Method for predicting self-repairing time of accelerated recovery type landslide
By combining multi-phase ascending and descending orbit satellite-borne radar imagery with the BFAST algorithm, quantitative prediction of the self-repair time of accelerated recovery landslides was achieved, solving the problem of inaccurate identification of the recovery time of accelerated recovery landslides in existing technologies, and improving the accuracy and timeliness of landslide disaster risk assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-12
- Publication Date
- 2026-04-07
AI Technical Summary
Existing technologies are insufficient to accurately identify and quantitatively predict the recovery time of accelerated recovery landslides, resulting in inadequate post-disaster landslide hazard assessment and dynamic risk management.
Using multi-phase ascending and descending orbit satellite-borne radar imagery combined with MT-InSAR and SAR POT technologies, an active landslide catalog was established using spatial clustering algorithms. Accelerated recovery landslides were identified using the BFAST algorithm, and linear and exponential decay functions were fitted to the surface deformation time series to calculate the recovery time.
It enables quantitative prediction of the self-repair time of accelerated recovery landslides, improves the accuracy and timeliness of landslide disaster risk assessment, and provides a reliable basis for disaster prevention and mitigation.
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Figure CN121806013A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of image geodesy and its geological disaster monitoring and early warning, and in particular to a self-repairing time prediction method for accelerated recovery landslides. BACKGROUND
[0002] Under the background of global climate change intensification and frequent extreme events, earthquakes, landslide instability dam / lake dam and its breach flood, heavy rainfall and other natural disasters are prone to induce a large number of accelerated recovery landslides. After experiencing short-term accelerated deformation, such landslides will gradually enter the self-repairing stage, during which the ground rupture caused by the landslides often causes serious damage to buildings / structures. In post-disaster disposal and hidden danger investigation, accelerated recovery landslides are often overlooked, but they may still evolve into new unstable slopes, posing a continuous threat to the safety of local residents' life and property. Therefore, accurately identifying accelerated recovery landslides and accurately predicting the recovery time of accelerated recovery landslides are of great significance to improving the risk identification ability and disaster prevention and reduction level of landslide disasters. Existing researches mainly focus on the analysis of landslide movement mechanism, such as identifying the deformation characteristics and types of landslides by using spaceborne radar remote sensing technology. However, the research on the quantification of the recovery process of accelerated recovery landslides and the prediction of their recovery time is relatively insufficient, and there is still a lack of effective technical means to quantitatively estimate the recovery time of such landslides. This makes it difficult for existing technologies to meet the needs of post-disaster landslide hazard assessment and dynamic risk management.
[0003] In view of the above problems, there is an urgent need for a self-repairing time prediction method for accelerated recovery landslides to solve the above problems existing in traditional methods. SUMMARY
[0004] The purpose of the present application is to provide a self-repairing time prediction method for accelerated recovery landslides, which realizes the automatic identification of accelerated recovery landslides and the quantitative prediction of their recovery time, and improves the accuracy and timeliness of landslide disaster risk assessment, providing a reliable basis for disaster prevention and reduction.
[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows: A self-repairing time prediction method for accelerated recovery landslides, comprising: Step 1: Using multi-period ascending-descending track spaceborne radar images, MT-InSAR technology and SAR POT technology are used to obtain the ground deformation time series and annual ground deformation rate of the study area; Step 2: Based on the ground deformation time series and the annual ground deformation rate, a spatial clustering algorithm is used to establish a catalog of active landslides in the study area; Step 3: Extracting the average ground time series deformation within the boundary of the active landslide, using BFAST algorithm to determine the accelerated recovery landslide; Step 4: Fit the time series of surface deformation of accelerated recovery landslides. The deformation before the extreme event is fitted with a linear function, and the deformation after the event is fitted with an exponential decay function. Step 5: Calculate the temporal velocity evolution of the accelerated recovery landslide based on the fitted exponential decay function; Step 6: Integrate all information on accelerated recovery landslides and establish an accelerated recovery landslide database.
[0006] Furthermore, in step 2, the active landslide cataloging includes the specific location and boundaries of the active landslides.
[0007] Furthermore, in step 3, the BFAST algorithm is used to determine accelerated recovery landslides, specifically as follows: The average temporal surface deformation within the boundary of the active landslide catalog is decomposed into trend components, seasonal components, and residual components using the BFAST algorithm. Accelerated recovery landslides were identified through breakpoint detection.
[0008] Furthermore, the criteria for determining an accelerated recovery landslide are: the number of breakpoints after the extreme event is greater than 2, and the deformation rate from the first breakpoint to the second breakpoint is greater than the deformation rate from the second breakpoint to the third breakpoint.
