Method for analyzing applicability of landslide instability time prediction based on insar monitoring data

Abstract

The present application relates to the field of geological disaster prevention method, specifically relates to the applicability analysis method for landslide instability time prediction based on InSAR monitoring data, the sensitivity of target landslide hidden danger point from multiple SAR satellite systems, the time series deformation monitoring data that can be obtained in the accelerated creep stage and the accuracy of monitoring data three aspects are quantitatively evaluated for the applicability of landslide instability time prediction based on InSAR monitoring data, which provides the basis for landslide hidden danger point classification and grading monitoring. The present application realizes the effective improvement of landslide hidden danger point monitoring quantity and the substantial reduction of monitoring cost.

Description

Technical Field

[0001] This invention relates to the field of geological disaster prevention and control methods, specifically to an applicability analysis method for predicting landslide instability time based on InSAR monitoring data. Background Technology

[0002] Geological disasters are natural factors or human activities that endanger life and property, including landslides, mudslides, debris flows, ground subsidence, ground fissures, and ground settlement. These disasters cause thousands of casualties and billions of yuan in economic losses annually, with landslides and collapses accounting for over 80% of these losses. Monitoring landslide hazard points and predicting their instability and failure time are effective means to minimize the losses caused by geological disasters.

[0003] Currently, for high-risk landslide hazard points, point monitoring equipment such as GNSS and crack gauges are commonly used to monitor the deformation of the landslide surface. Based on the time series data of landslide surface deformation obtained from the monitoring, the instability time of the landslide is predicted, which plays a positive role in the prevention and control of landslide disasters and the protection of life and property.

[0004] However, due to limitations such as monitoring costs, installing monitoring equipment at every potential landslide site is neither reasonable nor practical. In recent years, with advancements in remote sensing technology, space-based Earth observation technologies such as Synthetic Aperture Radar (InSAR), employing interferometric techniques, have emerged, making it possible to monitor landslide surface deformation at low cost and predict instability and failure times. However, due to the characteristics of InSAR monitoring, such as satellite revisit periods and phase confusion, as well as limitations imposed by factors like incident angle and vegetation influence, not all landslides can be predicted and forecasted entirely using InSAR monitoring data. Therefore, it is necessary to distinguish under what conditions, while maintaining accuracy in predicting landslide instability times, landslides can be predicted and forecasted at low cost entirely using InSAR monitoring data. Furthermore, it is crucial to classify and tier landslide hazard points for monitoring, thereby effectively increasing the number of monitored landslide hazard points while significantly reducing monitoring costs. Summary of the Invention

[0005] The present invention aims to provide an applicability analysis method for predicting landslide instability time based on InSAR monitoring data, in order to solve the problem of the unknown applicability of landslide instability time prediction based on InSAR monitoring data.

[0006] The applicability analysis of the landslide instability time prediction method based on InSAR monitoring data in this scheme includes the following steps: Step S1: Obtain topographic and geological data of the target landslide hazard point, and determine the deformation and failure mechanism of the target landslide hazard point based on the topographic and geological data of the target landslide hazard point. Also includes: Step S2: Based on the geographical coordinates of the target landslide hazard point, obtain m SAR satellite systems that pass over the target landslide hazard point at a constant period and whose service coverage includes the entire landslide body of the target landslide hazard point, as well as the revisit period, wavelength, and average incident angle of the SAR satellite systems. Step S3: For any SAR satellite system, calculate the local effective incident angle of the SAR satellite system on the surface of the target landslide hazard point based on the preset method A, and obtain the sensitivity index of the SAR satellite system to the target landslide hazard point based on the preset method B. Step S4: Based on the preset method C, obtain the duration DT of the accelerated creep stage of the target landslide hazard point and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage; Step S5: For any SAR satellite system, obtain the sufficiency index of the amount of data of the SAR satellite system for the target landslide hazard point based on the preset method D; Step S6: For any SAR satellite system, obtain the interferometric phase continuity index of the SAR satellite system for the target landslide hazard point based on the preset method E; Step S7, for any SAR satellite system, based on sensitivity indicators Data sufficiency indicators Interference phase continuity index Let i represent the i-th SAR satellite system, and let i represent the applicability of SAR satellite system monitoring data to predict the instability time of target landslide hazard points. The calculation is performed using the following formula:

[0007] ;

[0008] S8, For the target landslide hazard point, the applicability F of landslide instability time prediction based on InSAR monitoring data is calculated, and the calculation formula is as follows: .

