CN115162376B - Landslide hazard zoning method for carrying out probability analysis on landslide process based on GIS platform - Google Patents

Landslide hazard zoning method for carrying out probability analysis on landslide process based on GIS platform Download PDF

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CN115162376B
CN115162376B CN202210899790.6A CN202210899790A CN115162376B CN 115162376 B CN115162376 B CN 115162376B CN 202210899790 A CN202210899790 A CN 202210899790A CN 115162376 B CN115162376 B CN 115162376B
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姬建
佟斌
崔红志
张童
宋健
曹子君
苏立君
李杭州
王培清
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Abstract

The invention discloses a landslide hazard zone method for carrying out landslide physical process probability analysis based on a Geographic Information System (GIS) platform, which comprises the steps of inputting a topographic space data file, calculating a topographic gradient, selecting a shallow landslide physical model, sampling the topographic space data file, converting the topographic space data file into a raster layer file, converting the raster layer file into a Numpy array, extracting corresponding topographic data in the corresponding array, carrying out high-efficiency analysis on the landslide hazard probability based on a first-order reliability algorithm of an automatic iterative formula, and summarizing calculation results into the raster layer file; according to the invention, a landslide prediction method based on FORM is developed in GIS by adopting Python language, and the HLRF_x algorithm is used for iterative computation of reliability index, so that the conventional process of carrying out data interaction on multiple platforms by using a geospatial technology is avoided, and the defect that the analysis of topographic and geomorphic data in related software of a geographic information system is time-consuming and laborious is further overcome.

Description

Landslide hazard zoning method for carrying out probability analysis on landslide process based on GIS platform
Technical Field
The invention relates to the field of slope stability risk assessment, in particular to a landslide hazard zoning method for carrying out landslide process probability analysis based on a GIS platform.
Background
The Geographic Information System (GIS) can collect, store and analyze geographic information data of the earth surface, and can carry out three-dimensional problem complex analysis by combining spatial data and attribute data. Because of uncertainty and space variability of the own strength parameters of the rock-soil body slope, the rock-soil body slope instability is qualitatively estimated by using a probability analysis and reliability method. Although the first-order second moment method (FOSM) can be applied to a GIS platform, the calculation process has uncertainty, and satisfactory results can not be obtained by each calculation; under the condition of no external tool assistance, the calculation accuracy requirement is high, and when the calculation grids are relatively dense, the iterative algorithm is extremely complex to realize through common grid calculation; furthermore, if data analysis of topography and topographical parameters is considered, multiple data interactions must be performed on different platforms using geospatial techniques, which is time consuming and laborious. A method for estimating landslide hazard based on QPSO-BP neural network (application number 201811219468.4) provides a method for estimating a regional plot by training an estimation model according to a known data set and dividing the landslide hazard according to an influence factor, but the method requires a large amount of reliable historical landslide data to train the estimation model, and can not obtain a reliable result in a region without the historical landslide data.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a landslide hazard zone method for carrying out landslide process probability analysis based on a GIS platform, which uses HLRF_x algorithm to iteratively calculate reliability index beta (Reliability Index, RI) under a first-order reliability method FORM framework, does not need to use geospatial technology to carry out data interaction on multiple platforms, and overcomes the defect that the analysis of topography and topography data in related software of a geographic information system is time-consuming and labor-consuming. The method can rapidly predict the regional landslide and evaluate the earthquake slope disasters, and provides a rapid and efficient discrimination means.
In order to achieve the aim of the invention, the specific technical scheme is as follows:
a landslide danger zone method for carrying out landslide process probability analysis based on a GIS platform is characterized in that a topographic space data file is input, the topographic gradient is calculated, the topographic space data file is sampled and converted into a raster layer file, the raster layer file is converted into a Numpy array, corresponding topographic data in the Numpy array are extracted, the landslide danger probability analysis is carried out through a first-order reliability method (FirstOrderReliabilityMethod, FORM) of automatic iterative calculation based on a shallow landslide physical model, landslide danger probability analysis results are summarized into the raster layer file, and the landslide danger probability analysis results are output in the form of a layer.