[0009] Furthermore, in step 4, the exponential decay function is: (1) In the formula, It is a linear function before the extreme event occurs. a and b The parameters are estimated by linear fitting. It is a decaying exponential function following extreme events. , and These are the estimated parameters of the decay function.
[0010] Furthermore, in step 5, the temporal velocity evolution of the accelerated recovery landslide is calculated based on the fitted exponential decay function, specifically as follows: The temporal velocity evolution of accelerated recovery landslides is calculated based on the derivative of the fitted exponential decay function, yielding the normalized post-accelerated recovery landslide velocity as follows: (2) In the formula, Let be the velocity of motion after the extreme event, which decays from 1 to 0, and t be the time after the extreme event.
[0011] Furthermore, step 5 also includes defining the time required for the normalized rate after an accelerated recovery landslide event to decay to 90% of its peak value as the recovery time of the accelerated recovery landslide.
[0012] Furthermore, in step 6, the accelerated recovery landslide database includes the location, extent, movement rate before the extreme event, movement rate after the extreme event, acceleration time point, and recovery time of the accelerated recovery landslide.
[0013] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention enables quantitative prediction of the self-repair time of accelerated recovery landslides, accurately estimating the time required for a landslide to recover from an accelerated state to a stable state, providing scientific support for landslide disaster risk assessment, hidden danger investigation, and disaster prevention and mitigation decision-making.
[0014] 2. This invention achieves a leap from visual interpretation and identification to automatic identification of accelerated recovery landslides, providing a reliable basis for judging the movement mechanism of landslides.
[0015] 3. This invention has high versatility and can be applied to areas observable by radar satellites worldwide. Application scenarios include extreme events such as earthquakes, extreme rainfall, landslides / glacial dammed lakes and their outburst floods. Attached Figure Description
[0016] Figure 1 This is a map showing the location distribution of algal blooms (ALs) in a watershed under the influence of extreme events. Figure 2 A schematic diagram illustrating the cataloging of landslides automatically identified by the BFAST algorithm for accelerated recovery. Figure 3 A schematic diagram showing the average velocity of an AR-AL type landslide induced by the impoundment of a landslide-dammed lake. Figure 4 A schematic diagram illustrating the average velocity of an AR-AL type landslide induced by the flood from the breach of a landslide-dammed lake. Figure 5 A schematic diagram showing the average velocity of an AR-DL type landslide induced by the flood from the breach of a landslide-dammed lake. Figure 6 A graph showing the annual average deformation rate of an AR-AL type landslide induced before the impoundment of a landslide-dammed lake. Figure 7 Figure showing the annual average deformation rate of an AR-AL type landslide induced by the impoundment of water in a landslide-dammed lake. Figure 8 A schematic diagram of the temporal deformation at point P1 of an AR-AL type landslide affected by the impoundment of a landslide dammed lake; Figure 9 A graph showing the annual average deformation rate of an AR-AL type landslide induced by the flood before the collapse of a landslide-dammed lake. Figure 10 A graph showing the annual average deformation rate of an AR-AL type landslide induced by the flood after the collapse of a landslide-dammed lake. Figure 11 A schematic diagram of the temporal deformation at point P2 of an AR-AL type landslide affected by the flood from the breach of a landslide dammed lake; Figure 12 A graph showing the annual average deformation rate of an AR-DL type landslide induced before the flood caused by the breach of a landslide-dammed lake. Figure 13 A graph showing the annual average deformation rate of an AR-DL type landslide induced by the flood after the collapse of a landslide-dammed lake. Figure 14 A schematic diagram of the temporal deformation at point P3 of an AR-DL type landslide affected by the flood from the breach of a landslide dammed lake; Figure 15 A schematic diagram of the normalized velocity of an AR-type landslide; Figure 16 This is a schematic diagram of the method flow of the present invention. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.
[0018] Before providing a detailed description of the method, the key terms and abbreviations involved in the method of this invention will be explained, including: 1. InSAR: Interferometric Synthetic Aperture Radar. 2. MT-InSAR: multi-temporal InSAR (time-series InSAR); 3. POT: Pixel Offset Tracking; 4. BFAST: Breaks for Additive Season and Trend. 5. ALs: Active landslides; 6. AR: Accelerated recovery; 7. AR-AL: accelerated-then-recovered active landslides; 8. AR-DL: accelerated-then-recovery dormant landslides.
