InSAR-based sliding surface displacement and residual friction inversion method

Abstract

The invention relates to the field of geological disaster monitoring, and discloses a sliding surface displacement and residual friction inversion method based on InSAR, and the method comprises the steps: calculating LOS-direction deformation through employing an SBAS-InSAR method based on elevating track SAR data and ALOS DEM data; in combination with satellite parameters and topographic features, LOS direction deformation is decomposed to obtain a normal deformation rate in the slope direction; based on the decomposed normal deformation rate value along the slope direction, constructing a landslide sliding surface physical model, improving a particle swarm optimization algorithm, and inverting the optimal sliding surface depth of the landslide; based on the inverted optimal sliding surface depth value, a slope surface rate and sliding surface rate relation model is constructed, and the optimal sliding surface rate is obtained through inversion by using a genetic algorithm; obtaining the residual friction of the sliding surface by using the inversed optimal sliding surface rate and combining a friction coefficient calculation model; according to the method, the quantitative evaluation capability of landslide stability and activity is improved, and a more accurate and practical tool is provided for landslide risk prediction.

Description

Technical Field

[0001] The invention relates to the field of geological disaster monitoring, and in particular to an InSAR-based sliding surface displacement and residual friction inversion method. Background Art

[0002] Landslides, as a common geological disaster, pose a huge threat to human life, property and the natural environment. Slope deformation is the most direct manifestation of landslide disasters. By analyzing slope deformation, we can understand the mechanism of landslides and the possible hazards to a certain extent. However, with the in-depth research of geological disaster researchers, it is gradually found that the surface deformation of most landslides, especially medium and large landslides, is a direct reflection of the movement of deep sliding surfaces. The instability risk of the sliding body also directly depends on the movement state of the sliding surface and the residual friction of the sliding surface. However, due to the fact that the sliding surface of the landslide is mostly located in the deep part of the slope that is difficult to access, the movement characteristics of the landslide are complex, and the relationship between the deformation information of the landslide surface and the displacement field of the deep sliding surface is difficult to directly determine, and the residual friction of the sliding surface is difficult to evaluate. By studying the residual friction characteristics after the sliding surface displacement, the potential risk of landslides can be better evaluated and corresponding prevention and control measures can be taken. Therefore, the accurate inversion research of the displacement field and residual friction of the sliding surface of the landslide is of great significance.

[0003] Commonly used technologies for obtaining the movement characteristics of landslide surfaces include TDR (Time Domain Reflectometry) technology and borehole inclinometer technology. Although the data obtained are relatively reliable, they are all contact measurements, which require the deployment of multiple boreholes on the slope. When the landslide displacement is large, it will also damage the measuring instruments and require repeated maintenance by humans. The cost is high, time-consuming and labor-intensive, and only sparse point observation data can be obtained, which is difficult to reflect the true situation of the entire landslide. With the development of remote sensing technology, especially the application of synthetic aperture radar interferometry (InSAR) technology, researchers can monitor surface deformation with unprecedented accuracy and range. InSAR can detect extremely small surface movements by analyzing the phase difference of radar signals reflected from satellites to the ground, thereby effectively monitoring landslides and other surface deformation phenomena.

[0004] Although InSAR technology is very effective in monitoring surface deformation, it still faces challenges in revealing the deep movement mechanism of landslides. InSAR technology can only obtain the surface displacement field of landslides, and it is difficult to directly reflect the deep sliding surface displacement and the residual friction of the sliding surface. This is mainly because the deep displacement has a small impact on the surface reflection signal and is difficult to reflect directly through the radar signal. However, InSAR technology can only obtain the surface displacement field of landslides, and it is difficult to directly reflect the deep sliding surface displacement and the residual friction of the sliding surface. How to construct a relationship model between the surface displacement field and the deep sliding surface displacement and the residual friction of the sliding surface is the key to solving such problems. In addition, since the inversion problem is often accompanied by uncertainty and multiple solutions, how to improve the accuracy of the inversion becomes a key challenge. Summary of the invention

[0005] The purpose of the present invention is to propose an InSAR-based sliding surface displacement and residual friction inversion method to solve the technical problems that the existing InSAR technology is difficult to directly reflect the relationship between deep sliding surface displacement and sliding surface residual friction, and the inversion accuracy is not high.

