An inSAR-based sliding surface displacement and residual friction inversion method

Abstract

The present application relates to the field of geological disaster monitoring, disclose a kind of based on InSAR's sliding surface displacement and residual friction inversion method, method includes, based on lifting track SAR data and ALOS DEM data, using SBAS-InSAR method calculates LOS towards deformation;Combining satellite parameters and topographic features, the LOS towards deformation is decomposed to obtain along slope normal deformation rate;Based on the value of along slope normal deformation rate after decomposition, construct landslide sliding surface physical model, improve particle swarm optimization algorithm, inversion landslide optimal sliding surface depth;Based on the value of inversion optimal sliding surface depth, construct slope surface rate and sliding surface rate relationship model, using genetic algorithm is obtained optimal sliding surface rate by inversion;Using the optimal sliding surface rate of inversion, combined with friction coefficient calculation model, obtain sliding surface residual friction;The present application improves the quantitative evaluation ability to landslide stability and activity, provides more accurate and practical tool for landslide risk prediction.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring, and in particular to an inversion method for slip surface displacement and residual friction based on InSAR. Background Technology

[0002] Landslides, as a common geological hazard, pose a significant threat to human life, property, and the natural environment. Slope deformation is the most direct manifestation of landslide disasters, and analyzing slope deformation can provide some understanding of landslide mechanisms and potential hazards. However, as geological disaster researchers delve deeper into their studies, it has become increasingly clear that the surface deformation of most landslides, especially medium- to large-sized ones, is a direct reflection of the movement of the deep sliding surface. The instability risk of the landslide mass also largely depends on the motion state of the sliding surface and residual friction. However, due to the fact that landslide surfaces are often located in inaccessible deep parts of the slope, the complex characteristics of landslide motion, and the difficulty in directly determining the relationship between the deformation information of the landslide surface and the displacement field of the deep sliding surface, residual friction is difficult to assess. By studying the residual friction characteristics after sliding surface displacement, we can better assess the potential risks of landslides and take corresponding prevention and control measures. Therefore, accurate inversion research on the landslide surface displacement field and residual friction is of great significance.

[0003] Commonly used techniques for acquiring landslide surface movement characteristics mainly include Time Domain Reflectometry (TDR) and borehole inclinometer techniques. While these methods provide relatively reliable data, they are all contact-based measurements, requiring the deployment of multiple boreholes on the slope. Furthermore, large landslide displacements can damage the measuring instruments, necessitating repeated manual maintenance, resulting in high costs, time, and labor. Additionally, they only provide sparse observation data, failing 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), researchers can monitor surface deformation with unprecedented precision and range. InSAR analyzes the phase differences in radar signals reflected from satellites to the ground, enabling the detection of extremely small surface movements, thus effectively monitoring landslides and other surface deformation phenomena.

[0004] While InSAR technology is highly effective in monitoring surface deformation, it still faces challenges in revealing the deep-seated mechanisms of landslide movement. InSAR can only acquire the surface displacement field of a landslide, making it difficult to directly reflect deep-seated slip surface displacement and residual friction. This is mainly because deep-seated displacement has a relatively small impact on surface reflected signals, making it difficult to directly reflect these signals through radar. However, since InSAR only acquires the surface displacement field and cannot directly reflect deep-seated slip surface displacement and residual friction, constructing a model relating the surface displacement field to deep-seated slip surface displacement and residual friction is key to solving this problem. Furthermore, since inversion problems are often accompanied by uncertainties and multiple solutions, improving the accuracy of the inversion process is a critical challenge. Summary of the Invention

[0005] The purpose of this invention is to propose an InSAR-based method for inverting slip surface displacement and residual friction, thereby solving the technical problem that existing InSAR technology cannot directly reflect the relationship between deep slip surface displacement and residual friction, and the inversion accuracy is not high.

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

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

[0008] S2: Combining satellite parameters and terrain features, the LOS deformation decomposition yields the normal deformation rate along the slope.

