Muck field landslide total probability risk quantitative assessment method considering muck three-dimensional space variability

Abstract

The invention discloses a muck site landslide total probability risk quantitative evaluation method considering muck three-dimensional space variability, comprising the following steps: carrying out muck site drilling investigation to obtain muck site data; random field parameters representing the spatial variability of the muck are obtained; simulating three-dimensional space variation characteristics of the muck; based on a limit equilibrium method, carrying out three-dimensional slope stability evaluation and potential landslide volume estimation on the muck field; possible migration ranges under various landslide volumes are simulated; counting life and property data of a downstream disaster-bearing body, and estimating the vulnerability of the disaster-bearing body according to the landslide strength based on a building and personnel vulnerability evaluation formula; and establishing a muck site landslide total probability risk assessment formula, and carrying out landslide risk quantitative assessment and risk rating. According to the method, the unique spatial variation characteristic of the muck is considered, the stability and the potential slip position and quantity are analyzed from the three-dimensional perspective, and accurate risk assessment is realized in combination with a vulnerability quantitative assessment method.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster prevention and mitigation, and in particular to a method for quantitatively assessing the full probability risk of landslides in a landslide field by considering the three-dimensional spatial variability of landslides. Background Art

[0002] The rapid development of urbanization has produced a huge amount of construction waste. The types of waste are complex, and landfill is the main method of disposal. However, due to the imperfect risk control system of landfill sites, irregular disposal methods such as fast filling speed and over-capacity filling often occur, which can easily cause the landfill site to become unstable. Landfill sites pose a high risk to urban life safety. It is urgent to conduct landslide risk assessments on landfill sites, take prevention and control measures, strengthen risk control, and provide guidance for urban disaster prevention and control.

[0003] At present, few studies can comprehensively assess the landslide risk of landfill sites, and most of them analyze the various sub-items of risk assessment, such as slope stability and migration distance. A few comprehensive landfill risk assessment studies only focus on the hazard factors from the surface environment, such as slope, runoff coefficient, and catchment area, as the focus of risk assessment, and fail to reflect the physical process of landslide risk assessment and the characteristics of the landfill itself. There are many types of landfills in landfills, and the spatial distribution is very complex, with significant spatial variability, which cannot be ignored in landslide risk assessment. Due to the influence of artificial filling, landfill has unique spatial variation characteristics, such as the multi-peak edge distribution characteristics of landfill, and its spatial variability characterization method cannot simply apply the characterization method of natural soil spatial variability. In addition, compared with natural soil landslides, landfill landslides often show high-speed and long-distance characteristics, which needs to be noted when selecting key factors of landslide intensity. Therefore, it is urgent to establish a quantitative assessment method for landslide risk in landfill sites that takes into account the characteristics of landfill, landslide characteristics, and the physical process of landslides. Summary of the invention

[0004] In view of the above reasons, the present invention provides a quantitative assessment method for the full probability risk of landslides in a slag field taking into account the three-dimensional spatial variability of slag. This method takes into account the unique spatial variability characteristics of slag, analyzes the stability and potential sliding position and volume from a three-dimensional perspective, and combines it with a vulnerability assessment function suitable for landslides in slag landfills. Compared with traditional risk assessment methods, this method takes into account the characteristics of artificial slag more and can achieve accurate risk assessment results.

[0005] To achieve the above object, the present invention provides the following technical solutions: The present invention provides a method for quantitatively assessing the total probability risk of landslides in a landslide field taking into account the three-dimensional spatial variability of landslides, comprising the following steps: S1: Conduct drilling survey of the landfill site to obtain geotechnical parameters, water level information and digital elevation model of the landfill site; S2: Based on the sparse borehole data, the random field parameters characterizing the spatial variability of the soil are obtained; S3: Simulate the three-dimensional spatial variation characteristics of soil based on random field theory; S4: Based on the limit equilibrium method, three-dimensional slope stability assessment and potential landslide volume estimation of landfill are carried out; S5: Conduct numerical simulations of landslides to estimate the possible migration range under various landslide volumes; S6: Count the lives and properties of the downstream hazard-bearing bodies, estimate the vulnerability of the hazard-bearing bodies according to the landslide intensity based on the building and personnel vulnerability assessment formula; S7: Establish a full probability risk assessment formula for landslides in landfills to conduct quantitative assessment and risk rating of landslides.

