Landslide surge risk assessment method based on parameter inversion and dynamic numerical simulation

Abstract

The invention discloses a landslide surge risk assessment method based on parameter inversion and dynamic numerical simulation. The landslide surge risk assessment method comprises the following steps: S1, acquiring monitoring data of a landslide to be researched, and performing probability inversion to obtain updated parameters; s2, taking the updated parameters obtained in the step S1 as input, establishing a landslide-water body interaction fluid-solid coupling model, and outputting a landslide failure simulation result; and S3, carrying out risk grading evaluation on a simulation result output by the fluid-solid coupling model, and outputting landslide surge risk evaluation data. According to the landslide surge risk assessment method based on parameter inversion and dynamic numerical simulation, the precision and practicability of landslide surge risk assessment are improved.

Description

Technical Field

[0001] This invention relates to a landslide surge risk assessment method based on parameter inversion and dynamic numerical simulation. Background Technology

[0002] The reservoir bank landslide surge disaster chain is characterized by its suddenness, high destructive power, and wide impact, making it a key area for geological disaster prevention and control. Landslide stability is influenced by multiple factors. After continuous rainfall, the soil mass of the landslide experiences a significant increase in soil weight; although the underlying weathered top surface has undulations, the fluctuations are not significant, and the tilt direction is consistent with the topographic slope, thus easily leading to a water cushion effect. The large elevation span of the landslide results in a large landslide thrust; the leading edge of the landslide is the source of the reservoir, and the reservoir bank is an open surface with only hydrostatic pressure; once the leading edge becomes unstable, the resistance of the soil behind it will decrease sharply; the overall slope is relatively steep, easily causing the formation and connection of weak surfaces. If the reservoir water level rises, the groundwater level rises, which intensifies fissure seepage and increases the probability of landslide failure.

[0003] Existing methods for assessing landslide surge hazards include empirical formulas, physical model tests, and numerical simulations. However, these methods have the following problems: (1) The problem of parameter uncertainty has not been systematically considered: existing methods mostly treat geotechnical parameters as constant values ​​without considering their spatial variability and uncertainty, which leads to the evaluation results deviating from reality.

[0004] (2) Risk assessment lacks dynamism and systematicity: Existing methods mostly focus on a single link (such as surge height simulation or regional susceptibility assessment), and have not formed a closed-loop system from parameter inversion to surge simulation and then to risk assessment.

[0005] (3) Insufficient consideration of the vulnerability of the disaster-bearing body: Most methods do not combine the wave propagation distance and the climbing height with the vulnerability of the disaster-bearing body (such as buildings, ships, docks) for quantitative risk analysis.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a landslide surge risk assessment method based on parameter inversion and dynamic numerical simulation, thereby improving the accuracy and practicality of landslide surge risk assessment.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A landslide surge risk assessment method based on parameter inversion and dynamic numerical simulation includes the following steps: S1. Obtain monitoring data of the landslide to be studied, perform probability inversion, and obtain updated parameters; S2. Using the updated parameters obtained in step S1 as input, establish a fluid-structure interaction model of landslide-water interaction and output the simulation results of landslide failure. S3. Perform risk classification assessment on the simulation results output by the fluid-structure interaction model, and output landslide surge risk assessment data.

[0009] In some preferred embodiments, in step S1, probability inversion is performed based on Bayesian statistical principles, using the Markov chain Monte Carlo method, starting from the prior distribution of parameters and approximating the posterior distribution through iterative sampling.

[0010] In some preferred embodiments, probability inversion is performed according to the following formula:

[0011] in, The parameter vector to be inverted, For displacement data monitored on site, For the prior distribution, Let be the likelihood function. Let be the posterior distribution of the parameter to be sought.

[0012] In some preferred embodiments, step S1 further includes: obtaining a comparison map of the posterior distribution and the prior distribution, and ensuring that the inversion results converge reliably by analyzing the residual and parameter iteration trajectory maps.

[0013] In some preferred embodiments, in step S2, a fluid-structure interaction model is constructed based on the CEL method, using the following fluid motion control equation:

[0014] in, Let be the fluid density, and t be the time. This represents the gradient operator (Nabla operator). For velocity vector, For pressure, For dynamic viscosity, It is the acceleration due to gravity; The sliding body motion control equations are as follows:

[0015] in, Let be the yield function. Let q be the shape parameter of the yield function, and q be the deviatoric stress or Mises equivalent stress. It is the internal friction angle. It represents cohesive force.

