High slope chain generation risk assessment method based on numerical simulation

Abstract

The invention discloses a numerical simulation-based high slope chain generation risk assessment method, which belongs to the technical field of landslide numerical simulation and comprises the following steps of S1, collecting a landslide susceptibility result and landslide cataloguing data; s2, screening a landslide starting area; s3, landslide numerical simulation; and S4, spatial analysis and risk evaluation. According to the high-position slope chain generation risk assessment method based on numerical simulation, the landslide starting area is accurately screened through numerical simulation, and a scientific basis is provided for prevention and reduction of landslide disasters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of landslide numerical simulation, in particular to a high-position slope chain risk assessment method based on numerical simulation. BACKGROUND

[0002] Landslide disaster is a common and destructive geological disaster worldwide, which is usually closely related to the stability of high-position slopes. Traditional landslide risk assessment methods rely heavily on experience and field investigation, and when dealing with complex terrain and large-scale areas, there are often problems of insufficient precision and low efficiency. With the development of computer technology and numerical simulation methods, landslide risk assessment methods based on numerical simulation have gradually become a research hotspot. Among them, the FLOW-R model, as an advanced landslide numerical simulation tool, can simulate the initiation, propagation and deposition process of landslides, providing an effective technical means for landslide risk assessment. However, directly using landslide susceptibility assessment results as the input of the initiation zone of the FLOW-R model may lead to large deviations between the simulation results and the actual situation, as the susceptibility assessment results are usually based on statistical analysis. Therefore, there is an urgent need for a method that can accurately select the landslide initiation zone in the FLOW-R model to improve the accuracy and reliability of landslide risk assessment. SUMMARY

[0003] The purpose of the present application is to provide a high-position slope chain risk assessment method based on numerical simulation, which accurately selects the landslide initiation zone through numerical simulation, and provides a scientific basis for the prevention and disaster reduction of landslide disasters.

[0004] To achieve the above-mentioned purpose, the present application provides a high-position slope chain risk assessment method based on numerical simulation, comprising the following steps:

[0005] S1, landslide susceptibility results and landslide inventory data collection;

[0006] S2, landslide initiation zone screening;

[0007] S3, landslide numerical simulation;

[0008] S4, spatial analysis and risk assessment.

[0009] Preferably, in S1, the range of the study area is determined, and the geological, topographic and climatic data related to landslide susceptibility are collected, including elevation, slope, soil thickness distribution, soil type, rainfall, seismic activity, and the possibility of landslide occurrence in the region is evaluated using the infinite slope stability model. The landslide inventory data of the study area are collected, which include detailed records of historical landslide events, including the location, size, occurrence time and triggering factors of the landslide.

[0010] Preferably, S2 specifically includes the following steps:

[0011] S2.1, integrate the landslide susceptibility results generated in S1 with landslide inventory data;

[0012] S2.2, statistically analyze the spatial distribution of historical landslide events using kernel density estimation based on the integrated data from S2.1.

[0013] Preferably, S2.2 specifically includes the following steps:

[0014] S2.2.1, use the geographic coordinates in the landslide inventory data as input point sets, use Gaussian kernel function for density estimation, and output the kernel density map according to the landslide distribution density and spatial dimension of the study area;

[0015] S2.2.2, use the Segment Anything model SAM to analyze the kernel density map to obtain potential landslide initiation;

[0016] Preferably, in S3, the FLOW-R model is used to simulate the movement process of landslide material. The FLOW-R model is based on digital elevation model DEM and its derivatives for regional sensitivity analysis, and calculates the propagation path and deposition range of landslide material through numerical iteration method. Its core formula is:

[0017]

[0018] Where F is the net force of landslide material; m is the mass of landslide material; g is the acceleration of gravity; θ is the terrain slope; μ is the internal friction coefficient, which controls the friction resistance of landslide along the slope surface; ρ is the density of landslide material; v is the current speed of landslide material; ξ is the turbulence coefficient.

[0019] Preferably, S4 specifically includes the following steps:

[0020] S4.1, based on the landslide impact range generated by using FLOW-R for numerical simulation, through spatial superposition analysis, the landslide impact range is comprehensively evaluated with hazard body data;

[0021] S4.2, construct a risk assessment index system;

[0022] S4.3, based on the risk assessment results, develop a scientific risk management strategy.

