Probabilistic landslide tsunami disaster assessment method, device, and medium based on a logic tree

Abstract

The present invention discloses a probabilistic landslide tsunami disaster assessment method, device, and medium based on a logic tree, including: obtaining landslide data corresponding to the area to be detected; constructing a probabilistic landslide scenario set based on the logic tree method and deriving the geometric parameters of the landslide scenario; the first-level, second-level, third-level, and fourth-level nodes in the logic tree are landslide location, landslide volume, tsunami arrival point, and tsunami wave height; obtaining historical landslide tsunami records and submarine topography data of the area to be detected, and calculating the initial water level field of the tsunami wave through a first tsunami propagation numerical model according to the landslide location and volume in the probabilistic landslide scenario set; performing simulation using a second tsunami propagation numerical model based on the initial water level field of the tsunami wave to obtain the propagation path, maximum wave amplitude, and influence range of the tsunami; estimating the spatial probability of potential landslides in the area to be detected, the annual occurrence probability of landslides in each volume interval, and the probability that the maximum wave amplitude of the triggered tsunami does not exceed the tsunami wave height threshold at the tsunami arrival point.

Description

Technical Field

[0001] The present invention belongs to the field of marine science and technology, and particularly relates to a method, device, and medium for probabilistic landslide tsunami disaster assessment based on a logic tree. Background Art

[0002] Tsunami disasters occur with a low frequency but have extremely strong disaster-causing capabilities and are one of the most lethal marine disasters. Common tsunami triggering mechanisms include earthquakes, submarine landslides, and volcanic eruptions, etc. Among them, submarine landslides are the second largest tsunami source after earthquakes. Compared with earthquakes, submarine landslides usually may trigger larger tsunamis with a more local impact range, and the prediction and analysis of submarine landslide tsunamis are still in an immature stage. Submarine landslide tsunamis can damage marine facilities and even directly threaten the lives of people in coastal cities.

[0003] The assessment of landslide tsunami disasters is similar to that of earthquake tsunamis and generally can be divided into two methods: deterministic assessment based on typical landslide scenarios and probabilistic assessment based on a large number of landslide scenarios. In the past two decades, although the research on landslide tsunamis has mainly focused on the deterministic disaster assessment of typical landslide scenarios, significant progress has also been made in probabilistic landslide tsunami disaster assessment. Probabilistic landslide tsunami disaster assessment requires specifying probability distribution functions for key landslide source parameters (such as landslide volume and location, etc.), applying statistical simulation methods such as the Monte Carlo method and the logic tree method to construct a large number of random landslide scenarios, and calculating the generation and propagation process of landslide tsunamis through numerical simulation. Finally, statistical analysis is carried out based on the simulation results to evaluate the exceedance probability of each selected tsunami intensity index along the coast. Each landslide size can be characterized by a series of landslide source parameters, such as volume, area, and thickness, etc., and any known parameters that may affect the modeling of landslide tsunami disasters. Among them, the landslide volume is the most important factor affecting the generation of tsunamis caused by landslides and the size of the associated wave heights. Therefore, it is very important to perform stratified sampling of landslide body parameters in order to represent the overall variation of landslide source parameters. Quantifying as much as possible and accurately describing the size of the landslide helps to scientifically understand and recognize landslide tsunami disasters and their impacts.

[0004] In order to obtain relatively reliable results and consider the uncertainties of different factors to provide more reliable theoretical support for the estimation of the occurrence probability of tsunami disasters, probabilistic tsunami disaster assessment usually adopts the Monte Carlo method to establish a scenario set. Among them, the Monte Carlo method approximates the probability distribution and statistical characteristics of the problem through a large number of random samplings and simulations. In order to obtain sufficiently accurate results, it is usually necessary to establish a scenario set with a quantity reaching hundreds of thousands or even millions. Because numerical simulation consumes a large amount of computing resources, especially when high-precision results are required, the computing cost will increase significantly. Therefore, previous studies mostly adopted numerical models or simpler empirical formulas that require less computing resources. Although the computing cost can be reduced, the effect is poor, the computing accuracy is reduced, and usually only the maximum wave amplitude at a specific location is output. Summary of the Invention

[0005] The object of the present invention is to overcome the deficiencies in the prior art and provide a method, device, and medium for probabilistic landslide tsunami disaster assessment based on a logic tree.

