Logical tree-based probabilistic landslide and tsunami disaster assessment method, equipment and medium

Abstract

The invention discloses a logic tree-based probabilistic landslide tsunami disaster assessment method and device, and a medium. The method comprises the steps of obtaining landslide data corresponding to a to-be-detected region; constructing a probabilistic landslide scene set based on a logic tree method, and deducing landslide scene geometric parameters; the first-stage node, the second-stage node, the third-stage node and the fourth-stage node in the logic tree are the landslide position, the landslide volume, the tsunami arrival point and the tsunami wave height; acquiring historical landslide tsunami records and submarine topography data of the to-be-detected area, and calculating a tsunami wave initial water level field through a first tsunami propagation numerical model according to the landslide position and volume in the probabilistic landslide scene set; according to the tsunami wave initial water level field, a second tsunami propagation numerical model is used for simulation, and the propagation path, the maximum wave amplitude and the influence range of the tsunami are obtained; and estimating the space probability of potential landslide in the to-be-detected area, the annual occurrence probability of landslide in each volume interval, and the probability that the maximum amplitude of the generated tsunami does not exceed the tsunami wave height threshold at the tsunami arrival point.

Description

Technical Field

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

[0002] Tsunamis occur infrequently but are extremely destructive, making them one of the deadliest marine disasters. Common tsunami triggering mechanisms include earthquakes, submarine landslides, and volcanic eruptions. Among them, submarine landslides are the second largest tsunami source after earthquakes. Compared with earthquakes, submarine landslides can usually trigger larger but more localized tsunamis, and the prediction and analysis of submarine landslide tsunamis is still at 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 hazards is similar to that of earthquake tsunamis. It can usually 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 tsunami has mainly focused on the deterministic hazard assessment of typical landslide scenarios, the probabilistic landslide tsunami hazard assessment has also made significant progress. Probabilistic landslide tsunami hazard assessment requires specifying probability distribution functions for key landslide source parameters (such as landslide volume and location), applying statistical simulation methods such as Monte Carlo method and 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 performed based on the simulation results to evaluate the exceedance probability of each selected tsunami intensity index along the coast. Each landslide scale can be characterized by a series of landslide source parameters, such as volume, area and thickness, as well as any known parameters that may affect the landslide tsunami hazard modeling. Among them, landslide volume is the most important factor affecting the size of landslide-induced tsunamis and related wave heights. Therefore, it is very important to sample the landslide body parameters in a stratified manner in order to represent the overall variation of landslide source parameters. Quantifying and accurately describing the scale of landslides as much as possible will help to scientifically understand and recognize landslide tsunami hazards and impacts.

[0004] In order to obtain relatively reliable results and take into account the uncertainties of different factors, and to provide more reliable theoretical support for the estimation of the probability of tsunami disasters, the Monte Carlo method is usually used to establish scenario sets for probabilistic tsunami disaster assessment. Among them, the Monte Carlo method approximates the probability distribution and statistical characteristics of the problem through a large number of random sampling and simulations. In order to obtain sufficiently accurate results, it is usually necessary to establish scenario sets of up to 100,000 or even millions. Because numerical simulations require a lot of computing resources, especially when high-precision results are required, the computing cost will increase significantly, so previous studies have mostly used numerical models that require less computing resources or simpler empirical formulas. Although the computing cost can be reduced, the effect is poor, the calculation accuracy is reduced, and usually only the maximum amplitude at a specific location is output. Summary of the invention

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

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

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

[0008] A probabilistic landslide scenario set is constructed based on a logic tree method, and landslide scenario geometric parameters are derived 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 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 based on the landslide location and volume in the probabilistic landslide scenario set;

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

[0011] Estimate the spatial probability of a potential landslide at each landslide location based on factors including historical landslide tsunami records, seismic activity, and slope gradient in the area to be examined;

[0012] The distribution law of landslide volume and the annual average occurrence rate of landslides are obtained 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 tsunami amplitude caused by the probabilistic landslide scenario 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, comprising a memory and a processor, wherein 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 logic tree-based probabilistic landslide tsunami disaster assessment method.