[0023] Preferably, in S4.2, the risk assessment is based on three core elements: danger, vulnerability and exposure, and specifically includes the following steps:

[0024] S4.2.1, based on the landslide simulation results, extract the impact range and divide the danger level;

[0025] S4.2.2, combined with the type and social and economic data of hazard body, assess its vulnerability;

[0026] S4.2.3, quantifying the population, infrastructure of the affected area.

[0027] Therefore, the present application adopts the above-mentioned high-position slope chain risk assessment method based on numerical simulation, accurately screens the landslide starting area through numerical simulation, and provides a scientific basis for the prevention and disaster reduction of landslide disasters.

[0028] The technical solutions of the present application will be further described in detail below through the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of a high-position slope chain risk assessment method based on numerical simulation of the present application;

[0030] Figure 2 is a position of a study area and a DEM schematic diagram of a high-position slope chain risk assessment method based on numerical simulation of the present application;

[0031] Figure 3 is an affected distribution diagram of a high-position slope chain risk assessment method based on numerical simulation of the present application, wherein, Figure 3 (a) in is a potential landslide affected range diagram of a certain highway along the highway, Figure 3 (b) in is a potential landslide affected range diagram of a river along the certain highway, Figure 3 (c) in is a landslide affected range diagram of a building along the certain highway, Figure 3 (d) in is a building affected distribution range analysis diagram of a certain highway along the certain highway, Figure 3 (e) in is a potential landslide affected range diagram along the certain highway;

[0032] Figure 4 is a kernel density result diagram of a certain highway along a certain area of the present application. DETAILED DESCRIPTION

[0033] The technical solutions of the present application will be further described in detail below through the drawings and examples.

[0034] Unless otherwise defined, the technical terms or scientific terms used in the present application shall have the usual meanings understood by those skilled in the art to which the present application belongs.

[0035] Example 1

[0036] As shown in Figure 1 , the present application provides a high-position slope chain risk assessment method based on numerical simulation, taking a 20km buffer zone established along a certain highway as an example, including the following steps:

[0037] S1, landslide susceptibility result and landslide inventory data collection;

[0038] Determine the scope of the study area, collect data related to landslide susceptibility, including elevation, slope, soil thickness distribution, soil type, rainfall, seismic activity, use infinite slope stability model to evaluate the possibility of landslide occurrence in the region, collect landslide inventory data in the study area, including detailed records of historical landslide events, including location, size, time of occurrence and triggering factors.

[0039] S2, landslide initiation zone screening; specifically including the following steps:

[0040] S2.1, integrate the landslide susceptibility results generated in S1 with the landslide inventory data;

[0041] Integrate the landslide susceptibility results generated in the first step with the landslide inventory data to achieve good analysis of multi-source data. Landslide susceptibility results provide the probability distribution of landslide occurrence in the study area, while landslide inventory data records the specific location, size and triggering factors of historical landslides. By spatial overlay and analysis of these two types of data in geographic information system (GIS), high susceptibility areas and historical landslide areas are preliminarily identified, laying the foundation for subsequent refined screening. This process ensures the spatial consistency of data and provides reliable kernel input for density analysis.

[0042] S2.2, statistical analysis of the spatial distribution of historical landslide events based on the data integrated in S2.1 using kernel density estimation method; specifically including the following steps:

[0043] S2.2.1, use the geographic coordinates in the landslide inventory data as input point set, each point representing a historical landslide event; use Gaussian kernel function for density estimation, optimize bandwidth selection by cross-validation method, set the range to 100 meters, according to the landslide distribution density and spatial dimension of the study area, output the kernel density result map, as shown in Figure 4 By setting an appropriate bandwidth, areas with density values below the preset value are considered non-significant, thus eliminating those landslide points that are scattered, small in size and do not have the characteristics of initiation zone. The output resolution of the kernel density map is consistent with the input DEM (such as 10 meters or 30 meters), and the pixel value reflects the landslide density per unit area, with high value areas potentially corresponding to landslide initiation zones.

[0044] S2.2.2, adopt Segment Anything model SAM to analyze the kernel density map to obtain potential landslide initiation; SAM is a deep learning-based segmentation model, before applying SAM, the kernel density map needs to be constructed: convert the kernel density map to a grayscale image, standardize the pixel value to 0-255, the high-density area corresponds to high pixel value, and the low-density area corresponds to low pixel value, provide input for SAM.