[0006] In a first aspect, an embodiment of the present invention provides a method for probabilistic landslide tsunami disaster assessment based on a logic tree, the method comprising:

[0007] Obtain landslide data corresponding to the area to be detected;

[0008] Construct a probabilistic landslide scenario set based on the logic tree method, and deduce the geometric parameters of the landslide scenario according to the probabilistic landslide scenario set; wherein, the first-level node in the logic tree is the landslide location, the second-level node is the landslide volume, the third-level node is the tsunami arrival point, and the fourth-level node is the tsunami wave height;

[0009] Obtain the historical landslide tsunami records and submarine topography data of the area to be detected, and calculate the initial water level field of the tsunami wave through the first tsunami propagation numerical model according to the landslide location and landslide volume in the probabilistic landslide scenario set;

[0010] According to the initial water level field of the tsunami wave, use the submarine topography data corresponding to the area to be detected and the second tsunami propagation numerical model for simulation to obtain the propagation path, maximum wave amplitude, and influence range of the tsunami;

[0011] Estimate the spatial probability of potential landslides occurring at each landslide location according to factors including historical landslide tsunami records, seismic activities, and slopes in the area to be detected;

[0012] Obtain the distribution law of the landslide volume and the annual average incidence rate of landslides according to the historical landslide data corresponding to the area to be detected, so as to calculate the spatial probability of potential landslides occurring in each landslide volume interval;

[0013] Calculate the probability that the maximum wave amplitude of the tsunami triggered in the probabilistic landslide scenario set does not exceed the tsunami wave height threshold at the tsunami arrival point.

[0014] In a second aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, the memory is coupled to the processor; wherein, the memory is used to store program data, and the processor is used to execute the program data to implement the above-mentioned method for probabilistic landslide tsunami disaster assessment based on a logic tree.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above-mentioned method for probabilistic landslide tsunami disaster assessment based on a logic tree.

[0016] Fourthly, an embodiment of the present invention provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the above-mentioned probabilistic landslide tsunami disaster assessment method based on a logic tree.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0018] (1) The present invention integrates multidisciplinary knowledge such as marine geology, physical geography, geophysics, and numerical simulation, synthesizes data and theories in multiple fields, adopts an improved logic tree method, considers uncertain factors, constructs landslide scenarios, and uses a method of coupling empirical formulas and numerical models for simulation, comprehensively analyzing the occurrence probability of landslide tsunami events, the wave propagation law, and the impact on coastal areas, so that the disaster assessment is not limited to specific locations.

[0019] (2) The present invention constructs a set of probabilistic landslide scenarios based on the logic tree method. Among them, the first-level nodes in the logic tree are landslide locations, the second-level nodes are landslide volumes, the third-level nodes are tsunami arrival points, and the fourth-level nodes are tsunami wave heights; the present invention takes landslide locations and landslide volumes as the main branches, streamlines the branches in the logic tree method, and can not only calculate the exceedance probability of the nearshore wave amplitude at a location, but also evaluate the probabilistic landslide tsunami disasters in the entire basin range. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a flowchart of the probabilistic landslide tsunami disaster assessment method provided by the embodiment of the present invention;

[0022] Figure 2 It is a framework diagram of the improved logic tree method provided by the embodiment of the present invention;

[0023] Figure 3 It is a result diagram of the occurrence probability of submarine landslides in each volume interval provided by the embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of an electronic device provided by the embodiment of the present invention. Detailed Embodiments

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] It should be noted that, without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0027] As Figure 1 shown, the embodiment of the present invention provides a probabilistic landslide tsunami disaster assessment method based on a logic tree. The method includes the following steps:

[0028] Step S1: Obtain the landslide data corresponding to the area to be detected.