[0009] The beneficial effects of this plan are: By using SAR satellite systems that pass over target landslide hazard points at a constant period and whose service coverage includes the entire landslide area, it is possible to ensure that SAR satellite systems can stably provide time-series deformation monitoring data for the target landslide hazard points. Acquiring m SAR satellite systems allows for the evaluation of the applicability of SAR satellite system monitoring data for landslide instability time prediction for multiple passing target landslide hazard points. Based on data from the acquired m SAR satellite systems regarding their sensitivity to target landslide hazard points, the time-series deformation monitoring data that can be obtained during the accelerated creep phase, and the accuracy of the monitoring data, the applicability of landslide instability time prediction was quantitatively evaluated. This provides a basis for the classification and grading of landslide hazard point monitoring, effectively increasing the number of landslide hazard point monitoring points and significantly reducing monitoring costs.

[0010] Furthermore, in step S1, the topographic and geological data include the geometric center elevation, slope aspect, average slope, average plane curvature, average profile curvature, and slope lithology of the target landslide hazard point.

[0011] The beneficial effect is that the content included in the topographic and geological data can improve the completeness of the data in representing the actual geological conditions.

[0012] Furthermore, the deformation and failure mechanism of the target landslide hazard point is one of the following: traction landslide or push-type landslide.

[0013] The beneficial effect is that the setting of deformation failure mechanism can enable the method to cover deformation landslide results caused by different mechanisms, thereby improving the accuracy of the analysis results.

[0014] Furthermore, in step S3, the preset method A includes the following steps: Step S3A-1: For any SAR satellite system, based on the local effective incident angle geometric model and spatial geometric relationship, calculate the average slope aspect angle δ of the slope surface at the target landslide hazard point and the horizontal angle ω of the line of sight of the SAR satellite system; Step S3A-2: Calculate the local effective incident angle on the slope surface of the target landslide hazard point. The formula is:

[0015] ;

[0016] In the formula, α is the average slope of the target landslide hazard point.

[0017] The beneficial effect is that by calculating the local effective incident angle, the monitoring data acquisition at landslide hazard points can be accurately characterized in real time.

[0018] Furthermore, in step S3, the preset method B includes the following steps: Step S3B-1: For any SAR satellite system, based on step S3A-2, obtain the local effective incident angle of the slope surface at the target landslide hazard point. ;

[0019] Step S3B-2: Calculate the sensitivity index of the SAR satellite system to the target landslide hazard point. The formula is:

[0020] ;

[0021] In the formula, the sensitivity index A value of 1 indicates that accurate and effective time-series deformation monitoring data of target landslide hazard points can be obtained based on the SAR satellite system; sensitivity index A value of 0 indicates that the time-series deformation monitoring data of the target landslide hazard points obtained based on the SAR satellite system cannot meet the accuracy requirements for predicting the landslide instability time.

[0022] The beneficial effect is that, based on the calculation of sensitivity indicators by the local effective incident angle, the SAR system can accurately quantify the instability data collection of target landslide hazard points.

[0023] Furthermore, in step S4, the preset method C includes the following steps: Step S4C-1: Collect data on landslides that have already failed, including: landslide topography and geomorphology, geological conditions, deformation and failure mechanisms of the landslides that have failed, and deformation monitoring data during the landslide deformation and failure process. The deformation monitoring data includes the duration of the accelerated creep stage DT and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage. ;

[0024] Step S4C-2 takes the landslide topography, geological conditions, and deformation failure mechanism of the landslide that has become unstable as input from Step S4C-1, and uses the duration DT of the accelerated creep stage and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage from Step S4C-1 as input. Using these as labels, machine learning models were constructed to predict the duration DT of the accelerated creep stage in landslides. And predict the average deformation rate of the landslide surface at time DT / 2 during the accelerated creep stage of the landslide. machine learning models and for machine learning models and machine learning models Training and validation test datasets and ;