A landslide hazard zone method for carrying out landslide process probability analysis based on a GIS platform specifically comprises the following steps:
s1, inputting a topographic space data file, wherein the topographic space data file comprises a digital elevation model file (DEM), a soil type space distribution file (ESRIShapefile), a vegetation type space distribution file (ESRIShapefile), a geological parameter data file, a correlation coefficient matrix (R), a minimum gradient, a DEM standard deviation, an acceleration time course curve and a ground peak acceleration (PGA) distribution map. The soil type spatial distribution file (ESRIShapefile) and the vegetation type spatial distribution file (ESRIShapefile) are 'shp' files, the 'shp' files are Ploygen forms, and the file has a 'Unit' attribute and can be matched with an input csv file.
S2, calculating the terrain gradient alpha of each terrain point according to a Digital Elevation Model (DEM);
s3, data in the soil type space distribution file and the vegetation type space distribution file, which are represented by shp attributes, are sampled and converted into grid layer files according to the unchanged positions, the grid layer files are converted into Numpy arrays by using an ArcGIS built-in Python module, and the data in the Numpy arrays after conversion of different grid layer files are respectively extracted;
s4, according to the data in the Numpy array, calculating the safety coefficient of each grid data point, carrying out landslide hazard probability analysis based on a first-order reliability method (FirstOrderReliabilityMethod, FORM) of an automatic iteration algorithm, summarizing landslide hazard probability analysis calculation results into a grid layer file, and outputting reliability index beta (RI) and failure probability P in the form of a layer f Safety factor FoS, yield acceleration a c Permanent displacement D n 。
Iteratively calculating a reliability index beta based on the HLRF_x algorithm, wherein the reliability index beta represents the minimum distance from the mean value vector to the most probable failure point vector;
the iterative formula for calculating the reliability index beta under the HLRF_x algorithm framework is as follows:
Figure GDA0004191410140000021
Figure GDA0004191410140000022
Figure GDA0004191410140000031
P f ＝Φ(-β) (4)
wherein:
Figure GDA0004191410140000032
is a transformation matrix; r is the correlation matrix of all random variables, R -1 Is the inverse of the correlation matrix R; />
Figure GDA0004191410140000033
Representing an equivalent normal standard deviation of a kth variable in a normal space; x is x k Is the vector of the kth iteration comprising a random variable in x space; x-space refers to the original data space, i.e. real worldDistribution space, x, obeyed by parameters in the world k+1 Is a vector containing a random variable in the x-space in the k+1th iteration, and the x satisfying the formula (2) is satisfied in the last iteration k+1 Defined as X new ，x k+1 ＝X new At this time X new Is the most probable failure point vector; />
Figure GDA0004191410140000034
The k-th iteration in the normal space N comprises a mean value vector of random variables, and N represents the normal space; epsilon 1 ,ε 2 Is a predetermined small amount; />
Figure GDA0004191410140000035
A most likely point of failure vector component value representing an ith variable evaluated in x-space; u (u) i N And->
Figure GDA0004191410140000036
Respectively representing the equivalent normal average value and standard deviation of the ith variable in the normal space; p (P) f Representing failure probability; g (x) is a limit state function; phi (·) represents a standard normal cumulative distribution function, n * Representing the most probable failure point vector in normal space parameters, n *T A transpose vector representing the most likely failure point vector; />
Figure GDA0004191410140000037
Represents a limit state function g (x k ) Gradient vector of>
Figure GDA0004191410140000038
Representing gradient vector +.>
Figure GDA0004191410140000039
Is a transposed vector of (a).
Preferably, an initial x k Taking the average value x of the vectors of the historical random variables 0 。
Preferably, the soil type spatial distribution file (esrishapfile) and the vegetation type spatial distribution file (esrishapfile) are 'shp' files, comprising seven columns:
the first column is "Unit" and indicates the names of soil and vegetation units.