[0009] S3: Based on the decomposed normal deformation rate along the slope, a physical model of the landslide surface is constructed, and the particle swarm optimization algorithm is improved to invert the optimal landslide surface depth.

[0010] S4: Based on the inverted optimal slip surface depth value, construct a model of the relationship between slope surface rate and slip surface rate, and use a genetic algorithm to invert and obtain the optimal slip surface rate;

[0011] S5: Using the inverted optimal sliding surface speed, combined with the friction coefficient calculation model, the residual friction of the sliding surface is obtained.

[0012] A storage medium storing instructions and data for implementing an InSAR-based method for inverting slip surface displacement and residual friction.

[0013] An InSAR-based slip surface displacement and residual friction inversion device includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement an InSAR-based slip surface displacement and residual friction inversion method.

[0014] The beneficial effects provided by this invention are:

[0015] (1) Constructing a slip surface depth model: This invention combines heuristic algorithms with landslide slip surface inversion, and obtains continuous slip surface data quickly in a non-contact manner, thereby improving the accuracy, continuity and timeliness of the inverted slip surface, especially for high-altitude long-distance landslides;

[0016] (2) Correlation Model between Slope Surface Rate and Slip Surface Displacement: This invention constructs a model that correlates landslide surface deformation information with deep slip surface displacement, and uses a genetic algorithm to invert slip surface displacement. This method improves the accuracy and robustness of the inversion through optimized search, and innovatively reveals the relationship between surface deformation and deep slip, which not only enhances the reliability of slip surface displacement prediction, but also provides a new technical approach for landslide monitoring and early warning, and provides scientific basis and technical support for the prevention and assessment of geological disasters;

[0017] (3) By combining inverted slip surface displacement data with a friction coefficient calculation model, high-precision calculation of the residual friction coefficient of the slip surface was achieved. This method combines the slip surface mechanical properties with the inversion results of surface deformation information, and innovatively incorporates the dynamic changes of the residual friction coefficient of the slip surface into the analysis of landslide behavior. Through this model, not only is the ability to quantitatively assess landslide stability and activity improved, but a more accurate and practical tool is also provided for landslide risk prediction, significantly improving the scientificity and effectiveness of landslide early warning and control. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0019] Figure 2 The average annual deformation rate of the lifting rail;

[0020] Figure 3 It is decomposed into two-dimensional deformation rates along the slope normal direction; Figure 3 (a) Slope-direction deformation rate and (b) Normal deformation rate;

[0021] Figure 4 The optimal slip surface depth after inversion;

[0022] Figure 5 The residuals between the inverted slip surface displacement values, simulated surface displacement values, and measured values;

[0023] Figure 6 Residual friction on the sliding surface;

[0024] Figure 7 This is a schematic diagram of the hardware device operation according to an embodiment of the present invention. Detailed Implementation

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

[0026] Before formally describing the present invention, a general description of the solution of the present invention will be given first to facilitate understanding.

[0027] Please refer to Figure 1This invention provides an InSAR-based method for inverting slip surface displacement and residual friction, comprising the following steps:

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

[0029] As one embodiment, this embodiment utilizes Sentinel-1 satellite SAR data provided by the European Space Agency (ESA) Copernicus program and applies the Small Baseline Set (SBAS) InSAR technique to accurately measure the deformation, displacement, and velocity of the landslide surface. Simultaneously, DEM data provided by the Radar Topographic Survey Mission (SRTM), with a ground resolution of 30m, is also used to eliminate surface effects and perform topographic adjustments.

[0030] To ensure the accuracy of the data processing results, this invention selected short baseline interferometric pairs, automatically set the coherence coefficient threshold, and performed filtering, phase unwrapping, and elimination of track, atmospheric, and terrain errors. Finally, the annual average deformation rate of the slope elevation and elevation data was obtained, ensuring high-quality results.