[0025] Step S4C-3, respectively, for the machine learning model and machine learning models Train, validate, and test to optimize the machine learning model. and machine learning models The parameters in the model are used to obtain the trained machine learning model. and ;

[0026] Step S4C-4: For the target landslide hazard point, the topographic data, geological conditions, and deformation and failure mechanisms of the target landslide hazard point obtained in step S1 are used as the machine learning model obtained in step S4C-3. and The input, and based on the machine learning model and The duration of the accelerated creep stage DT and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage were analyzed at the target landslide hazard point. The prediction was performed to obtain the duration DT of the accelerated creep stage at the target landslide hazard point and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage. .

[0027] The beneficial effects are: based on the topography, geological conditions and deformation failure mechanism of the target landslide hazard point, the duration and deformation rate of the accelerated deformation stage of the target landslide hazard point can be predicted, providing a basis for the applicability analysis of landslide instability time prediction based on InSAR monitoring data.

[0028] Furthermore, in step S4C-1, the number of landslides that have already become unstable and failed is greater than 50, and the landslide deformation and failure mechanisms include both traction landslides and shove landslides.

[0029] The beneficial effect is that by setting the number of landslides, the duration of the accelerated creep stage DT and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage are ensured. The accuracy of the prediction.

[0030] Furthermore, in step S5, the preset method D includes the following steps: Step S5D-1: For any SAR satellite system, calculate the number of landslide surface deformation time-series data that can be obtained from InSAR monitoring data at time DT / 2 during the accelerated creep phase of the target landslide hazard point. The formula is:

[0031] ,in, The revisit period for the i-th SAR satellite system;

[0032] Step S5D-2: Calculate the sufficiency index of the SAR satellite system for the target landslide hazard points. The formula is:

[0033] ;

[0034] In the formula, FS is the safety factor. 1; A value of 1 indicates the minimum amount of time-series deformation monitoring data of the target landslide hazard point that can be obtained based on the SAR satellite system to meet the requirements for predicting the instability time of the target landslide hazard point. A value of 0 indicates that the amount of time-series deformation monitoring data of the target landslide hazard point obtained based on the SAR satellite system cannot meet the minimum data requirement for predicting the instability time of the target landslide hazard point.

[0035] The beneficial effects are: by setting a safety factor FS, under the premise of ensuring that the instability time prediction of the target landslide hazard point is met, the amount of landslide surface deformation time series data obtained from InSAR monitoring data during the accelerated creep stage is adjusted according to the socio-economic value of the target landslide hazard point, thereby improving the accuracy of the instability time prediction of the target landslide hazard point. Specifically, the more landslide surface deformation time series data there is, the higher the accuracy of the instability time prediction of the target landslide hazard point.

[0036] Furthermore, in step S6, the preset method E includes the following steps: Step S6E-1: For any SAR satellite system, calculate the maximum deformation rate that the SAR satellite system can detect. The formula is:

[0037] In the formula, The wavelength of the i-th SAR satellite system;

[0038] Step S6E-2: Calculate the interferometric phase continuity index of the SAR satellite system for the target landslide hazard point. The formula is:

[0039] ;

[0040] A value of 1 indicates that the SAR satellite system can effectively and accurately measure the deformation at time DT / 2 during the accelerated creep stage of the target landslide hazard point. A value of 0 indicates that when the SAR satellite system measures the surface deformation of the target landslide hazard point at time DT / 2 during the accelerated creep stage, an interference phase aliasing phenomenon will occur, making it impossible to effectively and accurately measure the magnitude of the surface deformation of the target landslide hazard point.

[0041] The beneficial effect is that, based on the calculation of the maximum deformation rate, comparing the maximum deformation rate with the average deformation rate to calculate the interferometric phase continuity index can improve the accuracy of the interferometric quantization calculation results. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of an embodiment of the applicability analysis method for predicting landslide instability time based on InSAR monitoring data; Figure 2 This is a geometric model diagram of the local effective incident angle in an embodiment of the applicability analysis method for predicting landslide instability time based on InSAR monitoring data. Detailed Implementation

[0043] The following detailed description provides further details on specific implementation methods.