[0045] The segmentation process of SAM relies on point hints. Using kernel density as the center hint point of high-density aggregation area, the kernel density value is automatically identified as a local maximum by a high-precision detection algorithm. To ensure comprehensive coverage, you can supplement the hint points in the area with a susceptibility score of 0.8. SAM segments the kernel density map based on these hints, generating multiple segmentation gap codes, which have the ability to capture the irregular boundaries of high-density areas, and then traditional sparse value segmentation methods improve intelligence and performance.

[0046] The segmentation codes generated by SAM need to be optimized in the later stage. First, remove areas smaller than 100 square meters to reduce noise. Second, the screening criteria include: areas with an average kernel density value lower than 1 event per area are excluded; areas with an overlap of less than 50% with the area with a susceptibility score of 0.8 are considered low-risk; areas with a slope less than 5° are excluded, and sparse does not have landslide initiation. The remaining start area after screening retains high-risk and high-aggregation characteristics. After screening, the start area is classified and prioritized according to the average kernel density value and potential impact factors (such as area and distance to adjacent disaster bodies), and areas with high kernel density values are prioritized for subsequent analysis.

[0047] S3, numerical simulation of landslides;

[0048] The FLOW-R model is based on the Depth-Integrated Continuity Equation and the Voellmy rheological model. The FLOW-R model simulates the movement of landslide material. This model simplifies the three-dimensional fluid mechanics equation and treats landslide material as a continuous medium, with the dynamics of the surface propagation controlled by frictional resistance and turbulent effects. The Voellmy rheological model combines the characteristics of solid friction and fluid turbulence, making it suitable for simulating the trajectories of different types of landslides (such as debris flows and rock avalanches). The FLOW-R model is based on the Digital Elevation Model (DEM) and its derivatives for regional sensitivity analysis, and calculates the propagation path and deposition range of landslide material through numerical iteration methods. The core formula is:

[0049]

[0050] Where F is the net force of landslide material; m is the mass of landslide material; g is the acceleration of gravity; θ is the terrain slope; μ is the internal friction coefficient, controlling the frictional resistance of landslide along the slope surface; ρ is the density of landslide material; v is the current speed of landslide material; ξ is the turbulent flow coefficient.

[0051] The FLOW-R model uses high-resolution DEMs (e.g., 10 meters or 30 meters) as the terrain basis, ensuring accurate representation of terrain features. The selected landslide initiation zone is input as the initial sliding source into the model, and the boundary and volume information of the initiation zone are directly imported from the results of the second step. To configure the key parameters of the model, refer to the rock and soil parameters provided by the FLOW-R official website for different types of landslides, and preliminarily set the software parameters, including the turbulent flow coefficient ξ and initial speed conditions, etc.

[0052] To ensure the accuracy of the simulation results, select a typical historical landslide event in the study area as a calibration case. You can choose the Attabad landslide event and extract its actual impact range as reference data. Through preliminary simulation, compare the differences between the landslide path and deposition range output by the model and the historical records, iteratively adjust the key parameters, and use the trial-and-error method combined with sensitivity analysis to determine the best combination of parameters.

[0053] After calibration, run the FLOW-R model to simulate the landslide impact range through numerical iterative calculation. The simulation results are saved in Shp format, generating a landslide impact range map, as shown in Figure 2 , which includes the landslide path boundary and deposition area range. To facilitate subsequent analysis, use GIS software such as ArcGIS or QGIS to visualize the results and label key areas (such as maximum outflow distance and deposition depth).

[0054] S4, spatial analysis and risk assessment; including the following steps:

[0055] S4.1, based on the landslide impact range generated by using Flow-R for numerical simulation, through spatial overlay analysis, the landslide impact range is integrated with the hazard-bearing body data for comprehensive evaluation; this includes spatial overlay of landslide impact range map and hazard-bearing body data such as residential areas, roads, buildings, etc., to identify threatened areas and calculate the number of affected populations, infrastructure length, and land use type area, to obtain the spatial distribution results of buildings, highways, and rivers affected by landslides in the study area, as shown in Figure 3 , where Figure 3 (a) in the figure is a potential landslide impact range map along a certain highway, Figure 3 (b) in the figure is a potential landslide impact range map along a certain highway, Figure 3 (c) in the figure is a landslide impact range map along a certain highway, Figure 3(d) is a certain highway along the line of the affected area of the building distribution analysis chart, Figure 3 (e) is a certain highway along the line of the potential landslide impact range chart.