[0016] In some preferred embodiments, in step S2, the model parameters are calibrated based on in-situ test and experimental data of the study area; and the model is validated by comparing it with empirical formulas.

[0017] In some preferred embodiments, in step S2, the fluid-structure interaction model outputs one or more of the following quantitative results: landslide surge dynamic evolution sequence diagram, landslide movement velocity change process over time, landslide movement distance-time curve, landslide water ingress volume-time curve, and surge climbing law.

[0018] In some preferred embodiments, in step S3, risk classification is performed using a transcendental probability model, wherein the spatial impact probability of the surge is... Defined as the distance of swell movement Exceeding a specific threshold of a certain venue The probability of is shown in the following formula:

[0019] Surging distance exceeding probability As shown in the following formula:

[0020] in, The total number of samples. This is an indicator function.

[0021] In some preferred embodiments, in step S3, based on the landslide failure probability Surpassing the probability of swell Calculate the overall hazard of the landslide surge chain. , as shown .

[0022] In some preferred embodiments, step S3, outputting landslide surge risk assessment data includes one or more of the following: surge distance-overtake probability relationship curve, hazard zoning map, and disaster-bearing body risk statistics table; wherein, disaster-bearing body risk... Quantified as:

[0023] in, For the economic value of the disaster-bearing body, The vulnerability of the disaster-bearing body.

[0024] Taking into account both the physical characteristics of surge waves and the vulnerability of the disaster-bearing body, multi-dimensional risk quantification results are provided.

[0025] The beneficial effects of this invention are as follows: The landslide surge risk assessment method of this invention directly applies the posterior distribution of parameters obtained by Bayesian inversion to the CEL surge model, realizing a closed-loop process from parameter inversion and numerical simulation to risk assessment, thereby improving the consistency and reliability of the assessment; the simulation parameters are closer to reality, improving the accuracy of surge simulation; and it supports dynamic updates based on monitoring data, making it suitable for risk warning and prevention in actual engineering. Attached Figure Description

[0026] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of a landslide surge risk assessment method according to an embodiment of the present invention.

[0028] Figure 2 This is a parameter inversion program architecture diagram according to an embodiment of the present invention.

[0029] Figures 3a to 3d This is a comparison diagram of the posterior and prior distributions of parameters according to an embodiment of the present invention.

[0030] Figure 4 This is a residual iteration trajectory diagram according to an embodiment of the present invention.

[0031] Figures 5a to 5d This is an iterative trajectory diagram of soil and rock parameters according to an embodiment of the present invention.

[0032] Figure 6 This is a schematic diagram of a landslide surge numerical model according to an embodiment of the present invention.

[0033] Figure 7 The figure shows a comparison between the simulated wave height results and the empirical formula for the propagation wave height from 193 to 214 s.

[0034] Figure 8 This is a schematic diagram illustrating the entire dynamic evolution process of landslide surge output in an embodiment of the present invention.

[0035] Figure 9 This is a contour plot of the sliding body velocity over time, output by an embodiment of the present invention.

[0036] Figure 10 This is the landslide movement distance-time curve output by an embodiment of the present invention.

[0037] Figure 11 This is the landslide inflow volume-time curve output by an embodiment of the present invention.

[0038] Figure 12 This is the surge ramp curve output by an embodiment of the present invention.

[0039] Figure 13 This is a sampling surge distance-overshoot probability diagram output by an embodiment of the present invention.

[0040] Figure 14 The curves showing the mean and variance of the surge distance output by an embodiment of the present invention as a function of the number of samplings are shown.

[0041] Figure 15 For experience PDF curves.

[0042] Figure 16 This is a test plot for the generalized extreme value distribution. Detailed Implementation

[0043] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more readily understood by those skilled in the art. It should be noted that the description of these embodiments is for the purpose of aiding understanding the present invention, but does not constitute a limitation thereof.