[0044] Example Applicability analysis of landslide instability time prediction methods based on InSAR monitoring data, such as Figure 1 As shown, it includes the following steps: Step S1: Obtain topographic and geological data of the target landslide hazard point, and determine the deformation and failure mechanism of the target landslide hazard point based on the topographic and geological data. The topographic and geological data include the geometric center elevation, slope aspect, average slope, average plane curvature, average profile curvature, and slope lithology of the target landslide hazard point. The deformation and failure mechanism of the target landslide hazard point is either a traction landslide or a push-movement landslide.

[0045] Step S2: Based on the geographic coordinates of the target landslide hazard point, acquire m SAR satellite systems that pass over the target landslide hazard point at a constant period and whose service coverage includes the entire landslide body of the target landslide hazard point. and SAR satellite system revisit cycle ,wavelength and the average incident angle of the sensor θ ,in A SAR satellite system that passes over a target landslide hazard point with a constant period and whose service coverage includes the entire landslide body at the target landslide hazard point can ensure that it can stably provide time-series deformation monitoring data for the target landslide hazard point; acquiring m SAR satellite systems can evaluate the applicability of SAR satellite system monitoring data for multiple passing target landslide hazard points for landslide instability time prediction.

[0046] Step S3, for any SAR satellite system SAR satellite system calculation based on preset method A Local effective angle of incidence on the surface of the target landslide hazard point Based on preset method B, the sensitivity index of the SAR satellite system to the target landslide hazard point was obtained. Based on such Figure 2 The model shown, in which, Let α be the incident angle of the satellite sensor, and α be the average slope of the target landslide hazard point. This is the effective incident angle at a local point on the ground. and These are the slope angle at the ground point and the horizontal angle along the satellite line of sight, respectively, with true north as the 0° direction and clockwise rotation as the positive direction; , , These are the satellite line-of-sight vector, the ground point normal vector, and the resultant vector of the satellite line-of-sight vector and the ground point normal vector, respectively. Preset method A includes the following steps:

[0047] Step S3A-1, for any SAR satellite system Based on the local effective incident angle geometric model and spatial geometric relationship, the average slope aspect angle δ of the slope surface at the target landslide hazard point and the horizontal angle ω of the line of sight of the SAR satellite system are calculated.

[0048] Step S3A-2: Calculate the local effective incident angle on the slope surface of the target landslide hazard point. The formula is:

[0049] ;

[0050] In the formula, α is the average slope of the target landslide hazard point.

[0051] Preset method B includes the following steps: Step S3B-1, for any SAR satellite system Based on step S3A-2, the local effective incident angle of the slope surface at the target landslide hazard point is obtained. ;

[0052] Step S3B-2, Calculate the SAR satellite system Sensitivity indicators for target landslide hazard points The formula is:

[0053] ;

[0054] In the formula, the sensitivity index A value of 1 indicates a SAR satellite system. It can obtain accurate and effective time-series deformation monitoring data of target landslide hazard points; sensitivity indicators A value of 0 indicates a SAR satellite system. The obtained time-series deformation monitoring data of the target landslide hazard points cannot meet the accuracy requirements for predicting the instability time of landslides.

[0055] Step S4: Based on the preset method C, obtain the duration DT of the accelerated creep stage at the target landslide hazard point and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage. The preset method C includes the following steps:

[0056] Step S4C-1: Collect data on landslides that have already failed. The number of landslides that have failed is greater than 50, and the landslide deformation and failure mechanisms include both traction-type and shove-type landslides. This includes: landslide topography and geological data, deformation and failure mechanisms of the failed landslides, and deformation monitoring data during the landslide deformation and failure process. The deformation monitoring data includes the duration of the accelerated creep stage (DT) and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage. ;

[0057] Step S4C-2 takes the landslide topography, geological conditions, and deformation failure mechanism of the landslide that has become unstable as input from Step S4C-1, and uses the duration DT of the accelerated creep stage and the average deformation rate from Step S4C-1 as input. Using the distribution as labels, machine learning models are constructed to predict the duration DT of the accelerated creep stage in landslides. And predict the average deformation rate of the landslide surface at time DT / 2 during the accelerated creep stage of the landslide. machine learning models and for machine learning models and machine learning models Training and validation test datasets and ;

[0058] The machine learning model and machine learning models This is a machine learning regression model, namely one of the following: random forest, extreme gradient boosting, support vector machine, and artificial neural network model. The relevant model algorithms have been publicly disclosed in the industry, and will not be elaborated on in this article.