[0006] Specifically, the present invention provides an InSAR-based sliding surface displacement and residual friction inversion method, comprising the following steps:

[0007] S1: Based on Sentinel-1 ascending and descending SAR data and ALOSDEM data, the LOS deformation is calculated using the SBAS-InSAR method;

[0008] S2: Combining satellite parameters and terrain characteristics, the LOS deformation is decomposed to obtain the normal deformation rate along the slope;

[0009] S3: Based on the decomposed deformation rate value along the slope normal, the physical model of the landslide sliding surface is constructed, the particle swarm optimization algorithm is improved, and the optimal sliding surface depth of the landslide is inverted;

[0010] S4: Based on the inverted optimal sliding surface depth value, a slope velocity and sliding surface velocity relationship model is constructed, and the optimal sliding surface velocity is obtained by inverting using genetic algorithm;

[0011] S5: Using the inverted optimal sliding velocity and the friction coefficient calculation model, the sliding surface residual friction is obtained.

[0012] A storage medium stores instructions and data for realizing an InSAR-based sliding surface displacement and residual friction inversion method.

[0013] A sliding surface displacement and residual friction inversion device based on InSAR comprises: a processor and the storage medium; the processor loads and executes instructions and data in the storage medium to implement a sliding surface displacement and residual friction inversion method based on InSAR.

[0014] The beneficial effects provided by the present invention are:

[0015] (1) Constructing a sliding surface depth model: The present invention combines a heuristic algorithm with the inversion of the landslide sliding surface, and quickly obtains continuous sliding surface data in a non-contact manner, thereby improving the accuracy, continuity, and timeliness of the inverted sliding surface, which has obvious advantages in particular for high-position remote landslides;

[0016] (2) Model for associating slope velocity with sliding surface displacement: This paper constructs a model that associates the landslide surface deformation information with the deep displacement of the sliding surface, and uses a genetic algorithm to invert the sliding surface displacement. This method improves the accuracy and robustness of the inversion by optimizing the search, and innovatively reveals the relationship between surface deformation and deep sliding. It not only improves the reliability of sliding surface displacement prediction, but also provides a new technical approach for landslide monitoring and early warning, and provides a scientific basis and technical support for the prevention and assessment of geological disasters;

[0017] (3) By combining the inverted sliding surface displacement data with the friction coefficient calculation model, the high-precision calculation of the sliding surface residual friction coefficient is achieved. This method combines the sliding surface mechanical characteristics with the inversion results of surface deformation information, and innovatively incorporates the dynamic changes of the sliding surface residual friction coefficient into the landslide behavior analysis. Through this model, not only the quantitative assessment ability of landslide stability and activity is improved, but also a more accurate and practical tool is provided for landslide risk prediction, which significantly improves the scientificity and effectiveness of landslide warning and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of the method of the present invention;

[0019] Figure 2 is the average annual deformation rate of the ascending and descending orbit;

[0020] Figure 3 It is a two-dimensional decomposition into the deformation rate along the slope normal; Figure 3 (a) deformation rate along the slope, (b) deformation rate in the normal direction;

[0021] Figure 4 is the optimal sliding surface depth after inversion;

[0022] Figure 5 is the residual between the inverted sliding surface displacement value, the simulated surface displacement value and the measured value;

[0023] Figure 6 It is the residual friction of sliding surface;

[0024] Figure 7 It is a schematic diagram of the working of the hardware device of an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0026] Before formally describing the present invention, the scheme of the present invention is first generally described for easy understanding.

[0027] Please refer to Figure 1The present invention provides a sliding surface displacement and residual friction inversion method based on InSAR, comprising the following steps:

[0028] S1: Based on Sentinel-1 ascending and descending SAR data and ALOSDEM data, the LOS deformation is calculated using the SBAS-InSAR method;

[0029] As an example, the Sentinel-1 satellite SAR data provided by the Copernicus program of the European Space Agency (ESA) is used in this embodiment, and the small baseline set SBAS-InSAR technology is used to accurately measure the deformation displacement and velocity of the landslide surface. At the same time, the DEM data provided by the Radar Topography Mission (SRTM) is also used. These data have a ground resolution of 30m and are used to eliminate surface effects and perform terrain adjustments.

[0030] In order to ensure the accuracy of the data processing results, the present invention selects a short-baseline interferometer pair, automatically sets the coherence coefficient threshold, and performs filtering processing, phase unwrapping, and eliminates orbit, atmosphere and terrain errors. Finally, the annual average deformation rate of the slope ascending and descending track data is obtained, ensuring the high quality of the results.