[0044] After continuous rainfall, reservoirs and similar areas may develop cracks and faults in their soil and rock masses. Instability in these areas can trigger landslide surges, posing significant safety risks. The basic soil and rock conditions for landslides are as follows: the sliding zone soil is mainly composed of gravelly silty clay; the sliding body is mainly composed of residual colluvial gravelly soil, overlain by a thin layer of gravelly silty clay; the sliding bed is mainly composed of relatively hard, strongly weathered, medium-thick to very thick layers of shallow metamorphic sandstone. This invention considers the spatial variability and uncertainty of soil and rock masses, forming a closed-loop system from parameter inversion to surge simulation and then to risk assessment. It combines surge propagation distance, slope height, and the vulnerability of disaster-bearing bodies (such as buildings, ships, and docks) for quantitative risk analysis, providing a systematic, dynamic, and multi-dimensional landslide surge risk assessment method. This achieves a closed-loop process from parameter inversion and surge simulation to risk assessment, improving assessment accuracy and practicality.

[0045] Figure 1 A flowchart illustrating an embodiment of a landslide surge risk assessment method is shown. (Refer to...) Figure 1 As shown, the landslide surge risk assessment method includes the following steps: S1. Obtain monitoring data of the landslide to be studied, perform probability inversion, and obtain updated parameters; S2. Using the updated parameters obtained in step S1 as input, establish a fluid-structure interaction model of landslide-water interaction and output the simulation results of landslide failure. S3. Perform risk classification assessment on the simulation results output by the fluid-structure interaction model, and output landslide surge risk assessment data.

[0046] Step S1 is performed in the parameter inversion module; in this embodiment, a Bayesian-MCMC (Markov-chain Monte Carlo sampling) inversion model is specifically used for parameter iterative updates. In other embodiments, genetic algorithms, neural networks, deep learning, machine learning, and other methods can be used for parameter inversion.

[0047] Step S2 is performed in the surge simulation module; in this embodiment, the CEL (Coupled Eulerian-Lagrangian) method is used for surge simulation. In other embodiments, methods such as SPH (Smoothed Particle Hydrodynamics) and MPM (Matter Point Method), which can achieve system integration with parameter inversion, can be used for surge simulation.

[0048] Step S3 is performed in the risk assessment module; in this embodiment, a transcendental probability model is used for risk classification. In other embodiments, fuzzy comprehensive evaluation, neural networks, or other methods can be used for risk classification.

[0049] The embodiment also provides a landslide surge risk assessment system, which includes: The parameter inversion module is used to acquire monitoring data of the landslide under study, perform probability inversion, and obtain updated parameters. The surge simulation module takes the updated parameters output by the parameter inversion module as input, establishes a fluid-structure interaction model of landslide-water interaction, and outputs the simulation results of landslide failure. The risk assessment module is used to perform risk classification assessment (such as exceedance probability assessment) on the simulation results output by the fluid-structure interaction model, and output landslide surge risk assessment data.

[0050] The landslide surge risk assessment method of the embodiment is described in detail below.

[0051] 1. Inversion of physical and mechanical parameters of slip zone soil based on Bayesian update First, an initial geological model and monitoring data (such as surface crack displacement and deep displacement) of the landslide in the study area were obtained through field investigation, laboratory tests, and in-situ testing (e.g., borehole drilling and displacement monitoring). Based on this, a numerical model was constructed. Key physical and mechanical parameters of the slip zone soil (such as cohesion) were then analyzed. internal friction angle Elastic modulus Poisson's ratio Treating as a random variable, it is substituted into the numerical model, and probability inversion is performed based on Bayesian statistical principles.

[0052] The core formula of Bayesian inference is as follows:

[0053] in, The parameter vector to be inverted, For displacement data monitored on site, For the prior distribution, Let be the likelihood function. Let be the posterior distribution of the parameter to be sought.

[0054] Figure 2 The program structure diagram for inverting the physical and mechanical parameters of slip zone soil is shown. (Refer to...) Figure 2 As shown, the Markov chain Monte Carlo method is employed, starting from the prior distribution of parameters and approximating the posterior distribution through iterative sampling. The DREAM algorithm is also used to accelerate the convergence of multiple Markov chains, ensuring the effectiveness of the project.