[0059] Step S4C-3, respectively, for the machine learning model and machine learning models Train, validate, and test to optimize the machine learning model. and machine learning models The parameters in the model are used to obtain the trained machine learning model. and ;

[0060] Optimize machine learning models and machine learning models The optimization algorithms used are commonly employed in the industry, such as particle swarm optimization, Bayesian optimization, and grid search. These algorithms are already publicly available in the industry and will not be elaborated upon in this article.

[0061] Step S4C-4: For the target landslide hazard point, the topographic data, geological conditions, and deformation and failure mechanisms of the target landslide hazard point obtained in step S1 are used as the machine learning model obtained in step S4C-3. and The input, and based on the machine learning model and The duration of the accelerated creep stage DT and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage were analyzed at the target landslide hazard point. The prediction was performed to obtain the duration DT of the accelerated creep stage at the target landslide hazard point and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage. .

[0062] Step S5, for any SAR satellite system Based on the preset method D, the sufficiency index of the SAR satellite system for the target landslide hazard points is obtained. The preset method D includes the following steps:

[0063] Step S5D-1, for any SAR satellite system Calculate the number of landslide surface deformation time-series data available from InSAR monitoring data at time DT / 2 during the accelerated creep stage of the target landslide hazard point. The formula is:

[0064] ,in, RT is the revisit period of the i-th SAR satellite system, and DT is the duration of the accelerated creep stage of the target landslide hazard point obtained in step S4C-4.

[0065] Step S5D-2, Calculate the SAR satellite system For the sufficiency index of data on target landslide hazard points The formula is:

[0066] ;

[0067] In the formula, FS is the safety factor. 1. The FS value should be determined based on the socio-economic value of the threatened objects at the target landslide hazard point. The higher the socio-economic value of the threatened objects at the target landslide hazard point, the larger the FS value. (Data sufficiency index) A value of 1 indicates the minimum amount of time-series deformation monitoring data of the target landslide hazard point that can be obtained based on the SAR satellite system to meet the requirements for predicting the instability time of the target landslide hazard point. This represents the data sufficiency index. A value of 0 indicates that the amount of time-series deformation monitoring data of the target landslide hazard point obtained based on the SAR satellite system cannot meet the minimum data requirement for predicting the instability time of the target landslide hazard point.

[0068] Step S6, for any SAR satellite system Based on the preset method E, the SAR satellite system is obtained. For the interference phase continuity index of the target landslide hazard point, the preset method E includes the following steps:

[0069] Step S6E-1, for any SAR satellite system Calculate SAR satellite system Maximum detectable deformation rate The formula is:

[0070] In the formula, The wavelength of the i-th SAR satellite system;

[0071] Step S6E-2, Calculate the SAR satellite system Interference phase continuity index for target landslide hazard points The formula is:

[0072] ;

[0073] In the formula, the interference phase continuity index A value of 1 indicates a SAR satellite system. It can effectively and accurately measure the deformation at time DT / 2 during the accelerated creep stage of the target landslide hazard point; interferometric phase continuity index A value of 0 indicates a SAR satellite system. When measuring the surface deformation of the target landslide hazard point at time DT / 2 during the accelerated creep stage, interference phase aliasing occurs, making it impossible to effectively and accurately measure the magnitude of the surface deformation. In the formula... The average deformation rate of the landslide surface at time DT / 2 during the accelerated creep stage obtained in step S4C-4. .

[0074] Step S7, for any SAR satellite system Based on sensitivity indicators Data sufficiency indicators Interference phase continuity index For SAR satellite systems Applicability of monitoring data to predict the instability time of target landslide hazard points The calculation is performed using the following formula:

[0075] .