[0019] like Figure 16 As shown, this invention provides a method for predicting the self-repair time of accelerated recovery landslides, comprising: Step 1: Using multi-phase ascending and descending orbit satellite-borne radar imagery, MT-InSAR and SAR POT technologies are employed to obtain the time series of surface deformation and the annual surface deformation rate of the study area. Step 2: Based on the time series of surface deformation and the annual surface deformation rate, a spatial clustering algorithm is used to establish an inventory of active landslides in the study area; Step 3: Extract the average temporal surface deformation within the boundary of the active landslide and use the BFAST algorithm to determine the accelerated recovery type of landslide; Step 4: Fit the time series of surface deformation of accelerated recovery landslides. The deformation before the extreme event is fitted with a linear function, and the deformation after the event is fitted with an exponential decay function. Step 5: Calculate the temporal velocity evolution of the accelerated recovery landslide based on the fitted exponential decay function; Step 6: Integrate all information on accelerated recovery landslides and establish an accelerated recovery landslide database.
[0020] In step 2, the active landslide cataloging includes the specific location and boundaries of the active landslides.
[0021] In step 3, the BFAST algorithm is used to determine accelerated recovery landslides, specifically as follows: Step 301: Using the BFAST algorithm, decompose the average temporal surface deformation within the boundary of the active landslide catalog into trend components, seasonal components, and residual components, as follows: (1) In the formula, X t For the time series of surface deformation, Q t For trend components, J t As a seasonal component, C t These are the residual components; Step 302: Simultaneously, the BFAST algorithm can continuously improve the model through iterative adjustments, and determine accelerated recovery landslides through breakpoint detection, which are: If there are more than two breakpoints after an extreme event, it indicates that the ALs have at least two rate-change phenomena after the extreme event. Based on this, the deformation rate between the breakpoints is obtained. If the deformation rate from the first breakpoint to the second breakpoint is greater than the deformation rate from the second breakpoint to the third breakpoint, it is an accelerated recovery landslide. Otherwise, it is excluded, and an AR-type landslide catalog is drawn.
[0022] In step 4, the exponential decay function can effectively characterize the nonlinear decay and viscoelastic recovery of landslide motion after external disturbance. The specific function is as follows: (2) In the formula, It is a linear function before the extreme event occurs. a and b The parameters are estimated by linear fitting. It is a decaying exponential function following extreme events. , and These are the estimated parameters of the decay function.
[0023] In step 5, the temporal velocity evolution of the accelerated recovery landslide is calculated based on the fitted exponential decay function, specifically as follows: To quantitatively assess the decay characteristics of AR-type landslides, the temporal velocity evolution of accelerated recovery landslides was calculated based on the derivative of the fitted exponential decay function, yielding the normalized post-event velocity of the accelerated recovery landslide: (3) In the formula, denoted as the motion rate after the extreme event occurs, decaying from 1 to 0, where t is the time after the extreme event occurs. The recovery time of an accelerated recovery landslide is defined as the time required for the rate to decay to 90% of its peak value after a normalized accelerated recovery landslide event.
[0024] In step 6, the accelerated recovery landslide database includes the location, extent, movement rate before the extreme event, movement rate after the extreme event, acceleration time point, and recovery time of accelerated recovery landslides. This serves as a resource for long-term disaster prevention and mitigation efforts in the affected areas after extreme events.
[0025] This invention provides several embodiments to illustrate the method described herein, using a watershed affected by a landslide instability in 2018 that led to the impoundment of a barrier lake and its subsequent breach and flood as an example: Figure 1The location distribution map of ALs in the area affected by the impoundment of a landslide-dammed lake and its outburst flood in 2018. The location of ALs was automatically delineated based on deformation information using a spatial clustering algorithm (a total of 217 ALs were drawn). Figure 2 The BFAST algorithm is used to automatically identify and catalog accelerated recovery landslides. This is mainly based on the number of breakpoints identified by the BFAST algorithm and the magnitude of the rate between the breakpoints. It should be noted that the number of breakpoints is counted from the time the extreme event occurs. In this example, there are 28 AR-type landslides.
[0026] Figures 3-5 The average deformation rate and evolution of AR-type landslides, among which, Figure 3 The average velocity of an AR-type landslide induced by the impoundment of a landslide-dammed lake. Figure 4 The average rate of AR-type landslides induced by the flood from the breach of a landslide-dammed lake during a certain event. Figure 5 The average deformation rate of an AR-type landslide induced by a flood from the breach of a dormant landslide-dammed lake is given. AR-AL represents an active landslide before the extreme event, and AR-DL represents a dormant landslide before the extreme event. It should be noted that, to address the inconsistency in the LOS deformation direction between ascending and descending orbit data acquisition, this invention projects all LOS data onto the landslide slope direction before calculating the average deformation value.