[0029] Furthermore, the landslide data includes landslide location, landslide volume, landslide area, and landslide thickness.

[0030] Step S2: Construct a probabilistic landslide scenario set based on the logic tree method, and derive the geometric parameters of the landslide scenario according to the probabilistic landslide scenario set; wherein, the first-level node in the logic tree is the landslide location, the second-level node is the landslide volume, the third-level node is the tsunami arrival point, and the fourth-level node is the tsunami wave height.

[0031] Furthermore, as Figure 2 shown, in this example, the area to be detected is divided into i sub-areas of the same size and reasonable accuracy (i = 1, 2, 3...), and the sub-areas are numbered in the order of increasing longitude and latitude. The length of the unit grid should be of the same order of magnitude as the volume of the largest landslide derived from historical landslide events in the study area.

[0032] Furthermore, in this example, the second-level node defines 6 landslide scale levels, each level spanning 3 orders of magnitude. In the order of increasing level, they are: the first-level landslide scale (very small, 10 -3 –10 0 m 3 ), the second-level landslide scale (small, 10 0 –10 3 m 3 ), the third-level landslide scale (medium, 10 3 –10 6 m 3 ), the fourth-level landslide scale (large, 10 6 –10 9 m 3), the fifth level of landslide scale (giant, 10 9 –10 12 m 3 ), the sixth level of landslide scale (extreme, monster, 10 12 –10 15 m 3 ), constraining the area and thickness of the landslide. Therefore, the area, thickness and volume are not sampled independently. In this example, since the landslide volume of the "first level landslide scale" is small, in order to save time and computing resources, the present invention does not consider it for the time being, and only considers the remaining 5 landslide scales, that is, the volume of 10 0 –10 15 m 3 The maximum landslide volume V in the study area is determined based on known historical landslide events. Lmax and minimum value V Lmin , and judge that the landslide volume range is in a certain or several scale levels. The present invention uses a logarithmic scale to evaluate the landslide volume, with V L Logarithm (base 10) of V L The measurement value of lgV Lmin ≤ lgV L ≤lgV Lmax A dataset collection for logV L Set the appropriate discrete interval interval , and obtain j volume intervals (j=1, 2, 3 …). For each landslide volume interval C j , it is planned to construct i landslide scenarios at different locations, and the number of locations is consistent with the number of grids in the study area. In this way, the total number of scenarios to be simulated is i×j, and the landslide scenario n is [1, i×j].

[0033] It should be noted that in this example, the landslide scale classification standard is set to conduct stratified sampling of landslide parameters in the study area. The landslide volume is the most important factor affecting the tsunami caused by landslides and the size of the related wave height. The scale of the landslide is quantified as much as possible and accurately described, thereby improving the accuracy of the assessment of potential landslide tsunami disasters in the future, and helping to scientifically understand and recognize landslide tsunami hazards and impacts.

[0034] Furthermore, the process of deriving the landslide scenario geometric parameters according to the probabilistic landslide scenario set in step S2 specifically includes:

[0035] For each landslide volume interval, the middle value of the landslide volume interval is taken as the characteristic volume V of the landslide volume interval;

[0036] Using the empirical relationship of landslide geometric parameters, according to the characteristic volume V = L×W×T, determine the geometric parameters of the landslide scenario of the landslide body, including the length L, width W, and thickness T.

[0037] Among them, the landslides moving on the slope include sliding and slumping; for the initial sliding length Lslide, the sliding width is 0.25 × Lslide, and the sliding thickness is 0.01×Lslide; for the initial slumping length Lslump, the width is equal to Lslump, and the slumping thickness is 0.1×Lslump.