[0006] Furthermore, S1 specifically includes the following steps: conducting indoor or in-situ tests to test geotechnical engineering parameters, wherein the geotechnical engineering parameters include cohesion, internal friction angle, and density of the slag; while obtaining the geotechnical engineering parameters, recording the corresponding spatial position; and obtaining a digital elevation model of the site through drone mapping.

[0007] Furthermore, the random field parameters in S2 include: edge distribution, mutual correlation coefficient, horizontal and vertical autocorrelation distance parameters; the random field parameters characterizing the spatial variability of slag soil need to take into account the unique spatial variability characteristics of slag soil. Due to artificial disturbance, the spatial variability characteristics of slag soil are different from those of natural soil. This is mainly because the slag site is composed of a variety of slag soils, which makes the edge distribution of slag soil parameters in the site multi-peak distribution. The multi-peak distribution is estimated by a mixed Gaussian model, and the mutual correlation between cohesion and internal friction angle is determined according to the Pearson correlation coefficient.

[0008] Furthermore, the horizontal and vertical autocorrelation distance parameters need to map the soil parameters in the original space to the standard normal space using the probability conversion method, and the expressions are as follows: ; In the formula, c and is the cohesion and internal friction angle, X i and X i,NG are the soil parameters in the standard normal space and original space, respectively, i is the cumulative probability density function of the non-Gaussian distribution, estimated by the Gaussian mixture model, It is the inverse function of the standard Gaussian cumulative distribution function; Based on the intensity parameters in the standard normal space, the maximum likelihood method is used to estimate the horizontal and vertical autocorrelation distances. The specific maximum likelihood function is expressed as follows: ; Where, X c and are the cohesion and internal friction angle under standard normal distribution, δ h and δ v are the horizontal and vertical autocorrelation distances, which are hidden in the autocorrelation matrix R A In the present invention, the autocorrelation matrix is ​​determined according to the selected autocorrelation function and the spatial relative position of the borehole samples.

[0009] Furthermore, the S3 specifically includes the following steps: According to the random field parameters, the random field theory is used to simulate the spatial variability of soil parameters, as follows: ; Where U is dimensional independent standard Gaussian random matrix, X U yes The standard Gaussian random matrix with cross-correlation and autocorrelation is L, the first column is the cohesion, the second column is the internal friction angle, A and L C The autocorrelation matrix R is A and the cross-correlation matrix R C The decomposition matrix of X U The geotechnical parameters converted into the original space are as follows: .

[0010] Furthermore, the S4 specifically includes the following steps: Based on the generated three-dimensional random distribution of cohesion and internal friction angle, the slope safety factor is calculated using the three-dimensional limit equilibrium method combined with the surface digital elevation model and the water level elevation in the landfill. The soil between the sliding surface and the surface digital elevation model is regarded as potential sliding soil, and the corresponding volume is calculated; Multiple random fields of cohesion and internal friction angle are generated, and the probability of slope instability is calculated based on the Monte Carlo simulation method to obtain the potential sliding volume distribution.

[0011] Furthermore, the S5 specifically includes the following steps: According to the estimated sliding surface position in S4, the pore water pressure coefficient at the base of the sliding surface is calculated, and the cohesion and internal friction angle of the soil at the base of the sliding surface are obtained; The soil between the sliding surface and the surface digital elevation model is regarded as a potential sliding soil and a landslide initiation source. The digital elevation model around the landfill is obtained to simulate the landslide migration path. Based on the multiple random field simulations of cohesion and internal friction angle in S4 and the corresponding sliding surface distribution, multiple landslide migration path simulations were carried out to estimate the migration range and calculate the spatial impact probability.

