type landslides is assessed; the deformation characteristics, deformation rate, landslide volume, and landslide velocity parameters of the landslide body are analyzed to assess the potential threat and impact range of the landslide; the potential risk of landslides to personnel, property, and infrastructure is assessed in combination with the geographical location, surrounding environment, and socioeconomic conditions of the landslide body; Prevention and control measures module: formulate corresponding prevention and control measures according to the deformation characteristics and risk level of traction-type landslides; carry out engineering control, monitoring and early warning, and personnel evacuation work.

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