[0038] Step S3: Obtain the historical landslide tsunami records and submarine topography data of the area to be detected, and calculate the initial water level field of the tsunami wave through the first tsunami propagation numerical model according to the landslide location and landslide volume in the probabilistic landslide scenario set; according to the initial water level field of the tsunami wave, use the submarine topography data corresponding to the area to be detected and the second tsunami propagation numerical model to simulate, and obtain the propagation path, maximum wave amplitude, and influence range of the tsunami.

[0039] Among them, high-resolution submarine topography data can be obtained from public websites such as GEBCO (General Bathymetric Chart of the Oceans).

[0040] Furthermore, the first tsunami generation numerical model includes but is not limited to models such as the TSUNAMI3D model or NHWAVE; the second tsunami propagation numerical model includes but is not limited to models such as the NEOWAVE model or FUNWAVE-TVD.

[0041] Step S4: Estimate the spatial probability of potential landslides in the area to be detected according to factors including the historical landslide tsunami records, seismic activities, and slopes in the area to be detected; obtain the distribution law of the landslide volume according to the landslide data corresponding to the area to be detected, and estimate the annual occurrence probability of landslides in each volume interval in the area to be detected; calculate the probability that the maximum wave amplitude of the tsunami triggered in the probabilistic landslide scenario set does not exceed the tsunami wave height threshold at the tsunami arrival point.

[0042] Specifically, each event probability value can be obtained through the analysis of the simulation results of the landslide-tsunami numerical model. Combining the numerical simulation results and historical records, a probabilistic method is introduced, comprehensively considering factors such as the occurrence probability of landslide events and the influence range of tsunami wave propagation, to obtain the disaster risk assessment results under different scales of landslides and disaster levels, providing scientific guidance for regional tsunami disaster prevention and emergency response. Similar to earthquakes, for some data with the occurrence time of landslides, when assessing landslide-tsunami disasters, in addition to the weights of landslide location and landslide scale, the annual occurrence probability of landslides is also required, that is, to quantify the probability that a certain tsunami intensity z (nearshore tsunami wave amplitude) at the target location k exceeds the threshold Z within a given time window ΔT. Then, the potential landslide-tsunami hazard curves and hazard maps for the study area and surrounding coastal target points are given.

[0043] Furthermore, the spatial probability of potential landslides occurring at the i-th landslide location The estimation process includes:

[0044] Each grid is assigned a weight to estimate the tendency of landslides to occur in each grid. Consider factors such as historical landslide events, seismic activity, and slope to estimate the spatial probability of potential landslides . In a cell, the assigned weights are as follows: If the average slope φ' of the grid is gentle (between 3° and 5°), it is 10; if the average depth of the grid is between 1,000 – 1,300 m, it is 10. If the average slope is steep (> 5°), it is 20. If it does not belong to the above situations, it is 1, and the possibility of landslides occurring in deep-sea basins and coastal areas is not completely excluded. If the factor of safety Fs < 1 (unstable), it is 10. Then, consider historical landslide events, map them using marine geological techniques, and update the weights by increasing the number of past events in the corresponding cells. The weights of each cell range from 1 to 40. A weight of 1 represents a slope < 3°, a depth less than 1,000 m or greater than 1,300 m, and low seismic activity, indicating a low possibility of large-scale landslides occurring in the relevant cell. A weight of 40 represents a large slope gradient, a depth between 1,000 and 1,300 m, and high seismic activity, indicating a high possibility of large-scale landslides occurring in the relevant cell. By normalizing the sum of the weights, the spatial probability of potential landslides can be obtained .