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

[0016] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned logic tree-based probabilistic landslide and tsunami disaster assessment method.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] (1) The present invention integrates multidisciplinary knowledge such as marine geology, physical geography, geophysics and numerical simulation, integrates data and theories from multiple fields, adopts an improved logic tree method, considers uncertainty factors, constructs landslide scenarios, and uses a method of coupling empirical formulas and numerical models for simulation. It comprehensively analyzes the probability of landslide and tsunami events, the laws of wave propagation and their impact on coastal areas, so that disaster assessment is no longer limited to specific locations.

[0019] (2) The present invention constructs a probabilistic landslide scenario set based on the logic tree method, 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. The present invention uses the landslide location and landslide volume as the main branches and simplifies the branches in the logic tree method. It can not only calculate the exceedance probability of the nearshore amplitude of the location, but also evaluate the probabilistic landslide tsunami disasters in the entire basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0021] Figure 1 A flow chart of a probabilistic landslide and tsunami disaster assessment method provided by an embodiment of the present invention;

[0022] Figure 2 A framework diagram of an improved logic tree method provided by an embodiment of the present invention;

[0023] Figure 3 A result diagram of the probability of submarine landslide occurrence in each volume interval provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of 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.

[0026] It should be noted that, in the absence of conflict, the features in the following embodiments and implementations may be combined with each other.

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

[0028] Step S1, obtaining 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, constructing a probabilistic landslide scenario set based on a 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.

[0031] Furthermore, if Figure 2 As shown in the figure, this example divides the area to be detected into i sub-areas of equal size and reasonable accuracy (i=1, 2, 3...), and numbers the sub-areas in increasing order of longitude and latitude. The length of the unit grid should be equivalent to 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 six landslide scale levels, each of which spans three orders of magnitude, and the order of increasing levels is as follows: 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 of landslide scale (medium, 10 3 –10 6 m 3 ), the fourth level of landslide scale (large, 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, the landslide scenario geometric parameters including length L, width W, thickness T of the landslide body are determined;

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

[0038] Step S3, obtaining historical landslide tsunami records and seabed 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 volume in the probabilistic landslide scenario set; according to the initial water level field of the tsunami wave, using the seabed topography data corresponding to the area to be detected and the second tsunami propagation numerical model to simulate, to obtain the propagation path, maximum amplitude, and impact range of the tsunami.

[0039] Among them, high-resolution seabed topography data can be obtained through 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 the TSUNAMI3D model or NHWAVE model; the second tsunami propagation numerical model includes but is not limited to the NEOWAVE model or FUNWAVE-TVD model.

[0041] Step S4, estimating the spatial probability of potential landslides in the area to be detected based on factors including historical landslide and tsunami records, seismic activities and slope in the area to be detected; obtaining the distribution law of landslide volume based on the landslide data corresponding to the area to be detected, and estimating the annual probability of landslides in each volume interval in the area to be detected; and calculating the probability that the maximum amplitude of the tsunami caused by the probabilistic landslide scenario does not exceed the tsunami wave height threshold at the tsunami arrival point.

[0042] Specifically, the probability values ​​of each event can be obtained by analyzing the simulation results of the landslide tsunami numerical model. Combining the numerical simulation results and historical records, a probabilistic method is introduced to comprehensively consider factors such as the probability of landslide events and the impact range of tsunami wave propagation, and 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 landslide occurrence time, when evaluating landslide tsunami disasters, in addition to the weights of landslide location and landslide scale, the annual probability of landslide occurrence is also required, that is, to quantify the probability that a certain tsunami intensity z (nearshore tsunami amplitude) at the target location k exceeds the threshold Z within a given time window ΔT. Then, the potential landslide tsunami hazard curve and hazard map of the study area and surrounding coastal target points are given.