[0012] Furthermore, the downstream disaster-prone bodies counted in S6 are mainly divided into two categories: one is the building disaster-prone body with economic losses as the value, including houses, transportation facilities and important projects; the other is the human disaster-prone body with the number of casualties as the value; Count the economic value of buildings that are susceptible to disasters and the population of people that are susceptible to disasters; The landslide intensity factors obtained by numerical simulation in S5: depth, velocity and impact force, are used as the intensity inputs for vulnerability assessment. The building vulnerability assessment formula needs to be determined according to the building type and the high-speed and long-distance migration characteristics of landfill landslides. The vulnerability of personnel is determined according to the corresponding building vulnerability indicators.

[0013] Furthermore, the full probability risk assessment formula for landslide in the landfill established in S7 is as follows: ; Where P f is the probability of slope instability, is the probability of landslide spatial impact range, is the exposure probability of the hazard-bearing body. The exposure probability of the building hazard-bearing body is 1, and the exposure probability of the human hazard-bearing body is determined by the daily routine of the people within the landslide impact area. is the vulnerability of the hazard-bearing body, where I is the landslide intensity, E is the value of the hazard-bearing body, n is the number of Monte Carlo simulations, and m is the number of downstream hazard-bearing bodies.

[0014] The present invention provides a device for quantitatively assessing the total probability risk of landslides in a landfill site taking into account the three-dimensional spatial variability of landslides. The device includes a memory and a processor, wherein the processor is used to implement the process of the method for quantitatively assessing the total probability risk of landslides in a landfill site taking into account the three-dimensional spatial variability of landslides when executing the computer program stored in the memory. The calculation program is written in Python language, and spatial variability parameter estimation and strength parameter random field simulation are realized. Scoops3D software is called to complete three-dimensional slope stability analysis, and Massflow software is called to complete landslide numerical simulation. ArcGIS Pro software is used to compile the downstream hazard-bearing body into a vector file, and its vector file is converted into matrix data through Python to obtain the landslide strength borne by each hazard-bearing body, and the vulnerability assessment and the final total probability risk quantitative assessment are completed in combination with the vulnerability assessment formula.

[0015] Based on the above technical solution, the embodiments of the present invention can at least produce the following technical effects: The present invention considers the unique spatial variation characteristics of slag, analyzes the stability and potential sliding position and volume from a three-dimensional perspective, and combines the vulnerability quantitative assessment method to establish a full-probability slag field landslide quantitative risk assessment method for the first time. Compared with the traditional risk assessment model, this method takes into account the characteristics of artificial slag more and can achieve accurate risk assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0017] Figure 1 The flowchart of the quantitative assessment method of the total probability risk of landslide in landfill considering the three-dimensional spatial variation of landfill; Figure 2 Part (a) represents the strength parameter cohesion of a landfill obtained by drilling exploration. Figure 2 Part (b) shows the internal friction angle of a landfill obtained by drilling exploration; Figure 3 This is the surface elevation model of a landfill site obtained by drone aerial survey; Figure 4 It is a bimodal edge distribution model of strength parameters in a landfill; Figure 5 Part (a) of the above shows the three-dimensional random field of the cohesion parameters in a landfill. Figure 5 Part (b) shows the generated three-dimensional random field of the internal friction angle parameters of a landfill; Figure 6 Part (a) of the graph shows the three-dimensional slope safety factor distribution diagram of a landfill. Figure 6 Part (b) shows the potential sliding volume distribution map of a landfill; Figure 7 Part (a) of the graph shows the potential sliding position of a landfill. Figure 7 Part (b) shows the potential sliding migration range of a landfill; Figure 8 Part (a) shows the assessment results of the personnel risk of landslide in a certain landfill site. Figure 8 Part (b) shows the assessment results of the economic risk of a landfill site. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0019] Figure 1 The flowchart of the quantitative assessment method of the total probability risk of landslide in landfill considering the three-dimensional spatial variability of landfill is mainly divided into 7 steps as follows: S1: Conduct drilling survey of the landfill site to obtain geotechnical parameters, water level information and digital elevation model of the landfill site; S2: Based on the sparse borehole data, the random field parameters characterizing the spatial variability of the soil are obtained; S3: Simulate the three-dimensional spatial variation characteristics of soil based on random field theory; S4: Based on the limit equilibrium method, three-dimensional slope stability assessment and potential landslide volume estimation of landfill are carried out; S5: Conduct numerical simulations of landslides to estimate the possible migration range under various landslide volumes; S6: Count the lives and properties of the downstream hazard-bearing bodies, estimate the vulnerability of the hazard-bearing bodies according to the landslide intensity based on the building and personnel vulnerability assessment formula; S7: Establish a full probability risk assessment formula for landslides in landfills to conduct quantitative assessment and risk rating of landslides.