[0031] S2: Combining satellite parameters and terrain characteristics, the LOS deformation is decomposed to obtain the normal deformation rate along the slope;

[0032] It should be noted that the surface deformation obtained by SBAS-InSAR technology is the deformation information of the radar line of sight (LOS), which cannot accurately reflect the deformation characteristics of the real surface.

[0033] The invention combines information such as slope and aspect, satellite flight parameters and the like to decompose the deformation observed by synthetic aperture radar (SAR) into two main components, namely, along the slope and in the normal direction.

[0034] The along-slope component reflects the movement of the surface along the slope, which is crucial for analyzing the activity trend and speed of landslides, while the normal component represents the movement of the surface perpendicular to the slope. Through this two-dimensional decomposition, more accurate surface deformation information can be extracted from SAR data.

[0035] As an embodiment, first, the slope coordinate system is established using the slope and aspect information of the slope. The slope coordinate system ABC is established, and the right-hand spiral coordinate system is formed along the slope direction (OA), the normal direction (OC), and the perpendicular direction (OB). At the same time, it is defined that the downward movement along the slope direction axis is positive, and the movement along the normal direction axis in the sliding surface is positive. Generally speaking, when a landslide slides under gravity, the deformation amount moving downward along the sliding surface is much greater than the deformation amount perpendicular to the slope direction. Therefore, the present invention combines the Sentinel-1A lifting rail to solve the deformation formula along the slope direction and the normal direction as shown in formula (1):

[0036]

[0037] Where: a, b, c are the projection coefficients of deformation along the slope direction, along the vertical slope direction and along the normal direction respectively; D A , D B and D C represent the deformation along the slope direction OA, perpendicular to the slope direction OB, and normal to the slope direction OC respectively; α and β represent the average slope and slope angle of the landslide; θ and Represents the radar satellite incident angle and flight direction angle.

[0038] S3: Based on the decomposed deformation rate value along the slope normal, the physical model of the landslide sliding surface is constructed, the particle swarm optimization algorithm is improved, and the optimal sliding surface depth of the landslide is inverted;

[0039] It should be noted that the principle of mass conservation is the core theory of the sliding surface model. Its basic principle is that the mass in the landslide system remains unchanged in unit time, and there is no increase, decrease or loss of mass. For soil landslides, especially landslides that exhibit creep sliding characteristics, it is reasonable to assume that the landslide soil is incompressible, which means that the density of the landslide body remains constant during the sliding process. Based on this assumption, applying the principle of mass conservation to landslide modeling is not only feasible, but also can effectively describe the movement mechanism of the landslide body and the changes in the internal material distribution.

[0040] In the model construction, the present invention regards the entire landslide as a continuum composed of multiple control bodies, and divides the landslide into several small unit bodies, so that its movement process can be described more accurately. These control bodies are connected to each other, and their mass changes can be described by the mass conservation equation.

[0041] Furthermore, the vertical integration between the base sliding surface and the upper sliding surface of the landslide body is performed to obtain the optimal sliding surface depth value. The physical model of the landslide sliding surface is as follows:

[0042]

[0043] Where h is the depth of the control volume, the landslide body is composed of multiple control volumes, v represents the average deformation component, If the landslide sliding time per unit time is 0, it can be further simplified to That is, the rate of change of thickness is equal to the normal deformation rate.

[0044] Combining the kinematic boundary conditions with the fluid power law function, we can obtain:

[0045]

[0046] f is the rheological parameter, represents the speed of slope surface movement. It can be further rewritten as in represents the coefficient matrix including sampling interval, rheological parameters and slope surface deformation rate; Represents the sliding surface depth matrix combined with the regularized one-two norm, setting the objective function to: The improved particle swarm optimization algorithm is used for inverse calculation to obtain the optimal landslide depth value.

[0047] It should be noted that in a two-dimensional solution space, a group of N particles moves at a certain speed in space. Each particle will move towards its own best position (pbest) and the global best position (gbest) according to the historical information in the group to seek the optimal solution to the problem. The present invention uses the sliding surface model constructed by the mass conservation principle as the target equation and designs an optimized particle update rule, so as to better overcome the limitation that the traditional algorithm is prone to fall into the local optimal solution, and finally realize the search for the global optimal solution.

[0048] This rule improves the update formula of the classic particle swarm algorithm and reduces the proportion of inertia weight, so as to achieve a better balance between global search and local search capabilities.