[0055] After the inversion is completed, the following can be obtained: Figures 3a to 3d The parameters shown are (in order: cohesion) internal friction angle Elastic modulus Poisson's ratio A comparison chart of the posterior and prior distributions visually illustrates the update process of parameter uncertainty after incorporating measured data. (Refer to...) Figures 3a to 3d Using surface crack deformation monitoring data as the basis for inversion, 25% of the samples after calculation convergence were taken as the posterior distribution of parameters. The mean and maximum frequency of each parameter index were close to those in the survey report, proving the validity of the results.

[0056] Through analysis Figure 4 The residuals shown Iterative trajectory plot and Figures 5a to 5d The parameters shown are (in order: cohesion) internal friction angle Elastic modulus Poisson's ratio The iterative trajectory plot ensures reliable convergence of the inversion results. For example... Figure 4 As shown, after fewer than 1750 iterations, the Rstat values ​​of the random variables are all less than 1.2, indicating that each random variable converges to a stationary posterior distribution. Figures 5a to 5d As shown, the iteration of each parameter exhibits periodic oscillations, indicating that the Markov chain is gradually approaching stable convergence.

[0057] 2. Dynamic numerical simulation of landslide surge based on CEL method The posterior distribution of the parameters obtained from the inversion in Part 1 above is sampled and used as input to establish a fluid-structure interaction model of landslide-water interaction. Figure 6 A numerical model of landslide surge in a reservoir's study area, constructed according to the method of this embodiment, is shown.

[0058] The governing equations for the model are defined as follows: Fluid motion (described using Euler):

[0059] in, For fluid density, For velocity vector, For pressure, For dynamic viscosity, This is the acceleration due to gravity.

[0060] Sliding motion (using Lagrange description, Mohr-Coulomb plasticity model):

[0061] in, The shape parameter is the yield function.

[0062] Model parameter calibration and validation: Based on in-situ test and indoor experimental data of the study area, the model parameters are calibrated, and the model is validated by comparing with empirical formulas. Figure 7 The example shows a comparison curve between the numerical simulation results and empirical formulas output by the model, as shown in the figure. Figure 7 As shown, the propagating wave height obtained in the later stages of the simulation is close to that of the empirical formula.

[0063] Visualization results: The model can output dynamic evolution sequence diagrams of landslide surges, the change process of landslide velocity over time, landslide movement distance-time curves, landslide inflow volume-time curves, and surge climb patterns. These output visualized quantitative structures are used to analyze surge energy and intensity.

[0064] Figure 8 The diagram illustrates the dynamic evolution sequence of landslide surges output by a model from one embodiment, clearly demonstrating the entire process of landslide instability, water ingress, initial wave generation, propagation, uplift to the opposite bank, and the superposition of forward and reverse surges. (Refer to...) Figure 8 As shown, the swell generated after the slide body enters the water propagates outwards. When the swell reaches the opposite bank, it rises along the bank slope to a certain height, forming a reverse swell. Subsequently, the reverse swell propagates and superimposes with the waveform of the forward swell, interacting and consuming energy until the water surface returns to calm. Figure 9 A schematic diagram illustrating the change in landslide velocity over time, output by a model from one embodiment, is shown. (Refer to...) Figure 9 As shown, the velocity of the landslide body first increases and then decreases; the landslide first slides from the rear edge, the velocity of the sliding body gradually increases, forming two high-speed fan-shaped areas, the area of ​​which first increases and then decreases; the high-speed area of ​​the landslide shifts to the front edge.

[0065] Figure 10The example shows a landslide movement distance-time curve output by a model from one embodiment. Figure 11 The following is an example of a model output showing the landslide inflow volume-time curve. Figure 12 The diagram shows a surge ramp curve output by a model from one embodiment. (Refer to...) Figure 12 The first wave appeared at 4 seconds, with a height of 2.42m and a maximum height of 7.10m. At 18 seconds, the swell began to climb along the bank slope, and the climbing height and the horizontal distance to the landslide point both increased and then decreased.

[0066] 3. Quantitative assessment of surge risk considering uncertainties (1) Surge hazard modeling The spatial impact probability of a surge is defined as the surge travel distance. Exceeding a specific threshold of a certain venue The probability of.

[0067]

[0068] The probability of exceeding the swell travel distance is:

[0069] in, The total number of samples. This is an indicator function.