[0056] S4.2, construct risk evaluation index system;

[0057] Risk assessment is based on three core elements: risk, vulnerability and exposure, including the following steps:

[0058] S4.2.1, based on the results of landslide simulation, extract the impact range of the stack, and divide the risk level;

[0059] S4.2.2, combined with the type of disaster-bearing body and social and economic data, assess its vulnerability;

[0060] S4.2.3, quantification of the affected area of population, infrastructure and other exposure index. Through the analytic hierarchy process (AHP) to determine the weight of each index, generate different risk level area, focus on high-risk areas and key disaster-bearing body. On this basis, risk visualization and mapping work.

[0061] S4.3, based on the results of risk assessment to develop scientific risk management strategies.

[0062] According to the risk level chart, the limit F development suggestion of high-risk area is put forward, the land use planning is optimized, and the landslide prevention key area is determined, and the engineering measures are preferentially implemented.

[0063] Therefore, the present application adopts the above-mentioned one kind based on numerical simulation's high position slope chain risk assessment method, through numerical simulation accurate screening landslide starting area, provides scientific basis for the prevention and disaster reduction of landslide disaster.

[0064] Finally, it should be noted that: the above examples are used to illustrate the technical solutions of the present application rather than to limit them, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalent replace the technical solutions of the present application, and these modifications or equivalent replacements also cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A high-level slope chain risk assessment method based on numerical simulation, characterized by: The following steps are involved: S1. Landslide susceptibility results and landslide inventory data collection; S2, screening of landslide initiation area; S3, numerical simulation of landslide; S4. Spatial analysis and risk assessment.

2. The method for assessing chain risk of high-level slopes based on numerical simulation according to claim 1 is characterized by: In S1, the scope of the study area is determined, and geological, topographic, and climatic data related to landslide susceptibility are collected, including elevation, slope, soil thickness distribution, soil type, rainfall, and seismic activity. The possibility of regional landslides is assessed using an infinite slope stability model, and landslide catalog data for the study area are collected, which includes detailed records of historical landslide events, including the location, size, occurrence time, and triggering factors of the landslides.

3. The method for assessing chain risk of high-level slopes based on numerical simulation according to claim 1 is characterized by: S2 specifically includes the following steps: S2.

1. Integrate the landslide susceptibility results generated in S1 with the landslide inventory data; S2.

2. Based on the data integrated in S2.1, the kernel density estimation method is used to conduct statistical analysis on the spatial distribution of historical landslide events.

4. The method for assessing chain risk of high-level slopes based on numerical simulation according to claim 3 is characterized by: S2.2 specifically includes the following steps: S2.2.

1. Use the geographic coordinates of the landslide inventory data as the input point set and use the Gaussian kernel function to perform density estimation. Output a kernel density map based on the landslide distribution density and spatial dimension of the study area. S2.2.

2. Use the Segment Anything Model (SAM) to analyze the kernel density map to obtain potential landslide initiation.

5. The method for assessing high-level slope chain risk based on numerical simulation according to claim 1 is characterized by: The FLOW-R model is used in S3 to simulate the movement of landslide materials. The FLOW-R model performs regional sensitivity analysis based on the digital elevation model (DEM) and its derivatives. It calculates the propagation path and deposition range of landslide materials through a numerical iteration method. Its core formula is: Where F is the net force of the landslide material; m is the mass of the landslide material; g is the acceleration due to gravity; θ is the terrain slope; μ is the internal friction coefficient, which controls the frictional resistance of the landslide along the slope; ρ is the density of the landslide material; v is the current velocity of the landslide material; and ξ is the turbulence coefficient.

6. The method for assessing chain risk of high-level slopes based on numerical simulation according to claim 1 is characterized by: S4 specifically includes the following steps: S4.

1. Based on the landslide impact range generated by numerical simulation using FLOW-R, conduct a comprehensive assessment of the landslide impact range and the hazard-bearing volume data through spatial overlay analysis; S4.

2. Construct a risk assessment indicator system; S4.

3. Develop scientific risk management strategies based on risk assessment results.

7. The method for assessing chain risk of high-level slopes based on numerical simulation according to claim 6 is characterized by: The risk assessment in S4.2 is based on three core elements: hazard, vulnerability, and exposure, and includes the following steps: S4.2.

1. Based on the landslide simulation results, extract the impact area and classify the hazard level; S4.2.

2. Assess the vulnerability of the hazard-prone body based on its type and socio-economic data; S4.2.

3. Quantify the population and infrastructure in the affected area.

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