[0020] In S1, drilling survey of the landfill site is carried out to obtain geotechnical parameters and water level information of the landfill site. The site survey can obtain original soil samples by drilling, and carry out indoor tests to test geotechnical parameters such as cohesion, internal friction angle, and gravity of the landfill; while obtaining geotechnical parameters, the corresponding spatial position is recorded. The digital elevation model of the site is obtained through drone mapping. Figure 2 The cohesion and internal friction angle data were obtained from drilling survey of a landfill. Figure 3 This is a surface model of a landfill site mapped by a drone.

[0021] In S2, random field parameters characterizing the spatial variability of slag are obtained based on sparse borehole data. Random field parameters include: edge distribution, mutual correlation coefficient, horizontal and vertical autocorrelation distance. The random field parameters characterizing the spatial variability of slag need to consider the unique spatial variability characteristics of slag. Due to artificial disturbance, the spatial variability characteristics of slag are different from those of natural soil. This is mainly because the slag field is composed of a variety of slag, which makes the edge distribution of slag parameters in the field multi-peaked. This multi-peak distribution can be estimated by a mixed Gaussian model. The mutual correlation between cohesion and internal friction angle can be determined based on the Pearson correlation coefficient. Figure 4 This is a bimodal edge distribution model of the cohesion and internal friction angle of the landfill obtained using the Gaussian mixture model.

[0022] In S2, random field parameters characterizing the spatial variability of soil slag are obtained based on the sparse drilling data. The horizontal and vertical autocorrelation distance parameters require mapping the soil parameters in the original space to the standard normal space using the equal probability transformation method, and the expressions are as follows: ; In the formula, c and is the cohesion and internal friction angle, X i and X i,NG are the soil parameters in the standard normal space and original space, respectively, i is the cumulative probability density function of the non-Gaussian distribution, which can be estimated by the Gaussian mixture model. It is the inverse function of the standard Gaussian cumulative distribution function. Based on the intensity parameters in the standard normal space, the maximum likelihood method is used to estimate the horizontal and vertical autocorrelation distances. The specific maximum likelihood function is expressed as follows: ; Where, X c and are the cohesion and internal friction angle under standard normal distribution, and are the horizontal and vertical autocorrelation distances, which are hidden in the autocorrelation matrix R A In the above, the autocorrelation matrix is ​​determined by the selected autocorrelation function and the spatial relative position of the borehole samples. The autocorrelation function in the field is the exponential square autocorrelation function, as follows: ; In the formula, , ,and is the relative distance between two positions in the x, y, and z directions. is the autocorrelation matrix R A Based on the landfill survey data, the maximum likelihood method can be used to estimate that the horizontal and vertical autocorrelation distances are 34 meters and 3 meters respectively.