[0045] Furthermore, the spatial probability of potential landslides occurring annually corresponding to each landslide volume interval The estimation process includes:

[0046] The probability of at least one landslide with a volume not exceeding V L level landslide is called V LThe cumulative probability distribution F(lgV L ):

[0047]

[0048]

[0049] where N L is the cumulative landslide quantity, V L is the landslide volume, b is the cumulative power-law scaling exponent, a is a constant, V Lmax is the maximum value of the historical landslide volume, and V Lmin is the minimum value of the historical landslide volume;

[0050] Substituting the landslide cumulative quantity-volume distribution relation into the above formula, we get:

[0051]

[0052] Subtracting the cumulative distribution functions corresponding to the upper and lower limits of each volume interval gives the occurrence probability of each interval. According to the historical landslide records, the average annual occurrence rate L of landslides with a landslide volume lgV Lmin greater than lgV is as follows:

[0053]

[0054] where represents the total number of occurrences of landslides with a landslide volume lgV L greater than lgV Lmin in the area to be detected, and A is the statistical time period;

[0055] Combined with the average annual occurrence rate of landslides, the spatial probability of potential landslides occurring annually in each landslide volume interval in the study area can be calculated as follows:

[0056]

[0057] And the spatial probability of potential landslides occurring annually in each landslide volume interval is used as the branch weight of node 2 of the logic tree. The spatial probability of potential landslides occurring annually in each landslide volume interval provides information on the time scale of landslides.

[0058] Furthermore, the estimation process of the spatial probability of a tsunami triggered by a landslide at the tsunami arrival point k includes:

[0059]

[0060] In the formula, is the number of times the tsunami wave reaches the target location k in the simulation result.

[0061] Furthermore, the spatial probability that the maximum wave amplitude z of the tsunami triggered by a landslide is greater than the wave amplitude threshold Z at the tsunami arrival point k The estimation process includes:

[0062]

[0063] In the formula, is the number of times the maximum wave amplitude of the tsunami wave exceeds the threshold Z in the simulation reaching the target point.

[0064] Assume that each landslide scenario in the scenario set is independent, that is, V j The annual non-occurrence probability of landslides in the volume interval approximately follows a Poisson distribution, then N j The occurrence probability of = 0 is:

[0065]

[0066] Among them, N j is the potential annual occurrence frequency of landslides in the V j volume interval in the study area. Then The occurrence probability of is:

[0067]

[0068] Considering the weights of each branch in the logic tree, the annual occurrence probability of the nth landslide-tsunami scenario in the scenario set is:

[0069]

[0070] In the formula, is the spatial probability of potential landslides occurring at the i-th landslide location, represents the spatial probability of the tsunami triggered by the landslide at the tsunami arrival point k, represents the spatial probability that the maximum wave amplitude z of the tsunami triggered by the landslide is greater than the wave amplitude threshold Z at the tsunami arrival point k.

[0071] The probability that the maximum wave amplitude of the tsunami triggered by any landslide scenario n does not exceed the wave amplitude threshold Z at the target location k can be expressed as:

[0072]

[0073]

[0074] The annual occurrence joint probability that the maximum wave amplitude of the tsunami does not exceed Z at the target location k is:

[0075] ​

[0076] The annual occurrence probability that the maximum wave amplitude of a tsunami exceeds Z at least once at the target location k is:

[0077]

[0078] where N Z is the total number of annual tsunami occurrence events where the maximum wave amplitude of the tsunami exceeds Z at the target location, and N Z |n is the annual occurrence frequency of the maximum wave amplitude of the tsunami triggered by the nth scenario exceeding Z at the target location k; is the spatial probability of a potential landslide occurring at the ith landslide location, is the spatial probability of a potential landslide occurring annually corresponding to the jth landslide volume interval, represents the spatial probability of a landslide-triggered tsunami at the tsunami arrival point k, represents the spatial probability that the maximum wave amplitude z of a landslide-triggered tsunami is greater than the wave amplitude threshold Z at the tsunami arrival point k.

[0079] Example 1

[0080] In this example, submarine landslide data in a certain sea area is obtained, and a probabilistic landslide-tsunami disaster assessment method based on a logic tree provided by the present invention is further elaborated. For ease of explanation, submarine landslide data in a certain sea area is taken as an example. The specific steps are as follows:

[0081] The first part includes establishing a landslide database and preliminarily analyzing the landslide data, and then constructing a suitable set of landslide scenarios; specifically:

[0082] Establish a landslide database in a certain sea area: A total of 1500 submarine landslide data are collected and sorted from public websites or published literature.