The second column is "param" representing the soil parameter name or vegetation parameter name; the soil parameters comprise soil cohesive force C s The internal friction angle phi of the soil body, the sliding depth D, the terrain gradient alpha and the soil body dry-weight gamma m Volume weight gamma of saturated soil sat Volumetric weight of water gamma w And ground water height H w The method comprises the steps of carrying out a first treatment on the surface of the Vegetation parameters include root system cohesive force C r And vegetation weight q t 。
The third column is "dist" representing the probability distribution name of each variable.
The fourth to seventh columns are "stat1", "stat2", "stat3" and "stat4", respectively, representing the corresponding probability distribution parameter values, and when the variable meets the two-parameter statistical distribution (normal distribution or log-normal distribution), the statistical values are assigned to the "stat1" and "stat2" columns, and the zero values are filled in the "stat3" and "stat4" columns.
Preferably, the data in the soil type space distribution file and the vegetation type space distribution file which are characterized by shp attributes are sampled and converted into grid layer files according to the unchanged positions, and the grid layer files are converted into Numpy arrays by using an ArcGIS built-in Python module.
The parameters in the Numpy array comprise soil parameters and vegetation parameters, and the soil parameters comprise soil cohesion C s The internal friction angle phi of the soil body, the sliding depth D, the terrain gradient alpha and the soil body dry-weight gamma m Volume weight gamma of saturated soil sat Volumetric weight of water gamma w And ground water height H w The method comprises the steps of carrying out a first treatment on the surface of the Vegetation parameters include root system cohesive force C r And vegetation weight q t 。
The calculation step of the terrain gradient alpha comprises the following steps: calculating the terrain gradient alpha of each terrain point according to a Digital Elevation Model (DEM) based on an eight-node adjacent pixel algorithm, converting the sampling into a grid layer file, and converting the grid layer file into a Numpy array;
the terrain gradient alpha depends on the rate of change of the surface in the horizontal (dz/dx) direction and the vertical (dz/dy) direction from the central picture element, and the terrain gradient alpha is calculated by the following formula:
Figure GDA0004191410140000041
ATAN represents an arctangent function; dx, dy, dz denote the differentiation of each topographical point in the x, y, z direction.
Calculating the safety coefficient FoS of each terrain point based on a shallow landslide physical model, wherein the shallow landslide physical model comprises a Hammond model and an infinitely long slope model, and obtaining the yield acceleration a of each terrain point according to a formula (8) and a formula (9) by seismic analysis c Permanent displacement D n 。
a c ＝(FoS-1)g·sinα (8)
Figure GDA0004191410140000042
Wherein: a, a c FoS is the safety coefficient of each terrain point, g is the gravity acceleration, alpha is the terrain gradient and D n For permanent displacement, a max Is the ground peak acceleration.
Preferably, the safety coefficient FoS of each terrain point is calculated based on a Hammond model, and the calculation formula is formula (6):
Figure GDA0004191410140000051
wherein: c (C) r C is the cohesive force of root system s Is the cohesive force of soil mass, q t Is vegetation weight, phi is soil internal friction angle, alpha is terrain gradient, gamma m Is soil body dry and severe degree, gamma sat Is the volume weight of saturated soil, gamma w Is the volume weight of water, D is the sliding depth, H w Is the ground water level.
Preferably, calculating a safety coefficient FoS of each terrain point based on an infinitely long slope model, wherein a calculation formula is formula (7);
Figure GDA0004191410140000052
wherein: c (C) s Is soil cohesive force, phi is soil internal friction angle, alpha is terrain gradient, gamma sat Is the volume weight of saturated soil, gamma w Is the volume weight of water, D is the sliding depth, H w Is the ground water level.