[0070] (2) Overall risk Overall risk of landslide surge hazard chain It is the probability of landslide failure. Surpassing the probability of swell The combined manifestation of:

[0071] (3) Visualization and Zoning Results Through extensive sampling calculations, landslide surge risk assessment data is output, including: surge distance-overshoot probability relationship curve, hazard zoning map, and disaster-bearing body risk statistics table, etc.

[0072] Figure 13 The example outputs a motion distance-overshoot probability curve based on 5000 sampling iterations. (Refer to...) Figure 13 As shown, the distance from the landslide entry point to the opposite bank is 348.53m, which is close to the 95% confidence interval, indicating that the calculation result is reasonable. The surge exceedance probability at the opposite bank is 48.58%, which, multiplied by the failure probability, yields a surge hazard of 4.19%. Based on this, the study area can be divided into five hazard zones: extremely high, high, medium, low, and extremely low, forming a hazard zoning map.

[0073] Figure 14The curves showing the mean and variance of the surge distance output by the embodiment as a function of the number of samples are illustrated. (Refer to...) Figure 14 As shown, the model tends to converge after 1500 samplings, based on the changes in mean and variance with the number of samplings.

[0074] Figure 15 and Figure 16 The results of the KS distribution test are shown. Combined with... Figure 15 The safety factor distribution function shown is and Figure 16 The safety factor probability density function shown, according to the KS overall distribution test results, indicates that the wave motion distance generally follows a generalized extreme value distribution, and the evaluation data output by the example has high validity.

[0075] Quantitative calculation of disaster-bearing bodies: comprehensively considering the economic value of disaster-bearing bodies such as coastal residential buildings, ships, and docks. and fragility (Related to surge rise height or wave height), its risk It can be quantified as:

[0076] Ultimately, a disaster-bearing body risk statistics table can be output, providing a direct and quantitative basis for decision-making regarding disaster prevention and mitigation funding and the formulation of measures.

[0077] Taking a certain source reservoir as an example, the output risk statistics of disaster-bearing bodies such as riverside residential buildings, ships, docks or ferry crossings are shown in Tables 1 to 3 below.

[0078] Table 1. Surge Risk Assessment for Coastal Residential Buildings

[0079] Table 2 Surge Risk Assessment for Vessels

[0080] Table 3 Surge Risk Assessment for Wharves and Ferry Terminals

[0081] The landslide surge risk assessment method in this embodiment first performs inverse analysis of the physical and mechanical parameters of the slip zone soil. Through a Bayesian model derived from parameter inversion, the distribution of soil and rock mass parameters is obtained and used as input variables for subsequent models. A dynamic numerical model of the landslide surge is then constructed and calculated. Based on the model's output, the exceedance probability of the landslide surge's movement distance is evaluated. Specifically, the soil and rock mass parameters... As a random variable, the limit state function 𝒈(𝑿) for random variable X is defined as follows:

[0082] Surging distance exceeding probability That is The probability is given by the following formula:

[0083] In the formula, Determined by the following function:

[0084] The embodiment directly applies the posterior distribution of parameters obtained from Bayesian inversion to the CEL surge model, integrating parameter inversion with surge simulation in a closed loop. It dynamically updates parameters and risks based on real-time monitoring data, achieving a dynamic risk assessment mechanism. Quantitative analysis is performed combining surge propagation distance, ramp height, and vulnerability of the affected body to achieve multi-dimensional risk assessment. Hazard classification is based on the probability of surge movement distance exceeding the threshold. Therefore, the landslide surge risk assessment method in this embodiment achieves a closed-loop process from parameter inversion and numerical simulation to risk assessment, improving the consistency and reliability of the assessment. Through posterior distribution sampling, the simulated parameters are made closer to reality, improving the accuracy of surge simulation. It supports dynamic updates based on monitoring data, making it suitable for risk warning and prevention in practical engineering. It comprehensively considers the physical characteristics of the surge and the vulnerability of the affected body, providing multi-dimensional risk quantification results.

[0085] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0086] As indicated in this specification and claims, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, and these steps and elements do not constitute an exclusive list; the method or apparatus may also include other steps or elements. The term "and / or" as used herein includes any combination of one or more of the associated listed items.