[0083] Construct a set of landslide scenarios: The length of the unit grid should be of the same order of magnitude as the volume of the largest landslide derived from historical landslide events in the study area. Therefore, a certain sea area is divided into 40 sub-regions of the same size and reasonable accuracy (i = 1, 2, 3...), and the sub-regions are numbered in the order of increasing longitude and latitude. Then, the landslide volume is evaluated using a logarithmic scale, and the logarithm (base 10) of V L is used to convert the measurement value of V L to obtain lgV Lmin ≤ lgV L ≤lgV Lmax to obtain a data set collection, and the discrete interval of lgV L is set to 0.1 to obtain 22 volume intervals (j = 1, 2, 3...). For each landslide volume interval C j, it is planned to construct landslide scenarios at 40 different locations, and the number of locations is consistent with the number of grids in the study area. In this way, the total number of scenarios to be simulated is 40×22, and the landslide scenario n takes values in [1, 880].

[0084] Derive the geometric parameters of each landslide scenario: For each landslide volume interval C j , take the middle value of the interval as the characteristic volume of each interval. Then, using the empirical relationship of landslide geometric parameters, based on the characteristic volume V = L×W×T, derive the relevant parameters such as the length L, thickness T, and width W of the landslide body.

[0085] The second part is to simulate the generation and subsequent propagation of landslide tsunamis; specifically including:

[0086] Collect historical landslide tsunami records and seabed topography data in a certain sea area to prepare for subsequent numerical simulations and disaster assessments. In this example, historical landslide tsunami records are mainly obtained from relevant publicly published documents, and high-resolution seabed topography data can be obtained from public websites such as GEBCO (General Bathymetric Chart of the Oceans);

[0087] Obtain historical landslide tsunami records and seabed topography data in the area to be detected, and calculate the initial water level field of the tsunami wave through the first tsunami propagation numerical model according to the landslide location and landslide volume in the probabilistic landslide scenario set;

[0088] According to the initial water level field of the tsunami wave, use the seabed topography data corresponding to the area to be detected and the second tsunami propagation numerical model for simulation to obtain the propagation path, maximum wave amplitude, and influence range of the tsunami.

[0089] The third part is to evaluate the landslide tsunami disaster; including:

[0090] Estimate the spatial probability of potential landslides occurring at each landslide location according to factors including historical landslide tsunami records, seismic activities, and slopes in the area to be detected;

[0091] Obtain the distribution law of landslide volume and the annual average incidence rate of landslides according to the historical landslide data corresponding to the area to be detected, so as to calculate the spatial probability of potential landslides occurring annually in each landslide volume interval; Figure 3 Show the annual occurrence probability of each volume interval of potential landslides .

[0092] Calculate the probability that the maximum wave amplitude of the tsunami triggered in the probabilistic landslide scenario set does not exceed the tsunami wave height threshold at the tsunami arrival point.

[0093] Correspondingly, the present application further provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the probabilistic landslide tsunami disaster assessment method based on a logic tree as described above. As Figure 4 shown, it is a hardware structure diagram of any device with data processing capabilities where the probabilistic landslide tsunami disaster assessment method based on a logic tree provided by an embodiment of the present invention is located. In addition to Figure 4 the processors, memory, and network interfaces shown, any device with data processing capabilities where the device in the embodiment is located usually may further include other hardware according to the actual functions of the any device with data processing capabilities, which will not be elaborated here.

[0094] Correspondingly, the present application further provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the probabilistic landslide tsunami disaster assessment method based on a logic tree as described above is implemented. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Further, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or will be output.