Preferably, the failure probability P f The following five classes are used:
I：P f ≤1％；
II：1％<P f ≤10％；
III：10％<P f ≤50％；
IV：50％<P f ≤90％；
V：P f ≥90％。
the invention has the beneficial effects that:
the invention provides a landslide hazard zone method for carrying out landslide process probability analysis based on a GIS platform, which uses an HLRF_x algorithm to iteratively calculate reliability indexes beta (Reliability Index, RI) under a first-order reliability method FORM framework, has quick calculation and wider applicability, does not need to use a geospatial technology to carry out data interaction on multiple platforms, and overcomes the defect that the analysis of topography and topography data in related software of a geographic information system is time-consuming and labor-consuming. The regional landslide prediction and the earthquake slope disaster evaluation can be rapidly carried out, and a rapid and efficient discrimination means is provided;
based on the first-order reliability method FORM, the HLRF_x algorithm is applied to regional landslide evaluation for the first time, and a more effective probability analysis framework is realized.
The HLRF_x algorithm iterative formula is adopted to calculate the reliability index beta (RI), and data interaction is not needed to be carried out on multiple platforms by using a geospatial technology, so that the calculation efficiency is improved, the application range is enlarged, and the defect that the time and labor are consumed for carrying out the data analysis of the topography and the geomorphology in the related software of the geographic information system is overcome.
After the initial topographic parameters are input and the calculation method is selected, results such as reliability and the like can be automatically calculated, and automatic drawing of regional disasters is realized, so that landslide disaster assessment is carried out.
Description of the drawings:
FIG. 1 is a landslide hazard zone frame diagram for carrying out landslide process probability analysis based on a GIS platform;
FIG. 2 is a flowchart of an iterative calculation of a reliability indicator β (RI) by the HLRF_x algorithm;
FIG. 3a is a diagram showing the calculation result of the safety factor FoS in the present embodiment;
FIG. 3b yield acceleration a of the present embodiment at different ground peak accelerations PGA c Calculating a result graph;
FIG. 3c shows the permanent displacement D of the present embodiment n Calculating a result graph;
fig. 4a is a distribution diagram of the reliability index β (RI) when the variation coefficient cov=0.05;
fig. 4b is a distribution diagram of the reliability index β (RI) when the variation coefficient cov=0.1;
fig. 4c is a distribution diagram of the reliability index β (RI) when the variation coefficient cov=0.2;
fig. 4d is a distribution diagram of the reliability index β (RI) when the variation coefficient cov=0.3;
fig. 4e shows the failure probability P when the coefficient of variation cov=0.05 in the present embodiment f Is a value distribution diagram of the number of the parts;
fig. 4f shows the failure probability P when the coefficient of variation cov=0.1 in the present embodiment f Is a value distribution diagram of the number of the parts;
fig. 4g shows the failure probability P when the coefficient of variation cov=0.2 in the present embodiment f Is a value distribution diagram of the number of the parts;
fig. 4h shows the failure probability P when the coefficient of variation cov=0.3 in the present embodiment f Is a value distribution map of the (c).
Detailed Description
The invention relates to a method for evaluating stability of a side slope of an earthquake, which is further described in detail below with reference to the accompanying drawings and specific embodiments.
The research area is located in Jiuzhai county in North of Sichuan province, and suffers from 7.0 grade earthquake in 8 months and 8 days of 2017, so that a large amount of landslide is initiated, and the existing mountain slope is composed of earthquake post-accumulation bodies and mainly is shallow rock landslide on falling rocks. It is therefore necessary to evaluate the sensitivity of the earthquake landslide in the area to prevent the occurrence of disasters again.
The specific flow is as follows:
the file needed by creating the nine village ditch topographic space data is simplified, and the method mainly comprises the following parts:
and simplifying and supposing the strength parameters of the rock and soil body in the soil type space distribution file. The region of interest is divided mainly into four lithology units, which are: and (3) the two-fold period: limestone, dolomite and dolomitic limestone; carbolic acid: limestone and limestone-to-dolomite; three-fold: green gray metamorphic curdling sandstone and siltstone; mud pot period: organic limestone and layered dolomite. Since it is extremely difficult to obtain actual formation parameters over such a wide range, simplified assumptions are made about rock strength parameters in a soil type spatial distribution file from three aspects: geological survey report of the research area; empirical values of local engineering; and performing backtracking analysis according to the monitoring parameters of the installed sensor and the ground monitoring station. The strength parameters for each geological unit are determined as shown in table 1.