[0031] S2: Combining satellite parameters and terrain features, the LOS deformation decomposition yields the normal deformation rate along the slope.

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

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

[0034] The aspect component reflects the movement of the surface along the slope, which is crucial for analyzing the activity trend and velocity of landslides; while the normal component represents the movement of the surface perpendicular to the slope. This two-dimensional decomposition allows for the extraction of more accurate surface deformation information from SAR data.

[0035] As one embodiment, the slope and aspect information of the slope body is first used to establish a slope coordinate system. A slope coordinate system ABC is established, with the slope aspect (OA), normal aspect (OC), and perpendicular to the slope aspect (OB) forming a right-handed spiral coordinate system. At the same time, downward movement along the slope aspect axis is defined as positive, and movement within the sliding plane along the normal aspect axis is defined as positive. Generally speaking, when a landslide slides under gravity, the deformation along the sliding plane downward is much greater than the deformation perpendicular to the slope aspect. Therefore, this invention combines the Sentinel-1A lifting rail to calculate the deformation along the slope aspect and normal aspect as shown in equation (1):

[0036]

[0037] In the formula: a, b, and c are the projection coefficients of deformation along the slope direction, along the perpendicular 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 direction OC, respectively; α and β represent the average slope and slope direction angle of the landslide body; θ and This represents the radar satellite's incident angle and flight direction angle.

[0038] S3: Based on the decomposed normal deformation rate along the slope, a physical model of the landslide surface is constructed, and the particle swarm optimization algorithm is improved to invert the optimal landslide surface depth.

[0039] It should be noted that the principle of mass conservation is the core theory for establishing landslide models. Its basic principle is that the mass of a landslide system remains constant per unit time; there is no increase, decrease, or loss of mass. For soil landslides, especially those exhibiting creep sliding characteristics, it is reasonable to assume that the landslide soil is incompressible, meaning that the density of the landslide mass remains constant throughout the sliding process. Based on this assumption, applying the principle of mass conservation to landslide modeling is not only feasible but also effectively describes the movement mechanism and internal material distribution changes of the landslide mass.

[0040] In this invention, the entire landslide is considered as a continuum composed of multiple control volumes during model construction. By dividing the landslide into several smaller unit volumes, its motion process can be described more accurately. These control volumes are interconnected, and their mass changes can be described by the mass conservation equation.

[0041] Furthermore, by integrating in the vertical direction between the base sliding surface and the upper sliding surface of the landslide body, the optimal sliding surface depth value is obtained. The physical model of the landslide surface is as follows:

[0042]

[0043] Where h is the depth of the control volume, the landslide body is composed of multiple control volumes, and v represents the average deformation component. If the landslide sliding is 0 per unit time, then 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 and the fluid power-law function, we can obtain:

[0045]

[0046] f is a rheological parameter. This represents the velocity of the slope surface movement. It can be further rewritten as... in This represents a coefficient matrix that includes the sampling interval, rheological parameters, and slope surface deformation rate. The objective function is set as follows: (This implies that the surface depth matrix is ​​combined with the first and second normal forms of regularization.) An improved particle swarm optimization algorithm was used for inversion calculation to obtain the optimal landslide depth value.

[0047] It should be noted that in a two-dimensional solution space, a swarm of N particles moves at a certain speed. Each particle moves towards its own best position (pbest) and the global best position (gbest) based on historical information from the swarm, seeking the optimal solution to the problem. This invention uses a smooth surface model constructed based on the principle of mass conservation as the objective equation and designs an optimized particle update rule to better overcome the limitation of traditional algorithms that are prone to getting trapped in local optima, ultimately achieving the search for the global optimal solution.

[0048] This rule improves the update formula of the classic particle swarm optimization algorithm by reducing the proportion of inertia weight, thereby achieving a better balance between global search and local search capabilities.