[0095] Those skilled in the art will readily think of other implementation schemes of the present application after considering the specification and practicing the content disclosed herein. The present application is intended to cover any variations, uses, or adaptive changes of the present application, and these variations, uses, or adaptive changes follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary.

[0096] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A probabilistic landslide tsunami disaster assessment method based on a logic tree, characterized in that, The method includes: Obtaining landslide data corresponding to the area to be detected; Constructing a probabilistic landslide scenario set based on the logic tree method, and deriving landslide scenario geometric parameters according to the probabilistic landslide scenario set; wherein, the first-level node in the logic tree is the landslide location, the second-level node is the landslide volume, the third-level node is the tsunami arrival point, and the fourth-level node is the tsunami wave height; Obtaining historical landslide-tsunami records and submarine topography data of the area to be detected, and calculating the initial water level field of the tsunami wave through the first tsunami propagation numerical model according to the landslide location and landslide volume in the probabilistic landslide scenario set; Performing simulation based on the initial water level field of the tsunami wave, the corresponding submarine topography data of the area to be detected, and the second tsunami propagation numerical model to obtain the propagation path, maximum wave amplitude, and influence range of the tsunami; Estimating the spatial probability of potential landslides occurring at each landslide location according to factors including historical landslide-tsunami records, seismic activities, and slope in the area to be detected; Obtaining the distribution law of landslide volume and the annual average incidence rate of landslides according to the corresponding historical landslide data of the area to be detected, so as to calculate the spatial probability of potential landslides occurring annually corresponding to each landslide volume interval; Calculating the probability that the maximum wave amplitude of the tsunami triggered in the probabilistic landslide scenario set does not exceed the tsunami wave height threshold at the tsunami arrival point.

2. The probabilistic landslide tsunami disaster assessment method based on a logic tree according to claim 1, wherein The landslide data includes landslide location, landslide volume, landslide area, and landslide thickness.

3. The probabilistic landslide tsunami disaster assessment method based on a logic tree according to claim 1, wherein The process of constructing a probabilistic landslide scenario set based on the logic tree method includes: Dividing the detection area into i sub-areas of the same size; Obtain the maximum value of the historical landslide volume corresponding to the area to be detected V Lmax and the minimum value V Lmin ; Taking the base-10 logarithm of the historical landslide volume, the dataset of the historical landslide volume lg V L is obtained, where lg V Lmin ≤ lg V L ≤lg V Lmax ; Set the unit discrete interval for the historical landslide volume dataset, and divide it into j volume intervals according to the unit discrete interval; for each landslide volume interval C j , construct i landslide scenarios at different positions, thus constructing i×j landslide scenarios.

4. The probabilistic landslide tsunami disaster assessment method based on a logic tree according to claim 1, wherein The process of deriving landslide scenario geometric parameters according to the probabilistic landslide scenario set includes: For each landslide volume interval, taking the median value of the landslide volume interval as the characteristic volume V of the landslide volume interval; Determining the landslide scenario geometric parameters of the landslide body including length L, width W, and thickness T according to the characteristic volume V = L×W×T; Among them, the landslides moving on the slope include sliding and slumping; for the initial sliding length Lslide, the sliding width is 0.25×Lslide, and the sliding thickness is 0.01×Lslide; for the initial slumping length Lslump, the width is equal to Lslump, and the slumping thickness is 0.1×Lslump.