[0049] Through this improvement, the global exploration ability of the algorithm can be effectively enhanced while retaining the necessary local optimization accuracy. The specific update rules are as follows:

[0050]

[0051] Where ω′ represents the updated inertia weight; ω is the inertia weight, which is used to balance the global and local search capabilities; σ * N(0.1) represents a random number between 0 and 1; ω min represents the minimum inertia weight; ω max represents the maximum inertia weight; represents the velocity of particle i at time t+1, c 1 and c 2 is the learning factor; p best Indicates the historical optimum point; represents the historical optimal position of particle i at time t; represents the sliding surface depth value of particle i at time t; r 1 and r 2 is a pseudo-random number between 0 and 1; represents the global optimal position, that is, the optimal sliding surface.

[0052] S4: Based on the inverted optimal sliding surface depth value, a slope velocity and sliding surface velocity relationship model is constructed, and the optimal sliding surface velocity is obtained by inverting using genetic algorithm;

[0053] It should be noted that when modeling the sliding surface depth, the landslide body is set as multiple continua, following the law of conservation of mass, and the landslide density remains unchanged. The flow rate of the solid in the landslide body changes slowly, and the rheological properties (such as viscosity and strength) of the landslide body material are relatively uniform. When the relationship between the velocity at the bottom of the landslide (sliding surface) and the velocity at the top (surface) is relatively simple, the shear deformation of the solid sliding surface can be approximately regarded as a linear distribution. The rate decay law is that the displacement rate shows a decay trend when it increases from the surface to the sliding surface depth, as shown in the following formula:

[0054] U s (x,y)=U b (x,y)+f(H(x,y),p)

[0055] U s (x, y) is the surface displacement of the landslide; U b (x, y) is the sliding surface displacement; f(H(x, y), p) is the distribution model of the displacement gradient, which depends on the landslide thickness H(x, y) and the model parameter p; where f(H, p) = αH(x, y), α is the shear coefficient, which determines the magnitude of the displacement gradient in the landslide body; it should be noted that the model derives the sliding surface displacement through the linear relationship between the surface displacement and the landslide thickness, which is convenient for processing the landslide displacement distribution under uniform shear conditions. Combined with the genetic algorithm, the optimal sliding surface displacement is inverted.

[0056] In addition, the genetic algorithm is a global optimization algorithm based on the biological evolution mechanism in nature. Its core idea is to simulate the natural selection and inheritance process, and continuously improve the fitness of individuals in the population through genetic operations such as selection, crossover and mutation, so as to gradually approach the optimal solution of the problem. The algorithm takes the initial population as the starting point, uses the fitness function to evaluate the quality of each individual, and preferentially retains individuals with higher fitness through selection operations, and then increases the diversity of the population through crossover and mutation to avoid falling into the local optimal solution. The genetic algorithm does not rely on the gradient information of the problem, has a strong global search capability and wide applicability, and is particularly suitable for solving nonlinear, multi-peak and complex constraint optimization problems, such as parameter optimization, path planning and hyperparameter adjustment in machine learning.

[0057] S5: Using the inverted optimal sliding velocity and the friction coefficient calculation model, the sliding surface residual friction is obtained.

[0058] It should be noted that the change in sliding surface friction is often considered to be an important feature that characterizes the gradual transition of the slope from locking to free sliding. This model is obtained by observing and counting a large amount of landslide data on the Earth and other planets, and can be widely used to describe the movement process of landslides of different scales.

[0059]

[0060] Where V is the sliding velocity of the slope along the sliding surface, V q is the velocity characteristic value after the ramp overcomes the static friction of the sliding surface. In order to compare the current sliding surface friction coefficient μ(V) with the steady-state friction coefficient μ w The difference, divided by the static friction coefficient μ of the sliding surface 0 and steady-state friction coefficient μ w The difference is converted into a function only related to the speed. The present invention defines a new function, called the friction residual W of the landslide, which converts the change of the sliding surface friction coefficient into a dimensionless value to characterize the friction residual degree of the landslide. This value only depends on the sliding speed V.

[0061] This transformation makes it easier to analyze the friction characteristics of a landslide, since all parameters related to the friction coefficient are simplified to a velocity-dependent function.

[0062]

[0063] When K>1, the landslide is in a stable state, and when K<1, the sliding body overcomes the static friction of the sliding surface, and the landslide is in an accelerated motion state. Based on the above-developed model, the present invention calculates the residual friction of the sliding surface based on this.