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

[0044] Each grid is assigned a weight in order to estimate the propensity for landslides to occur in each grid. Factors such as historical landslide events, seismicity, and slope gradient are taken into account to estimate the spatial probability of potential landslides. . In a cell, the weights assigned are as follows: 10 if the average slope φ' of the grid is gentle (between 3° and 5°), 10 if the average depth of the grid is between 1,000 – 1,300 m. 20 if the average slope is steep (> 5°). 1 if none of the above is true, which does not completely exclude the possibility of landslides in deep sea basins and coastal areas. 10 if the factor of safety Fs < 1 (unstable). Then, historical landslide events are taken into account and mapped using marine geological techniques, and the weights are updated by increasing the number of past events in the corresponding cell. The weight of each cell varies 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 seismicity, indicating a low probability of large-scale landslides in the relevant cell. A weight of 40 represents a steep slope, a depth between 1,000 and 1,300 m, and high seismicity, indicating a high probability of a large landslide within the cell in question. By normalizing the sum of the weights, the spatial probability of a potential landslide can be obtained: .

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

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

[0047]

[0048]

[0049] Where N L is the cumulative number of landslides, 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, V Lmin is the minimum value of the historical landslide volume;

[0050] Substituting the relationship between the cumulative number of landslides and the volume distribution into the above formula, we get:

[0051]

[0052] Subtract the cumulative distribution functions corresponding to the upper and lower limits of each volume interval to obtain the probability of occurrence of each interval. According to historical landslide records, the landslide volume lgV can be obtained. L Greater than lgV Lmin The annual average incidence of landslides , the expression is as follows:

[0053]

[0054] In the formula, Represents the landslide volume lgV corresponding to the area to be detected L Greater than lgV Lmin The total number of occurrences, A is the statistical period;

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

[0056]

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

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

[0059]

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

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

[0062]

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

[0064] Assume that each landslide scenario in the scenario set is independent of each other, that is, V j The annual probability of no landslide in the volume interval approximately follows the Poisson distribution, so N j =0 is:

[0065]

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

[0067]

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

[0069]

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

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

[0072] [

[0073]

[0074] The annual joint probability of the maximum tsunami amplitude not exceeding Z at the target location k is:

[0075]

[0076] The annual probability of the maximum tsunami amplitude exceeding Z at the target location k at least once is:

[0077]

[0078] Where N Z N is the total number of tsunami events in which the maximum tsunami amplitude at the target location exceeds Z. Z |n is the annual frequency of the maximum amplitude of the tsunami caused by the nth scenario at the target location k exceeding Z; is the spatial probability of potential landslide occurring at the i-th landslide location, is the spatial probability of potential landslide in the year corresponding to the j-th landslide volume interval, represents the spatial probability of a tsunami caused by a landslide reaching point k, It represents the spatial probability that the maximum amplitude z of the tsunami caused by the landslide is greater than the amplitude threshold Z at the tsunami arrival point k.

[0079] Example 1

[0080] In this example, submarine landslide data of a certain sea area is obtained, and the present invention provides a probabilistic landslide tsunami disaster assessment method based on a logic tree to further illustrate. For the convenience of explanation, this example takes submarine landslide data of a certain sea area as an example. Specifically, the following steps are included:

[0081] The first part includes the establishment of a landslide database and preliminary analysis of landslide data, and then the construction of a suitable landslide scenario set; specifically:

[0082] Establish a landslide database for a certain sea area: A total of 1,500 submarine landslide data were collected and organized from public websites or published literature.

[0083] Constructing landslide scenario set: The length of the unit grid should be equivalent to the volume of the largest landslide derived from the historical landslide events in the study area. Therefore, a sea area is divided into 40 sub-areas of equal size and reasonable accuracy (i=1, 2, 3…), and the sub-areas are numbered in the order of increasing longitude and latitude. Then the landslide volume is evaluated using a logarithmic scale, with V L Logarithm (base 10) of V L The measurement value of lgV Lmin ≤ lgV L ≤lgV Lmax The dataset collection, lgV L The discrete interval interval of is set to 0.1, and 22 volume intervals are obtained (j=1, 2, 3…). For each landslide volume interval C j, 40 landslide scenarios at different locations are planned to be constructed, 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 is [1, 880].