[0023] In S3, the three-dimensional spatial variation characteristics of the muck are simulated based on the random field theory. According to the estimated random field parameters, the random field theory is used to simulate the spatial variability of the muck parameters. The specific steps are as follows: ; Where U is dimensional independent standard Gaussian random matrix, X U yes The standard Gaussian random matrix with cross-correlation and autocorrelation is L, the first column is the cohesion, the second column is the internal friction angle, A and L C The autocorrelation matrix R is A and the cross-correlation matrix R C The decomposition matrix of , they can be determined according to Cholesky decomposition or eigenvalue decomposition. Figure 5 is the random field of cohesion and internal friction angle generated based on the drilling data of the landfill. Then the simulation data X U (Including cohesion X C and internal friction angle ) is converted into geotechnical parameters in the original space as follows: ; In the S4, a three-dimensional slope stability assessment and potential landslide volume estimation of the landfill are performed based on the limit equilibrium method. Based on the generated three-dimensional spatial random distribution of cohesion and internal friction angle, the slope safety factor is calculated using a three-dimensional limit equilibrium method, such as the Bishop method, combined with the surface digital elevation model and the water level elevation in the landfill. The soil between the sliding surface and the surface digital elevation model is regarded as a potential sliding soil, and the corresponding volume is calculated. Multiple random fields of cohesion and internal friction angle are generated, and the probability of slope instability is calculated based on the Monte Carlo simulation method to obtain the potential sliding volume distribution. The slope stability analysis is performed by calling the Scoops3D software using Python. Figure 6 This is the safety factor distribution diagram of the landfill. The calculated probability of instability is 0.9%.

[0024] In the described S5, based on the depth-integrated continuous medium model, the possible migration range under various landslide volumes is estimated. According to the estimated sliding surface position in S4, the pore water pressure coefficient of the sliding surface base is calculated, and the cohesion and internal friction angle of the sliding surface base soil are obtained. The soil between the sliding surface and the surface digital elevation model is regarded as a potential sliding soil body, as the landslide initiation source, the digital elevation model around the landfill is obtained, and the landslide migration path simulation is carried out based on the depth-integrated continuous medium model. According to the multiple random field simulations of cohesion and internal friction angle in S4, and the corresponding sliding surface distribution, multiple landslide migration path simulations are carried out to estimate the migration range and calculate the spatial impact probability. The numerical simulation of the landslide is carried out by calling Massflow software by Python. Figure 6 This is the distribution map of the potential sliding volume of the landslide in the landslide dump, with the volume ranging between 50,000 cubic meters and 600,000 cubic meters. Figure 7 is the potential sliding position of the landslide in the landfill under a certain working condition, Figure 7 is the final migration range of the landslide under the corresponding working condition.

[0025] The downstream disaster-prone bodies counted in S6 are mainly divided into two categories: one is the building disaster-prone body with economic losses as the value, including houses, transportation facilities and important projects; the other is the human disaster-prone body with the number of casualties as the value. The economic value of the building disaster-prone body and the population of the human disaster-prone body are counted. Figure 7 The types of hazard-bearing bodies downstream of the landfill are identified in the figure, including ponds, farmland and buildings. According to the high-speed and long-distance migration characteristics of landfill landslides, the vulnerability assessment formula for reinforced concrete buildings is selected as follows: ; Where V rc Represents the vulnerability value of reinforced concrete buildings, I D , I V and I P is the landslide strength acting on the building, respectively, depth, speed and impact force, which can be obtained through numerical simulation in S5. Based on the vulnerability values ​​obtained from the three landslide strengths, the maximum value is taken as the vulnerability value of the impacted building. The vulnerability assessment formula for non-reinforced concrete buildings is as follows: ; Where V non-rc Represents the vulnerability value of non-reinforced concrete buildings. Once farmland and ponds are buried by landslides, their vulnerability value is considered to be 1. The vulnerability estimation formula for people in buildings is as follows: ; Where V H is the corresponding personnel vulnerability value, V B It is the vulnerability value of the building where the personnel are located.