[0049] This improvement effectively enhances the algorithm's global exploration capability while retaining 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, used to balance the global and local search capabilities; σ * N(0.1) represents a random number between 0 and 1; ω min ω represents the minimum inertia weight; max Indicates the maximum inertia weight; Let p represent the velocity of particle i at time t+1, and c1 and c2 be learning factors; best Indicates the historical best point; This represents the historical optimal position of particle i from time t. R1 and R2 represent the slip surface depth of particle i at time t; R1 and R2 are pseudo-random numbers in the range [0-1]. This represents the globally optimal position, i.e., the optimal sliding surface.

[0052] S4: Based on the inverted optimal slip surface depth value, construct a model of the relationship between slope surface rate and slip surface rate, and use a genetic algorithm to invert and obtain the optimal slip surface rate;

[0053] It should be noted that when modeling the slip surface depth, the landslide body is treated as multiple continuums, following the law of mass conservation. The landslide density remains constant, the flow rate within the landslide body changes relatively slowly, and the rheological properties (such as viscosity and strength) of the landslide material are relatively uniform. Given the simple relationship between the velocity at the bottom (slip surface) and the velocity at the top (surface), the shear deformation of the solid slip surface can be approximated as a linear distribution. The rate decay law states that the displacement rate decreases as the slip surface depth increases from the surface, as shown in the following equation:

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

[0055] U s (x,y) represents the surface displacement of the landslide body; U b (x,y) represents the slip surface displacement; f(H(x,y),p) is the displacement gradient distribution model, dependent on the landslide thickness H(x,y) and the model parameter p; where f(H,p) = αH(x,y), α is the shear coefficient, determining the magnitude of the displacement gradient within the landslide. It should be noted that this model derives the slip surface displacement through the linear relationship between surface displacement and landslide thickness, facilitating the handling of landslide displacement distribution under uniform shear conditions. The optimal slip surface displacement is then retrieved using a genetic algorithm.

[0056] Furthermore, genetic algorithms are global optimization algorithms based on the biological evolution mechanisms of nature. Their core idea is to simulate natural selection and genetic processes, continuously improving the fitness of individuals in the population through genetic operations such as selection, crossover, and mutation, thereby gradually approaching the optimal solution to the problem. The algorithm starts with an initial population, uses a fitness function to evaluate the quality of each individual, prioritizes retaining individuals with higher fitness through selection, and increases population diversity through crossover and mutation to avoid getting trapped in local optima. Genetic algorithms do not rely on gradient information of the problem, possess powerful global search capabilities and wide applicability, and are particularly suitable for solving nonlinear, multi-peak, and complex constraint optimization problems, such as parameter optimization, path planning, and hyperparameter tuning in machine learning.

[0057] S5: Using the inverted optimal sliding surface speed, combined with the friction coefficient calculation model, the residual friction of the sliding surface is obtained.

[0058] It should be noted that changes in sliding surface friction are often considered an important characteristic of the gradual transition of a slope from locked to free sliding. This model, derived from extensive observational and statistical analysis of landslide data on Earth and other planets, can be widely applied to describe the motion processes of landslides at different scales.

[0059]

[0060] Where V is the sliding velocity of the slope along the sliding surface, V q This is the velocity characteristic value after the slope overcomes the static friction of the sliding surface to start. 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 μ0 and the steady-state friction coefficient μ w The difference is converted into a function that is only related to velocity. This invention defines a new function called the frictional residual of the landslide, W, which converts the change in the friction coefficient of the sliding surface into a dimensionless value that characterizes the degree of frictional residual of the landslide. This value depends only on the sliding velocity V.

[0061] This transformation makes it easier to analyze the frictional characteristics of landslides, as it simplifies all parameters related to the friction coefficient into a function related to velocity.

[0062]

[0063] When K > 1, the landslide is in a stable state; 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, this invention calculates the residual friction of the sliding surface.