[0084] Derivation of geometric parameters for each landslide scenario: For each landslide volume interval C j , taking the middle value of the interval as the characteristic volume of each interval. Then, using the empirical relationship of landslide geometric parameters, according to the characteristic volume V = L×W×T, the relevant parameters such as the length L, thickness T and width W of the landslide body are derived.

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

[0086] Collect historical landslide and tsunami records and seabed topography data in a certain sea area to prepare for subsequent numerical simulation and disaster assessment. In this example, historical landslide and tsunami records are mainly obtained from relevant public published literature, 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 based on the landslide location and volume in the probabilistic landslide scenario set;

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

[0089] The third part is to assess the landslide tsunami hazard; including:

[0090] Estimate the spatial probability of a potential landslide at each landslide location based on factors including historical landslide tsunami records, seismic activity, and slope gradient in the area to be examined;

[0091] The distribution law of landslide volume and the annual average occurrence rate of landslides are obtained 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; Figure 3 The annual probability of occurrence of each volume interval of potential landslide is shown. .

[0092] Calculate the probability that the maximum tsunami amplitude caused by the probabilistic landslide scenario does not exceed the tsunami wave height threshold at the tsunami arrival point.

[0093] Accordingly, the present application also provides an electronic device, comprising: 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 above-mentioned probabilistic landslide tsunami disaster assessment method based on a logic tree. Figure 4 As shown, a hardware structure diagram of any device with data processing capability for the probabilistic landslide and tsunami disaster assessment method based on a logic tree provided by an embodiment of the present invention is shown, except Figure 4 In addition to the processor, memory and network interface shown, any device with data processing capability in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capability, which will not be described in detail.

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

[0095] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only.

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

Claims

1. A probabilistic landslide tsunami disaster assessment method based on logic tree, characterized in that: The method comprises: Obtaining landslide data corresponding to the area to be detected; A probabilistic landslide scenario set is constructed based on a logic tree method, and landslide scenario geometric parameters are derived 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; 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 based on the landslide location and volume in the probabilistic landslide scenario set; According to the initial water level field of the tsunami wave, the seabed topography data corresponding to the area to be detected and the second tsunami propagation numerical model are used for simulation to obtain the propagation path, maximum amplitude and impact range of the tsunami; Estimate the spatial probability of a potential landslide at each landslide location based on factors including historical landslide tsunami records, seismic activity, and slope gradient in the area to be examined; The distribution law of landslide volume and the annual average occurrence rate of landslides are obtained 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; Calculate the probability that the maximum tsunami amplitude caused by the probabilistic landslide scenario does not exceed the tsunami wave height threshold at the tsunami arrival point.

2. The probabilistic landslide tsunami disaster assessment method based on logic tree according to claim 1 is characterized in that: The landslide data include landslide location, landslide volume, landslide area and landslide thickness.

3. The probabilistic landslide and tsunami disaster assessment method based on logic tree according to claim 1 is characterized in that: The process of constructing a probabilistic landslide scenario set based on the logic tree method includes: Divide the detection area into i sub-areas of equal size; Get the maximum value of the historical landslide volume corresponding to the area to be detected V Lmax and minimum value V Lmin ; Take the logarithm of the historical landslide volume to the base 10 and get the historical landslide volume dataset lg V L , lg V Lmin ≤ lg V L ≤lg V Lmax ; The unit discrete interval interval is set for the historical landslide volume dataset, and j volume intervals are obtained according to the unit discrete interval interval. C j , it is planned to construct i landslide scenarios at different locations, thereby constructing i×j landslide scenarios.