[0076] S8, For the target landslide hazard point, the applicability F of landslide instability time prediction based on InSAR monitoring data is calculated, and the calculation formula is as follows: .

[0077] Compared to existing InSAR-based monitoring technologies, this embodiment uses SAR satellite systems that pass over landslide hazard points and whose service coverage includes the entire landslide area. It quantitatively evaluates the applicability of InSAR-based landslide instability time prediction based on data from multiple SAR satellite systems, considering three aspects: sensitivity of the target landslide hazard points, the availability of time-series deformation monitoring data during the accelerated creep phase, and the accuracy of the monitoring data. This quantitative analysis and evaluation within satellite systems that pass over the landslide hazard points and whose service coverage includes the entire landslide area provides a reliable basis for subsequent classification and grading monitoring of landslide hazard points. By addressing the inherent validity of the data, it ensures more accurate predictions of landslide instability time based on satellite system data, effectively increasing the number of monitored landslide hazard points and significantly reducing monitoring costs.

[0078] The above descriptions are merely embodiments of the present invention, and common knowledge regarding specific structures and characteristics is not elaborated upon here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. An applicability analysis method for predicting landslide instability time based on InSAR monitoring data, including the following steps: Step S1: Obtain topographic and geological data of the target landslide hazard point, and determine the deformation and failure mechanism of the target landslide hazard point based on the topographic and geological data of the target landslide hazard point. Its characteristic is that it further includes: Step S2: Based on the geographical coordinates of the target landslide hazard point, obtain m SAR satellite systems that pass over the target landslide hazard point at a constant period and whose service coverage includes the entire landslide body of the target landslide hazard point, as well as the revisit period, wavelength, and average incident angle of the SAR satellite systems. Step S3: For any SAR satellite system, calculate the local effective incident angle of the SAR satellite system on the surface of the target landslide hazard point based on the preset method A, and obtain the sensitivity index of the SAR satellite system to the target landslide hazard point based on the preset method B. Step S4: Based on the preset method C, obtain the duration DT of the accelerated creep stage of the target landslide hazard point and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage; Step S5: For any SAR satellite system, obtain the sufficiency index of the amount of data of the SAR satellite system for the target landslide hazard point based on the preset method D; Step S6: For any SAR satellite system, obtain the interferometric phase continuity index of the SAR satellite system for the target landslide hazard point based on the preset method E; Step S7, for any SAR satellite system, based on sensitivity indicators Data sufficiency indicators Interference phase continuity index Let i represent the i-th SAR satellite system, and let i represent the applicability of SAR satellite system monitoring data to predict the instability time of target landslide hazard points. The calculation is performed using the following formula: ； S8, For the target landslide hazard point, the applicability F of landslide instability time prediction based on InSAR monitoring data is calculated, and the calculation formula is as follows: ； In step S3, for any SAR satellite system, the effective local incident angle of the slope surface at the target landslide hazard point is used. ; Calculate the sensitivity index of SAR satellite system for target landslide hazard points The formula is: ； In the formula, the sensitivity index A value of 1 indicates that accurate and effective time-series deformation monitoring data of target landslide hazard points can be obtained based on the SAR satellite system; sensitivity index A value of 0 indicates that the time-series deformation monitoring data of the target landslide hazard points obtained based on the SAR satellite system cannot meet the accuracy requirements for predicting the landslide instability time. In step S5, the preset method D includes the following steps: Step S5D-1: For any SAR satellite system, calculate the number of landslide surface deformation time-series data that can be obtained from InSAR monitoring data at time DT / 2 during the accelerated creep phase of the target landslide hazard point. The formula is: ,in, For the i-th SAR satellite system, DT is the duration of the accelerated creep phase of the landslide hazard point; Step S5D-2: Calculate the sufficiency index of the SAR satellite system for the target landslide hazard points. The formula is: ； In the formula, FS is the safety factor. 1; A value of 1 indicates the minimum amount of time-series deformation monitoring data of the target landslide hazard point that can be obtained based on the SAR satellite system to meet the requirements for predicting the instability time of the target landslide hazard point. A value of 0 indicates that the amount of time-series deformation monitoring data of the target landslide hazard point obtained based on the SAR satellite system cannot meet the minimum data requirement for predicting the instability time of the target landslide hazard point; In step S6, the preset method E includes the following steps: Step S6E-1: For any SAR satellite system, calculate the maximum deformation rate that the SAR satellite system can detect. The formula is: In the formula, The wavelength of the i-th SAR satellite system; Step S6E-2: Calculate the interferometric phase continuity index of the SAR satellite system for the target landslide hazard point. The formula is: ； A value of 1 indicates that the deformation at time DT / 2, the accelerated creep stage of the target landslide hazard point, can be effectively and accurately measured based on the SAR satellite system. A value of 0 indicates that when the SAR satellite system measures the surface deformation of the target landslide hazard point at time DT / 2 during the accelerated creep stage, an interference phase aliasing phenomenon will occur, making it impossible to effectively and accurately measure the magnitude of the surface deformation of the target landslide hazard point. To accelerate the average deformation rate of the landslide surface at time DT / 2 during the creep stage.