[0064] Embodiment 1:

[0065] 1. Based on Sentinel-1SAR data, the present invention uses SBAS-InSAR technology to extract the annual average deformation rate of the ascending and descending orbits. Combined with satellite parameters and landslide slope and direction, the LOS deformation rate of the ascending and descending orbits is decomposed into deformation along the slope direction and normal direction in two dimensions. Figure 2 is the average annual deformation rate of the ascending and descending orbit. Figure 3 It is a two-dimensional decomposition into deformation rates along the slope and in the normal direction.

[0066] 2. Based on the principle of mass conservation, a sliding surface model is established, and the optimal sliding surface depth is inverted using the improved particle swarm optimization algorithm. Combined with the surface digital elevation model, the optimal sliding surface is obtained. Figure 4 is the optimal sliding surface depth after inversion.

[0067] 3. Using the optimal sliding surface depth obtained by inversion, a model of the relationship between the surface velocity and the sliding surface velocity was constructed. The optimal sliding surface displacement was obtained by inversion using a genetic algorithm. In order to further quantitatively evaluate the reliability and accuracy of the inversion model, the sliding surface displacement value obtained by inversion was simulated to obtain the slope surface displacement, and the simulated slope surface displacement was compared with the measured slope surface displacement for residual estimation. The maximum residual value was 6 mm / year, which further illustrates the accuracy of the sliding surface displacement inversion proposed by the present invention. Figure 5 It is the residual between the inverted sliding surface displacement value, the simulated surface displacement value and the measured value.

[0068] 4. Based on the sliding surface displacement value and the friction coefficient calculation model, the sliding surface residual friction is calculated. Figure 6 Residual friction of sliding surface.

[0069] See also Figure 7 , Figure 7 4 is a schematic diagram of the working of the hardware device of an embodiment of the present invention, wherein the hardware device specifically comprises: an InSAR-based sliding surface displacement and residual friction inversion device 401, a processor 402 and a storage medium 403.

[0070] An InSAR-based sliding surface displacement and residual friction inversion device 401: The InSAR-based sliding surface displacement and residual friction inversion device 401 implements the InSAR-based sliding surface displacement and residual friction inversion method.

[0071] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the InSAR-based sliding surface displacement and residual friction inversion method.

[0072] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the InSAR-based sliding surface displacement and residual friction inversion method.

[0073] The beneficial effects of the present invention are:

[0074] (1) Constructing a sliding surface depth model: The present invention combines a heuristic algorithm with the inversion of the landslide sliding surface, and quickly obtains continuous sliding surface data in a non-contact manner, thereby improving the accuracy, continuity, and timeliness of the inverted sliding surface, which has obvious advantages in particular for high-position remote landslides;

[0075] (2) Model for associating slope velocity with sliding surface displacement: This paper constructs a model that associates the landslide surface deformation information with the deep displacement of the sliding surface, and uses a genetic algorithm to invert the sliding surface displacement. This method improves the accuracy and robustness of the inversion by optimizing the search, and innovatively reveals the relationship between surface deformation and deep sliding. It not only improves the reliability of sliding surface displacement prediction, but also provides a new technical approach for landslide monitoring and early warning, and provides a scientific basis and technical support for the prevention and assessment of geological disasters;

[0076] (3) By combining the inverted sliding surface displacement data with the friction coefficient calculation model, the high-precision calculation of the sliding surface residual friction coefficient is achieved. This method combines the sliding surface mechanical characteristics with the inversion results of surface deformation information, and innovatively incorporates the dynamic changes of the sliding surface residual friction coefficient into the landslide behavior analysis. Through this model, not only the quantitative assessment ability of landslide stability and activity is improved, but also a more accurate and practical tool is provided for landslide risk prediction, which significantly improves the scientificity and effectiveness of landslide warning and control.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A sliding surface displacement and residual friction inversion method based on InSAR, characterized by: The method comprises the following steps: S1: Based on Sentinel-1 ascending and descending SAR data and ALOSDEM data, the LOS deformation is calculated using the SBAS-InSAR method; S2: Combining satellite parameters and terrain characteristics, the LOS deformation is decomposed to obtain the normal deformation rate along the slope; S3: Based on the decomposed deformation rate value along the slope normal, the physical model of the landslide sliding surface is constructed, the particle swarm optimization algorithm is improved, and the optimal sliding surface depth of the landslide is inverted; S4: Based on the inverted optimal sliding surface depth value, a slope velocity and sliding surface velocity relationship model is constructed, and the optimal sliding surface velocity is obtained by inverting using genetic algorithm; S5: Using the inverted optimal sliding velocity and the friction coefficient calculation model, the sliding surface residual friction is obtained.