[0087] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0088] Unless otherwise specified in the examples, conventional conditions or manufacturer-recommended conditions were followed. Reagents or instruments used, unless otherwise specified, are commercially available products. As used herein, the term "about" is used to provide flexibility and imprecision associated with a given term, measure, or value. Those skilled in the art can readily determine the degree of flexibility for a particular variable.

[0089] Any step described in any method or process claim may be performed in any order, and is not limited to the order presented in the claims. The limitation of method + function or step + function is used only if all of the following conditions are met in a particular claim: a) it expressly states "method for..." or "step for..."; b) it expressly states the corresponding function. Structures, materials, or actions supporting the method + function are expressly described in the description herein. Therefore, the scope of the invention should be determined solely by the appended claims and their legal equivalents, and not by the description and examples given herein.

[0090] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are preferred embodiments. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and they should not be construed as limiting the scope of protection of the present invention. All equivalent transformations or modifications made according to the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A landslide surge risk assessment method based on parameter inversion and dynamic numerical simulation, characterized in that, Includes the following steps: S1. Obtain monitoring data of the landslide to be studied, perform probability inversion, and obtain updated parameters; S2. Using the updated parameters obtained in step S1 as input, establish a fluid-structure interaction model of landslide-water interaction and output the simulation results of landslide failure. S3. Perform risk classification assessment on the simulation results output by the fluid-structure interaction model, and output landslide surge risk assessment data.

2. The landslide surge risk assessment method according to claim 1, characterized in that, In step S1, probability inversion is performed based on Bayesian statistical principles. The Markov chain Monte Carlo method is used to approximate the posterior distribution by starting from the prior distribution of parameters and iterative sampling.

3. The landslide surge risk assessment method according to claim 2, characterized in that, Probability inversion is performed based on the following formula: ； in, The parameter vector to be inverted, For displacement data monitored on site, For the prior distribution, Let be the likelihood function. Let be the posterior distribution of the parameter to be sought.

4. The landslide surge risk assessment method according to claim 2, characterized in that, Step S1 also includes: obtaining a comparison map of the posterior and prior distributions, and ensuring the reliability of the inversion results by analyzing the residuals and parameter iteration trajectory maps.

5. The landslide surge risk assessment method according to claim 1, characterized in that, In step S2, a fluid-structure interaction model is constructed based on the CEL method, using the following fluid motion control equations: ； in, Let be the fluid density, and t be the time. Represents the gradient operator. For velocity vector, For pressure, For dynamic viscosity, It is the acceleration due to gravity; The sliding body motion control equations are as follows: ； in, Let be the yield function. Let q be the shape parameter of the yield function, and q be the deviatoric stress or Mises equivalent stress. It is the internal friction angle. It represents cohesive force.

6. The landslide surge risk assessment method according to claim 1, characterized in that, In step S2, the model parameters are calibrated based on in-situ test and experimental data from the study area; Model validation is performed by comparing the model with empirical formulas.

7. The landslide surge risk assessment method according to claim 1, characterized in that, In step S2, the fluid-structure interaction model outputs one or more of the following quantitative results: dynamic evolution sequence diagram of landslide surge, change process of landslide movement velocity over time, landslide movement distance-time curve, landslide water ingress volume-time curve, and surge climbing law.

8. The landslide surge risk assessment method according to claim 1, characterized in that, In step S3, a risk classification is performed using a transcendental probability model, where the spatial impact probability of the surge is considered. Defined as the distance of swell movement Exceeding a specific threshold of a certain venue The probability of is shown in the following formula: ； Surging distance exceeding probability As shown in the following formula: ； in, The total number of samples. This is an indicator function.

9. The landslide surge risk assessment method according to claim 8, characterized in that, In step S3, based on the landslide failure probability Surpassing the probability of swell Calculate the overall hazard of the landslide surge chain. , as shown .

10. The landslide surge risk assessment method according to claim 9, characterized in that, In step S3, the output landslide surge risk assessment data includes one or more of the following: surge distance-overtake probability relationship curve, hazard zoning map, and disaster-bearing body risk statistics table; among which, disaster-bearing body risk... Quantified as: ； in, For the economic value of the disaster-bearing body, The vulnerability of the disaster-bearing body.

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