2. The applicability analysis method for predicting landslide instability time based on InSAR monitoring data according to claim 1, characterized in that: In step S1, the topographic and geological data include the geometric center elevation, slope aspect, average slope, average plane curvature, average profile curvature, and slope lithology of the target landslide hazard point.

3. The applicability analysis method for predicting landslide instability time based on InSAR monitoring data according to claim 2, characterized in that: The deformation and failure mechanism of the target landslide hazard point is one of the following: traction landslide or push-movement landslide.

4. The applicability analysis method for predicting landslide instability time based on InSAR monitoring data according to claim 1, characterized in that: In step S3, the preset method A includes the following steps: Step S3A-1: For any SAR satellite system, based on the local effective incident angle geometric model and spatial geometric relationship, calculate the average slope aspect angle δ of the slope surface at the target landslide hazard point and the horizontal angle ω of the line of sight of the SAR satellite system; Step S3A-2: Calculate the local effective incident angle on the slope surface of the target landslide hazard point. The formula is: ； In the formula, α represents the average slope of the target landslide hazard point. The incident angle of the satellite sensor.

5. The applicability analysis method for predicting landslide instability time based on InSAR monitoring data according to claim 1, characterized in that: In step S4, the preset method C includes the following steps: Step S4C-1: Collect data on landslides that have already failed, including: landslide topography and geomorphology, geological conditions, deformation and failure mechanisms of the landslides that have failed, and deformation monitoring data during the landslide deformation and failure process. The deformation monitoring data includes the duration of the accelerated creep stage DT and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage. ; Step S4C-2 takes the landslide topography, geological conditions, and deformation failure mechanism of the landslide that has become unstable as input from Step S4C-1, and uses the duration DT of the accelerated creep stage and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage from Step S4C-1 as input. Using these as labels, machine learning models were constructed to predict the duration DT of the accelerated creep stage in landslides. And predict the average deformation rate of the landslide surface at time DT / 2 during the accelerated creep stage of the landslide. machine learning models and for machine learning models and machine learning models Training and validation test datasets and ; Step S4C-3, respectively, for the machine learning model and machine learning models Train, validate, and test to optimize the machine learning model. and machine learning models The parameters in the model are used to obtain the trained machine learning model. and ; Step S4C-4: For the target landslide hazard point, the topographic data, geological conditions, and deformation and failure mechanisms of the target landslide hazard point obtained in step S1 are used as the machine learning model obtained in step S4C-3. and The input, and based on the machine learning model and The duration of the accelerated creep stage DT and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage were analyzed at the target landslide hazard point. The prediction was performed to obtain the duration DT of the accelerated creep stage at the target landslide hazard point and the average deformation rate of the landslide surface at time DT / 2 of the accelerated creep stage. .

6. The applicability analysis method for predicting landslide instability time based on InSAR monitoring data according to claim 5, characterized in that: In step S4C-1, the number of landslides that have already become unstable and failed is greater than 50, and the landslide deformation and failure mechanisms include both traction landslides and shove landslides.

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