[0027] Figures 6-8 This case study illustrates how the impoundment of a landslide-dammed lake induced an AR-AL type landslide. Figures 9-11 This is a case study of an AR-AL type landslide triggered by the breach of a landslide-dammed lake. Figures 12-14 This is a case study of an AR-DL type landslide activated by the flood caused by the breach of a landslide-dammed lake.
[0028] Figure 15 When normalizing the velocity for AR-type landslides, it is important to note that the deformation rate decay characteristics of AR-type landslides induced by this extreme event exhibit significant differences. Some AR-type landslides decayed to 90% of their peak value within 0.8 years after the event, while others took over 38 years to decay. On average, the time required for the accelerated recovery landslide rate to decay to 90% of its peak value is approximately 9.3 years.
[0029] The present invention also provides an AR-type cataloging database and a table of recovery time prediction accuracy evaluation results, as shown in Table 1.
[0030] Table 1. Accuracy Assessment of AR-type Cataloging Database and Recovery Time Prediction
[0031] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0032] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0033] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0034] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0035] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.
Claims
1. A method for predicting the self-repair time of accelerated recovery landslides, characterized in that, include: Step 1: Using multi-phase ascending and descending orbit satellite-borne radar imagery, MT-InSAR and SAR POT technologies are employed to obtain the time series of surface deformation and the annual surface deformation rate of the study area. Step 2: Based on the time series of surface deformation and the annual surface deformation rate, a catalog of active landslides in the study area is established using a spatial clustering algorithm; Step 3: Extract the average temporal surface deformation within the boundary of the active landslide and use the BFAST algorithm to determine the accelerated recovery type of landslide; Step 4: Fit the time series of surface deformation of accelerated recovery landslides. The deformation before the extreme event is fitted with a linear function, and the deformation after the event is fitted with an exponential decay function. Step 5: Calculate the temporal velocity evolution of the accelerated recovery landslide based on the fitted exponential decay function; Step 6: Integrate all information on accelerated recovery landslides and establish an accelerated recovery landslide database.
2. The method for predicting the self-repair time of an accelerated recovery landslide according to claim 1, characterized in that, In step 2, the active landslide cataloging includes the specific location and boundaries of the active landslides.
3. The method for predicting the self-repair time of an accelerated recovery landslide according to claim 2, characterized in that, In step 3, the BFAST algorithm is used to determine accelerated recovery landslides, specifically as follows: The average temporal surface deformation within the boundary of the active landslide catalog is decomposed into trend components, seasonal components, and residual components using the BFAST algorithm. Accelerated recovery landslides were identified through breakpoint detection.
4. The method for predicting the self-repair time of an accelerated recovery landslide according to claim 3, characterized in that, The criteria for determining an accelerated recovery landslide are: the number of breakpoints after the extreme event is greater than 2, and the deformation rate from the first breakpoint to the second breakpoint is greater than the deformation rate from the second breakpoint to the third breakpoint.
5. The method for predicting the self-repair time of an accelerated recovery landslide according to claim 4, characterized in that, In step 4, the exponential decay function is: （1） In the formula, It is a linear function before the extreme event occurs. a and b The parameters are estimated by linear fitting. It is a decaying exponential function following extreme events. , and These are the estimated parameters of the exponentially decaying function.
6. The method for predicting the self-repair time of an accelerated recovery landslide according to claim 5, characterized in that, In step 5, the temporal velocity evolution of the accelerated recovery landslide is calculated based on the fitted exponential decay function, specifically as follows: The temporal velocity evolution of accelerated recovery landslides is calculated based on the derivative of the fitted exponential decay function, yielding the normalized post-accelerated recovery landslide velocity as follows: （2） In the formula, Let be the velocity of motion after the extreme event, which decays from 1 to 0, and t be the time after the extreme event.
7. The method for predicting the self-repair time of an accelerated recovery landslide according to claim 6, characterized in that, Step 5 also includes defining the recovery time of an accelerated recovery landslide as the time required for the normalized rate to decay to 90% of its peak value after the landslide event.
8. The method for predicting the self-repair time of an accelerated recovery landslide according to claim 7, characterized in that, In step 6, the accelerated recovery landslide database includes the location, extent, movement rate before the extreme event, movement rate after the extreme event, acceleration time point, and recovery time of the accelerated recovery landslide.