TABLE 1 surface soil physical Properties of different bedrock types in the study area
Figure GDA0004191410140000071
Simplifying assumption is made on the soil saturation. Since the nine village ditch earthquake occurs outside the rainy season and the change in the saturation of the soil is mainly due to rainfall and seasonal variation, it is neglected here, namely let H in formula (6) w ＝0。
Simplifying assumptions are made about the normal sliding depth. As proved by field investigation, the common-seismic landslide of nine village ditches is a shallow landslide, so that the normal sliding depth is assumed to be 2m.
The key point of the embodiment is to verify the feasibility of predicting landslide by using a GIS-FORM landslide prediction method, rather than considering more factors to reveal the mechanism of landslide induced by vibration. Thus, the ground peak acceleration profile is selected to represent the seismic impact.
As shown in FIG. 1, a landslide hazard zone analysis method based on a Geographic Information System (GIS) platform for performing landslide physical process probability analysis is characterized in that a topographic space data file is input, a ArcGIS built-in eight-node adjacent pixel method is selected to calculate the topographic gradient, the topographic space data file is sampled and converted into a grid layer file, a ArcGIS built-in Python module is utilized to convert the grid layer file into a Numpy array, corresponding topographic data in the Numpy array is extracted, a landslide hazard probability analysis result is summarized into a grid layer file based on a shallow landslide physical model through a first-order reliability (FirstOrderReliabilityMethod, FORM) algorithm of an automatic iterative algorithm, and a reliability index beta (RI) is output in a form of a layer; probability of failure P f Safety factor FoS, yield acceleration a c Permanent displacement D n 。
Files required for creating the nine village ditch topographic space data are input, including topographic space data files including a digital elevation model file (DEM), a soil type spatial distribution file (esrishapfile), a vegetation type spatial distribution file (eshapfile), a geological parameter data file, a correlation coefficient matrix (R), a minimum slope, a DEM standard deviation, an acceleration time course curve, and a ground peak acceleration (PGA) profile. The soil type spatial distribution file (ESRIShapefile) and the vegetation type spatial distribution file (ESRIShapefile) are 'shp' files, the 'shp' files are Ploygen forms, and the file has a 'Unit' attribute and can be matched with an input csv file. Comma Separated Values (CSV, also known as character separation Values, since separation characters may or may not be commas), the CSV file stores table data in plain text form.
The terrain gradient calculating method selects an Arcmap algorithm, namely an eight-node adjacent pixel method to calculate the terrain gradient, and belongs to a built-in algorithm of software ArcGIS;
calling a software ArcGIS built-in eight-node adjacent pixel algorithm to calculate the terrain gradient alpha of each terrain point according to the DEM file;
and selecting a shallow landslide physical model. The shallow landslide physical model is divided into a Hammond model and an infinitely long slope model. The shallow landslide physical model is divided into a Hammond model and an infinitely long side slope model, and the safety coefficient (FoS) of each terrain point based on the Hammond model or the safety coefficient (FoS) of each terrain point of the infinitely long side slope model is obtained through calculation, wherein the calculation formulas are respectively formula (6) and formula (7); the earthquake analysis obtains the yield acceleration a of each terrain point according to the formula (8) and the formula (9) c Permanent displacement D n 。
Figure GDA0004191410140000091
Figure GDA0004191410140000092
a c ＝(FoS-1)g·sinα (8)
Figure GDA0004191410140000093
The yield acceleration in this example selects an infinitely long slope model, and the calculation model is as follows:
Figure GDA0004191410140000094
the present embodiment calculates the permanent displacement D using the regression displacement formula n The calculation formula is as follows:
a c ＝(FoS-1)gsinα (8)
Figure GDA0004191410140000095
the yield acceleration program automatically samples and converts the raster data. The following raster layers are converted into Numpy arrays: soil mass cohesive force C s Internal friction angle of soil bodyPhi, sliding depth D, terrain gradient alpha and soil body dry-weight gamma m Volume weight gamma of saturated soil sat Volumetric weight of water gamma w Height H of groundwater w Root system adhesion force C r Weight q of vegetation t 。
The program automatically extracts the corresponding position elements of the Numpy array, and calculates the terrain gradient according to the selected eight-node adjacent pixel method; calculating the safety coefficient of each terrain point by using the infinite side slope model; the yield acceleration a of each terrain point is calculated by using a Jibson regression displacement formula c Permanent displacement D n 。
FIG. 2 shows the calculation of reliability index β and failure probability P using an iterative formula f . The iterative formula for calculating the reliability index beta under the HLRF_x algorithm framework is as follows:
Figure GDA0004191410140000096
Figure GDA0004191410140000097
Figure GDA0004191410140000101
P f ＝Φ(-β) (4)
wherein:
Figure GDA0004191410140000102
is a transformation matrix; r is the correlation matrix of all random variables, R -1 Is the inverse of the correlation matrix R; />
Figure GDA0004191410140000103
Representing an equivalent normal standard deviation of a kth variable in a normal space; x is x k Is the vector of the kth iteration comprising a random variable in x space; x-space refers to the original data space, i.e. the distribution space obeyed by parameters in the real world, x k+1 Is that the k+1 step iteration in x space contains a random variableAnd x satisfying the formula (2) at the last iteration k+1 Defined as X new ，x k+1 ＝X new At this time X new Is the most probable failure point vector; />
Figure GDA0004191410140000104
The k-th iteration in the normal space N comprises a mean value vector of random variables, and N represents the normal space; epsilon 1 ,ε 2 Is a predetermined small amount; />
Figure GDA0004191410140000105
A most likely point of failure vector component value representing an ith variable evaluated in x-space; u (u) i N And->
Figure GDA0004191410140000106
Respectively representing the equivalent normal average value and standard deviation of the ith variable in the normal space, and obtaining the equivalent normal average value and standard deviation through the Rackwitz-Fiessler transformation; p (P) f Representing failure probability; g (x) is a limit state function; phi (·) represents a standard normal cumulative distribution function, n * Representing the most probable failure point vector in normal space parameters, n *T A transpose vector representing the most likely failure point vector; />
Figure GDA0004191410140000107
Represents a limit state function g (x k ) Gradient vector of>
Figure GDA0004191410140000108
Representing gradient vector +.>
Figure GDA0004191410140000109
Is a transposed vector of (a).
In this embodiment, an initial iteration point X is first selected in the X space k Initial x k Can take the average value point x 0 Define the function g (x k )。
Will x k,1 (i.e. x k The first component of (a) becomes x k,1 +ΔX 1 WhereinΔX 1 Is a prescribed minor variation. Other terms in the equation remain unchanged, and the function g (x k,1 ) Is a new value of (c).
Calculating x 1 The change in the function value caused by a small change in the value, i.e. Δg (x k,1 )＝g(x k,1 )-g(x k ). At this time g (x) k ) For x 1 The derivative is approximately equal to the difference quotient deltag (x k,1 )/Δx 1 。
For vector x k Each component (i.e. x k,j ) Repeating the steps (2) to (3) to obtain gradient vectors
Figure GDA00041914101400001010
/>
Using
Figure GDA0004191410140000111
Calculating vector x k+1 Using
Figure GDA0004191410140000112
Calculating a reliability index beta, wherein x i * Seen as a new vector x k+1 。
Using a new vector x k+1 Repeating steps (1) to (5) until the vector x and beta values converge and satisfy the following two equations: x new -X||≤ε 1 ，||g(X new )||≤ε 2 Wherein ε is 1 And epsilon 2 Is a predetermined small amount.