[0064] Example 1:

[0065] 1. This invention is based on Sentinel-1 SAR data and uses SBAS-InSAR technology to extract the annual average deformation rate of the ascending and descending orbits. Combining satellite parameters and landslide slope aspect, the deformation rate of the ascending and descending orbits in the LOS direction is decomposed in two dimensions into deformation along the slope direction and normal direction. Figure 2 The average annual deformation rate of the lifting rail. Figure 3 It is a two-dimensional decomposition into the deformation rate along the slope and the normal direction.

[0066] 2. A slip surface model is established based on the principle of mass conservation, and the optimal slip surface depth is obtained by using an improved particle swarm optimization algorithm. Combined with the surface digital elevation model, the optimal slip surface is obtained. Figure 4 This represents the optimal slip surface depth after inversion.

[0067] 3. Using the optimal slip surface depth obtained through inversion, a model relating surface velocity and slip surface velocity is constructed. The optimal slip surface displacement is then obtained using a genetic algorithm. To further quantitatively evaluate the reliability and accuracy of the inversion model, the slope surface displacement is simulated using the inverted slip surface displacement values. Residual estimation is performed between the simulated and measured slope surface displacements. The maximum residual value is 6 mm / year, further demonstrating the accuracy of the slip surface displacement inversion proposed in this invention. Figure 5 The residuals between the inverted slip surface displacement value, the simulated value of the surface displacement, and the measured value.

[0068] 4. Based on the sliding surface displacement value, the residual friction of the sliding surface is calculated using a friction coefficient calculation model. Figure 6 This refers to residual friction on the sliding surface.

[0069] Please see Figure 7 , Figure 7 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: an InSAR-based sliding surface displacement and residual friction inversion device 401, a processor 402, and a storage medium 403.

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

[0073] The beneficial effects of this invention are:

[0074] (1) Constructing a slip surface depth model: This invention combines heuristic algorithms with landslide slip surface inversion, and obtains continuous slip surface data quickly in a non-contact manner, thereby improving the accuracy, continuity and timeliness of the inverted slip surface, especially for high-altitude long-distance landslides;

[0075] (2) Correlation Model between Slope Surface Rate and Slip Surface Displacement: This invention constructs a model that correlates landslide surface deformation information with deep slip surface displacement, and uses a genetic algorithm to invert slip surface displacement. This method improves the accuracy and robustness of the inversion through optimized search, and innovatively reveals the relationship between surface deformation and deep slip, which not only enhances the reliability of slip surface displacement prediction, but also provides a new technical approach for landslide monitoring and early warning, and provides scientific basis and technical support for the prevention and assessment of geological disasters;

[0076] (3) By combining inverted slip surface displacement data with a friction coefficient calculation model, high-precision calculation of the residual friction coefficient of the slip surface was achieved. This method combines the slip surface mechanical properties with the inversion results of surface deformation information, and innovatively incorporates the dynamic changes of the residual friction coefficient of the slip surface into the analysis of landslide behavior. Through this model, not only is the ability to quantitatively assess landslide stability and activity improved, but a more accurate and practical tool is also provided for landslide risk prediction, significantly improving the scientificity and effectiveness of landslide early 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An InSAR-based sliding surface displacement and residual friction inversion method, characterized in that: The method comprises the following steps: S1: based on Sentinel-1 ascending / descending track SAR data and ALOS DEM data, using SBAS-InSAR method to calculate LOS deformation; S2: combined with satellite parameters and terrain features, the LOS deformation is decomposed to obtain the along-slope normal deformation rate; S3: based on the along-slope normal deformation rate value after decomposition, a landslide slip surface physical model is constructed, and a particle swarm optimization algorithm is improved to invert the optimal slip surface depth of the landslide; S4: based on the inverted optimal slip surface depth value, a slope surface rate and slip surface rate relationship model is constructed, and the optimal slip surface rate is inverted using a genetic algorithm; Step S4 is as follows: S41: based on the conditional assumption of the depth model, a slope surface rate and slip surface rate relationship model is constructed, as follows: U s ( x , y ) is the surface displacement of the landslide; U b ( x , y ) is the displacement of the slip surface; 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; wherein, , α is the shear coefficient, which determines the size of the displacement gradient in the landslide body; S42: the model derives the slip surface displacement through the linear relationship between the surface displacement and the landslide thickness, and the optimal slip surface displacement is inverted combined with the genetic algorithm; S5: using the inverted optimal slip surface rate, a slip surface residual friction is calculated combined with a friction coefficient calculation model.