2. The method for inversion of sliding surface displacement and residual friction based on InSAR as claimed in claim 1, characterized in that: The calculation formula of the deformation rate along the slope normal in step S2 is as follows: Where a, b, and c are the projection coefficients of deformation along the slope direction, along the vertical slope direction, and along the normal direction, respectively; D A , D B and D C represent the deformation along the slope direction OA, perpendicular to the slope direction OB, and normal to the slope direction OC respectively; α and β represent the average slope and slope angle of the landslide; θ and Represents the radar satellite incident angle and flight direction angle.

3. The sliding surface displacement and residual friction inversion method based on InSAR as claimed in claim 1, characterized in that: The physical model of the landslide surface in step S3 is as follows: Where h is the depth of the control volume, the landslide body is composed of multiple control volumes, v represents the average deformation component, If the landslide sliding time per unit time is 0, it can be further simplified to That is, the rate of change of thickness is equal to the normal deformation rate.

4. The sliding surface displacement and residual friction inversion method based on InSAR as claimed in claim 3, characterized in that: In step S3, the optimal sliding surface depth of the landslide is inverted as follows: S31. Rewrite the landslide physical model by combining the kinematic boundary conditions and the fluid power law function to obtain: in f is the rheological parameter, Indicates the movement speed of the slope surface; S32, further replace the above formula into: in represents the coefficient matrix including sampling interval, rheological parameters and slope surface deformation rate; Represents the one-two norm of the sliding surface depth matrix combined with regularization; S33. Setting the objective function The improved particle swarm optimization algorithm is used for inverse calculation to obtain the optimal landslide depth value.

5. The method for inversion of sliding surface displacement and residual friction based on InSAR as claimed in claim 4, characterized in that: In step S33, the improved particle swarm optimization algorithm specifically reduces the inertia weight ratio, and the particle swarm update rule is as follows: Where ω′ represents the updated inertia weight; ω is the inertia weight, which is used to balance the global and local search capabilities; σ * N(0.1) represents a random number between 0 and 1; ω min represents the minimum inertia weight; ω max represents the maximum inertia weight; represents the velocity of particle i at time t+1, c1 and c2 are learning factors; p best Indicates the historical optimum point; represents the historical optimal position of particle i at time t; represents the sliding depth value of particle i at time t; r1 and r2 are pseudo-random numbers between 0 and 1; represents the global optimal position, that is, the optimal sliding surface.

6. The sliding surface displacement and residual friction inversion method based on InSAR as claimed in claim 1, characterized in that: Step S4 is specifically as follows: S41. Based on the conditional assumptions of the depth model, a relationship model between the slope velocity and the sliding surface velocity is constructed as follows: U s (x,y)=U b (x,y)+f(H(x,y),p) U s (x, y) is the surface displacement of the landslide; U b (x, y) is the displacement of the sliding surface; f(H(x, y), p) is the distribution model of the displacement gradient, which depends on the thickness of the landslide H(x, y) and the model parameter p; where f(H, p) = αH(x, y), α is the shear coefficient, which determines the magnitude of the displacement gradient within the landslide body; S42. The model derives the sliding surface displacement through the linear relationship between surface displacement and landslide thickness, and combines genetic algorithm to invert the optimal sliding surface displacement.

7. The method for inversion of sliding surface displacement and residual friction based on InSAR as claimed in claim 1, characterized in that: Step S5 is specifically as follows: S51. Construct the movement process of landslides of different scales: Where V is the sliding velocity of the slope along the sliding surface, V q It is the characteristic value of the speed after the ramp starts to overcome the static friction of the sliding surface; S52, the current sliding surface friction coefficient μ (V) and the steady-state friction coefficient μ w The difference is divided by the static friction coefficient μ0 and the steady-state friction coefficient μ w The difference is converted to a function related only to speed: W is the residual friction of the sliding surface; when K>1, the landslide is in a stable state, and when K<1, the sliding body overcomes the static friction of the sliding surface and the landslide is in an accelerated motion state.

8. A storage medium, characterized in that: The storage medium stores instructions and data for implementing the InSAR-based sliding surface displacement and residual friction inversion method described in any one of claims 1 to 8.

9. An InSAR-based sliding surface displacement and residual friction inversion device, characterized by: include: A processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement an InSAR-based sliding surface displacement and residual friction inversion method as described in any one of claims 1 to 8.

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