4. The probabilistic landslide and tsunami disaster assessment method based on logic tree according to claim 1 is characterized in that: The process of deriving the geometric parameters of landslide scenarios based on the probabilistic landslide scenario set includes: 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; According to the characteristic volume V=L×W×T, the landslide scenario geometric parameters including length L, width W, and thickness T of the landslide body are determined; Among them, the landslide moving on the slope includes sliding and collapse; for the initial sliding length Lslide, the sliding width is 0.25×Lslide, and the sliding thickness is 0.01×Lslide; for the initial collapse length Lslump, the width is equal to Lslump, and the collapse thickness is 0.1×Lslump.

5. The probabilistic landslide and tsunami disaster assessment method based on logic tree according to claim 1 is characterized in that: The process of obtaining the distribution law of landslide volume and the average annual occurrence rate of landslides according to the historical landslide data corresponding to the area to be detected, and then calculating the spatial probability of potential landslides in each landslide volume interval includes: The distribution law of the landslide volume is obtained according to the historical landslide data corresponding to the area to be detected; wherein, the distribution law of the 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 historical maximum value, the distribution probability of the landslide volume is the difference between the logarithm of the landslide volume after logarithmic transformation and the minimum value of the historical landslide volume, divided by the difference between the maximum value of the historical landslide volume and the minimum value of the historical landslide volume; when the landslide volume is greater than the maximum value of the historical landslide volume, the distribution probability of the landslide volume is 1; According to the historical landslide data corresponding to the area to be detected, the average annual occurrence rate of landslides with a landslide volume greater than the maximum value of the historical landslide volume is obtained; the average annual occurrence rate of landslides is the ratio of the total number of occurrences of the landslide volume corresponding to the area to be detected being greater than the maximum value of the historical landslide volume to the statistical period; The distribution law of landslide volume and the average annual occurrence rate of landslides are obtained according to the historical landslide data corresponding to the area to be detected, and the spatial probability of potential landslides occurring in each landslide volume interval is calculated.

6. The probabilistic landslide and tsunami disaster assessment method based on logic tree according to claim 1 or 5, characterized in that: The method further comprises: Calculate the annual probability of occurrence 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 the annual occurrence of potential landslides corresponding to each landslide volume interval; 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 is: 1 minus the probability that the annual occurrence frequency of potential landslides in the j-th landslide volume interval in the area to be detected is 0; Combined with the probability that the annual frequency of potential landslides in the jth landslide volume interval in the detection area is greater than 1, the spatial probability of potential landslides occurring at the i-th landslide location, and the probability of tsunamis caused by landslides at the tsunami arrival point, k The spatial probability of the maximum amplitude z of the tsunami caused by the landslide is greater than the amplitude threshold Z At the tsunami arrival point k The spatial probability of the nth landslide tsunami scenario in the probabilistic landslide scenario set is calculated.

7. The probabilistic landslide and tsunami disaster assessment method based on logic tree according to claim 1 is characterized in that: The process of calculating the probability that the maximum tsunami amplitude caused by the probabilistic landslide scenario does not exceed the tsunami wave height threshold at the tsunami arrival point includes: For each landslide scenario, determine whether the tsunami amplitude caused by it exceeds the amplitude threshold; if the tsunami amplitude is greater than the amplitude threshold, the amplitude exceeding probability of the landslide scenario is 1; if the tsunami amplitude is less than the amplitude threshold, the amplitude exceeding probability of the landslide scenario is 0; Based on the 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 detection area is greater than 1, the spatial probability of potential landslides occurring at the i-th landslide location, and the tsunami caused by the landslide at the tsunami arrival point, the k The spatial probability of the maximum amplitude z of the tsunami caused by the landslide is greater than the amplitude threshold Z At the tsunami arrival point k The spatial probability of calculating the maximum amplitude of the tsunami caused by any landslide scenario at the target location k No more than the amplitude threshold Z probability.

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 logic tree-based probabilistic landslide tsunami disaster assessment method described in any one of claims 1-7.

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

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the probabilistic landslide and tsunami disaster assessment method based on a logic tree as described in any one of claims 1 to 7 is implemented.

Images