2. The InSAR-based sliding surface displacement and residual friction inversion method of claim 1, wherein: The along-slope normal deformation rate calculation formula in step S2 is as follows: where, a , b , c are the projection coefficients along the slope direction, the perpendicular slope direction and the normal direction, respectively; D A , D B and D C represent the deformation along the slope direction OA, the perpendicular slope direction OB and the normal direction OC, respectively; α and β represent the average slope of the landslide and the slope direction angle; θ and φ represent the radar satellite incidence angle and the flight direction angle.

3. The InSAR-based sliding surface displacement and residual friction inversion method of claim 1, wherein: The landslide slip surface physical model in step S3 is as follows: wherein, h To control the body depth, the landslide body is composed of multiple control bodies, v represents the average deformation component, If the landslide slips 0 per unit time, it is further simplified as That is, the thickness change rate is equal to the normal deformation rate.

4. The InSAR-based sliding surface displacement and residual friction inversion method of claim 3, wherein: In step S3, the optimal slip surface depth of the landslide is inverted as follows: S31, the landslide physical model is combined with the kinematic boundary condition and the fluid power law function to be rewritten to obtain: ; wherein f is a rheological parameter, denotes the slope surface movement velocity; S32, further replace the above formula into: ; wherein denotes a coefficient matrix comprising the sampling interval, the rheological parameters and the surface deformation rate of the slope; denotes a regularized one-norm of the slip surface depth matrix; S33, set the target function The improved particle swarm optimization algorithm is used to carry out inversion calculation to obtain the optimal landslide depth value.

5. The InSAR-based sliding surface displacement and residual friction inversion method of claim 4, wherein: In step S33, the improved particle swarm optimization algorithm specifically reduces the inertia weight proportion, and the particle swarm update rule is as follows: where denotes the updated inertia weight; is the inertia weight, which is used to balance the ability of global and local search; denotes a random number between 0 and 1; denotes the minimum inertia weight; denotes the maximum inertia weight; denotes the particle i at the time t +1; c 1 and c 2 are learning factors; p best denotes the historical best point; denotes the historical best position of the particle i at the time t ; denotes the particle i at the time t ; r 1 and r 2 are pseudo random numbers between [0-1]; denotes the global best position, i.e. the best slide surface.

6. The InSAR-based sliding surface displacement and residual friction inversion method of claim 1, wherein: Step S5 is as follows: S51: the motion process of a landslide of different scales is constructed: wherein V is the sliding velocity of the slope along the sliding surface, V q is the velocity characteristic value of the slope after overcoming the static friction of the sliding surface S52, the difference between the current slip surface friction coefficient μ V ) and the steady state friction coefficient μ w divided by the slip surface static friction coefficient μ 0 and the steady state friction coefficient μ w is converted into a function that is only dependent on the velocity:​ W is the residual friction on the slip surface; when K > 1, the landslide is in a stable state, when K < 1, the landslide is in an accelerated motion state.

7. A storage medium characterized by: The storage medium stores instructions and data for realizing the landslide slip surface displacement and residual friction inversion method based on InSAR according to any one of claims 1-6.

8. An InSAR-based sliding surface displacement and residual friction inversion apparatus, characterized by: It comprises: A processor and a storage medium; the processor loads and executes the instructions and data in the storage medium to realize the landslide slip surface displacement and residual friction inversion method based on InSAR according to any one of claims 1-6.

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