5. The probabilistic landslide tsunami disaster assessment method based on a logic tree according to claim 1, characterized in that The process of obtaining the distribution law of landslide volume and the annual average incidence rate of landslides according to the corresponding historical landslide data of the area to be detected, so as to calculate the spatial probability of potential landslides occurring annually corresponding to each landslide volume interval includes: Obtaining the distribution law of landslide volume according to the corresponding historical landslide data of the area to be detected; wherein, the distribution law of landslide volume is: when the landslide volume is less than the historical minimum value, the distribution probability of the landslide volume is 0; when the landslide volume is between the historical minimum value and the maximum value, the distribution probability of the landslide volume is the difference between the logarithm of the landslide volume after logarithmic transformation and the logarithm of the historical minimum landslide volume, divided by the difference between the logarithm of the historical maximum landslide volume and the logarithm of the historical minimum landslide volume; when the landslide volume is greater than the historical maximum landslide volume, the distribution probability of the landslide volume is 1; Obtain the average annual landslide occurrence rate when the logarithm of the landslide volume is greater than the logarithm of the minimum value of the historical landslide volume, based on the historical landslide data corresponding to the area to be detected; the average annual landslide occurrence rate is the ratio of the total number of occurrences when the logarithm of the landslide volume corresponding to the area to be detected is greater than the logarithm of the minimum value of the historical landslide volume to the statistical period. Based on the historical landslide data corresponding to the area to be detected, obtain the distribution law of the landslide volume and the average annual landslide occurrence rate, and calculate the spatial probability of potential landslides occurring annually in each landslide volume interval.

6. The probabilistic landslide tsunami disaster assessment method based on a logic tree according to claim 1 or 5, characterized in that The method further includes: Calculate the annual occurrence probability of the nth landslide-tsunami scenario in the probabilistic landslide scenario set; including: The probability that the annual occurrence frequency of potential landslides in the jth landslide volume interval in the area to be detected is 0 is: the natural exponential function of the spatial probability of potential landslides occurring annually corresponding to each landslide volume interval. The probability that the annual occurrence frequency of potential landslides in the jth landslide volume interval in the area to be detected is greater than 1 is: 1 minus the probability that the annual occurrence frequency of potential landslides in the jth landslide volume interval in the area to be detected is 0. Combined with the probability that the annual occurrence frequency of potential landslides within the j-th landslide volume interval in the area to be detected is greater than 1, the spatial probability of potential landslides occurring at the i-th landslide location, the spatial probability of the tsunami triggered by the landslide at the tsunami arrival point k and the spatial probability that the maximum wave amplitude z of the tsunami triggered by the landslide is greater than the wave amplitude threshold Z at the tsunami arrival point k calculate the annual occurrence probability of the n-th landslide-tsunami scenario in the probabilistic landslide scenario set.

7. The probabilistic landslide tsunami disaster assessment method based on a logic tree according to claim 1, wherein The process of calculating the probability that the maximum wave amplitude of the tsunami triggered in the probabilistic landslide scenario set does not exceed the tsunami wave height threshold at the tsunami arrival point includes: For each landslide scenario, determine whether the tsunami wave amplitude it triggers exceeds the wave amplitude threshold; if the tsunami wave amplitude is greater than the wave amplitude threshold, the wave amplitude exceeding probability of this landslide scenario is 1; if the tsunami wave amplitude is less than the wave amplitude threshold, the wave amplitude exceeding probability of this landslide scenario is 0. Based on the wave amplitude exceedance probability corresponding to the landslide scenario, combined with the probability that the annual occurrence frequency of potential landslides in the j-th landslide volume interval in the area to be detected is greater than 1, the spatial probability of potential landslides occurring at the i-th landslide location, the spatial probability of the tsunami triggered by the landslide at the tsunami arrival point k , the spatial probability that the maximum wave amplitude z of the tsunami triggered by the landslide is greater than the wave amplitude threshold Z at the tsunami arrival point k , calculate the probability that the maximum wave amplitude of the tsunami triggered by any landslide scenario does not exceed the wave amplitude threshold at the target location k . Z ​ 8. An electronic device, comprising a memory and a processor, characterized in that, The memory is coupled to the processor; wherein, the memory is used to store program data, and the processor is used to execute the program data to implement the method for probabilistic landslide-tsunami disaster assessment based on a logic tree according to any one of claims 1-7 above.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for probabilistic landslide-tsunami disaster assessment based on a logic tree according to any one of claims 1-7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, it implements the method for probabilistic landslide-tsunami disaster assessment based on a logic tree according to any one of claims 1-7.

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