[0026] The total probability risk assessment formula for landslide in the landfill established in S7 is as follows: ; Where P f is the probability of slope instability, is the probability of landslide spatial impact range, is the exposure probability of the hazard-bearing body. The exposure probability of the building hazard-bearing body is 1, and the exposure probability of the human hazard-bearing body is determined by the daily routine of the people within the landslide impact area. is the vulnerability of the hazard-bearing body, where I is the landslide intensity, E is the value of the hazard-bearing body, n is the number of Monte Carlo simulations, and m is the number of downstream hazard-bearing bodies.

[0027] Figure 8 The estimated personnel risk and economic losses are: Since the landslide is short, it will not impact the downstream villages, so the personnel risk is 0. The economic losses are mainly the farmland and ponds buried by the landslide, with a potential economic loss of 89,000 yuan.

[0028] The quantitative risk assessment results obtained in S7 are used to rate the risk of landslides in the landslide dump according to the risk levels in the geological disaster prevention and control regulations. If the death toll is greater than or equal to 30, or the direct economic loss is greater than or equal to 10 million yuan, the risk level is extremely high; if the death toll is less than 30, but greater than or equal to 10, or the direct economic loss is less than 10 million yuan, but greater than or equal to 5 million yuan, the risk level is high; if the death toll is less than 10, but greater than or equal to 3, or the direct economic loss is less than 5 million yuan, but greater than or equal to 1 million yuan, the risk level is medium; if the death toll is less than 3, or the direct economic loss is less than 1 million yuan, the risk level is low. According to the quantitative risk assessment results of the landslide in the landslide dump, the risk level is low.

[0029] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of the present invention to be protected is defined by the attached claims and their equivalents.

Claims

1. A quantitative assessment method for the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill, characterized by: The following steps are involved: S1: Conduct drilling survey of the landfill site to obtain geotechnical parameters, water level information and digital elevation model of the landfill site; S2: Based on the sparse borehole data, the random field parameters characterizing the spatial variability of the soil are obtained; S3: Simulate the three-dimensional spatial variation characteristics of soil based on random field theory; S4: Based on the limit equilibrium method, three-dimensional slope stability assessment and potential landslide volume estimation of landfill are carried out; S5: Conduct numerical simulations of landslides to estimate the possible migration range under various landslide volumes; S6: Count the lives and properties of the downstream hazard-bearing bodies, estimate the vulnerability of the hazard-bearing bodies according to the landslide intensity based on the building and personnel vulnerability assessment formula; S7: Establish a full probability risk assessment formula for landslides in landfills to conduct quantitative assessment and risk rating of landslides.

2. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 1 is characterized in that: The S1 specifically includes the following steps: Conduct indoor or in-situ tests to test geotechnical parameters, including cohesion, internal friction angle, and gravity of the slag; While obtaining geotechnical parameters, the corresponding spatial positions are recorded; The digital elevation model of the site was obtained through drone mapping.

3. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 1 is characterized in that: The random field parameters in S2 include: edge distribution, mutual correlation coefficient, horizontal and vertical autocorrelation distance parameters; the random field parameters that characterize the spatial variability of slag need to take into account the unique spatial variability characteristics of slag. Due to artificial disturbance, the spatial variability characteristics of slag are different from those of natural soil. This is mainly because the slag yard is composed of a variety of slag, which makes the edge distribution of slag parameters in the site multi-peak distribution. The multi-peak distribution is estimated by a mixed Gaussian model, and the mutual correlation between cohesion and internal friction angle is determined based on the Pearson correlation coefficient.

4. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 3 is characterized in that: The horizontal and vertical autocorrelation distance parameters need to map the soil parameters in the original space to the standard normal space using the probability conversion method. The expressions are as follows: ； In the formula, c and is the cohesion and internal friction angle, X i and X i,NG are the soil parameters in the standard normal space and original space, respectively, i is the cumulative probability density function of the non-Gaussian distribution, estimated by the Gaussian mixture model, It is the inverse function of the standard Gaussian cumulative distribution function; Based on the intensity parameters in the standard normal space, the maximum likelihood method is used to estimate the horizontal and vertical autocorrelation distances. The specific maximum likelihood function is expressed as follows: ； Where, X c and are the cohesion and internal friction angle under standard normal distribution, and are the horizontal and vertical autocorrelation distances, which are hidden in the autocorrelation matrix R A In the present invention, the autocorrelation matrix is ​​determined according to the selected autocorrelation function and the spatial relative position of the borehole samples.

5. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 1 is characterized in that: The S3 specifically includes the following steps: According to the random field parameters, the random field theory is used to simulate the spatial variability of soil parameters, as follows: ； Where U is dimensional independent standard Gaussian random matrix, X U yes The standard Gaussian random matrix with cross-correlation and autocorrelation is L, the first column is the cohesion, the second column is the internal friction angle, A and L C The autocorrelation matrix R is A and the cross-correlation matrix R C The decomposition matrix of X U The geotechnical parameters converted into the original space are as follows: 。 6. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 1 is characterized in that: The S4 specifically includes the following steps: Based on the generated three-dimensional random distribution of cohesion and internal friction angle, the slope safety factor is calculated using the three-dimensional limit equilibrium method combined with the surface digital elevation model and the water level elevation in the landfill. The soil between the sliding surface and the surface digital elevation model is regarded as potential sliding soil, and the corresponding volume is calculated; Multiple random fields of cohesion and internal friction angle are generated, and the probability of slope instability is calculated based on the Monte Carlo simulation method to obtain the potential sliding volume distribution.

7. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 1 is characterized in that: The S5 specifically includes the following steps: According to the estimated sliding surface position in S4, the pore water pressure coefficient at the base of the sliding surface is calculated, and the cohesion and internal friction angle of the soil at the base of the sliding surface are obtained; The soil between the sliding surface and the surface digital elevation model is regarded as a potential sliding soil and a landslide initiation source. The digital elevation model around the landfill is obtained to simulate the landslide migration path. Based on the multiple random field simulations of cohesion and internal friction angle in S4 and the corresponding sliding surface distribution, multiple landslide migration path simulations were carried out to estimate the migration range and calculate the spatial impact probability.

8. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 1 is characterized in that: The downstream disaster-prone bodies counted in S6 are mainly divided into two categories: one is the building disaster-prone body with economic losses as the value, including houses, transportation facilities and important projects; the other is the human disaster-prone body with the number of casualties as the value; Count the economic value of buildings that are susceptible to disasters and the population of people that are susceptible to disasters; The landslide intensity factors obtained by numerical simulation in S5: depth, velocity and impact force, are used as the intensity inputs for vulnerability assessment. The building vulnerability assessment formula needs to be determined according to the building type and the high-speed and long-distance migration characteristics of landfill landslides. The vulnerability of personnel is determined according to the corresponding building vulnerability indicators.

9. The method for quantitatively assessing the total probability risk of landslides in a landfill considering the three-dimensional spatial variability of landfill according to claim 1 is characterized in that: The total probability risk assessment formula for landslide in the landfill established in S7 is as follows: ； Where P f is the probability of slope instability, is the probability of landslide spatial impact range, is the exposure probability of the hazard-bearing body. The exposure probability of the building hazard-bearing body is 1, and the exposure probability of the human hazard-bearing body is determined by the daily routine of the people within the landslide impact area. is the vulnerability of the hazard-bearing body, where I is the landslide intensity, E is the value of the hazard-bearing body, n is the number of Monte Carlo simulations, and m is the number of downstream hazard-bearing bodies.

10. A device for quantitatively assessing the total probability risk of landslides in a landfill site taking into account the three-dimensional spatial variability of landfill, the device comprising a memory and a processor, wherein: The processor is used to execute the computer program stored in the memory to implement the process of the method for quantitatively assessing the full probability risk of landslide in a landfill considering the three-dimensional spatial variability of the landfill as described in any one of claims 1 to 9.

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