Obtaining a series of visual geological disaster graphs, and outputting a reliability index beta (RI) in the form of a layer; probability of failure P f The method comprises the steps of carrying out a first treatment on the surface of the A safety factor FoS; yield acceleration a c The method comprises the steps of carrying out a first treatment on the surface of the Permanent displacement D n A comparative analysis may be performed.
In this example, fig. 3 (a) shows a graph of the results of the calculation of the safety factor FoS for the investigation region according to the present method. FIG. 3 (b) shows PGA-a according to the present method in the investigation region c And calculating a result graph. FIG. 3 (c) shows the permanent displacement D of the investigation region according to the present method n And calculating a result graph. Wherein FoS is greater than or equal to 1.0 and is divided into safe areas, and FoS is less than 1.0 and is divided into unsafe areas.
Reliability index beta (RI) and failure probability P taking into account the different coefficient of variation COV (ratio of standard deviation to mean of specific random variables) f Comparison graph. The Gao Bianyi coefficient means that the random variable in the study area will vary greatly, and therefore the failure probability P f The reliability index β (RI) is very large. The variation of the distribution of the reliability index beta (RI) is closely related to the coefficient of variation COV of the corresponding parameter, and in the case of very low variability (cov=0.05), only 4.3% of landslides have a beta (RI) value less than zero. The embodiment obtains the failure probability P with the variation coefficient COV ranging from 0.05 to 0.30 f As can be seen from fig. 4a-4d, the variation of the distribution of the reliability index β is closely related to the coefficient of variation COV of the corresponding parameter, compared to the reliability index β (RI) graph (as shown in fig. 4a-4 h), and in case of very low variability (cov=0.05), only 4.3% of the landslide has a reliability index β (RI) value less than zero. P with coefficient of variation COV ranging from 0.05 to 0.30 f The distribution diagrams are shown in FIGS. 4e-4 h.
The failure probability P f The following five classes are used:
I：P f ≤1％；
II：1％<P f ≤10％；
III：10％<P f ≤50％；
IV：50％<P f ≤90％；
V：P f ≥90％。
p under different coefficients of variation f The comparison shows that the landslide area of high risk mountain increases significantly with the coefficient of variation COV, a phenomenon which in fact also reflects the nature of the uncertainty and probability analysis: the expected failure probability will increase when a region is subjected to greater variability, i.e., higher COV values.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.

Claims (8)

1. A landslide danger zone method for carrying out landslide process probability analysis based on a GIS platform is characterized in that a topographic space data file is input, the topographic gradient is calculated, the topographic space data file is sampled and converted into a raster layer file, the raster layer file is converted into a Numpy array, corresponding topographic data in the Numpy array is extracted, the landslide danger probability analysis is carried out through a first-order reliability algorithm of an automatic iterative algorithm based on a shallow landslide physical model, landslide danger probability analysis results are summarized into the raster layer file, and the danger probability analysis results are output in a layer mode;
a landslide hazard zone method for carrying out landslide process probability analysis based on a GIS platform specifically comprises the following steps:
s1, inputting a topographic space data file, wherein the topographic space data file comprises a digital elevation model file, a soil type space distribution file, a vegetation type space distribution file, a geological parameter data file, a correlation coefficient matrix, a minimum gradient, a DEM standard deviation, an acceleration time course curve and a ground peak acceleration distribution map;
s2, calculating the terrain gradient alpha of each terrain point according to the digital elevation model file;
s3, sampling and converting data in the soil type space distribution file and the vegetation type space distribution file into a grid layer file according to the unchanged positions, converting the grid layer file into a Numpy array, and respectively extracting the data in the Numpy arrays converted by different grid layer files;
s4, according to the data in the Numpy array, calculating the safety coefficient of each grid data point, carrying out landslide hazard probability analysis based on a first-order reliability algorithm of an automatic iteration algorithm, summarizing calculation results of the landslide hazard probability analysis into a grid layer file, and outputting a reliability index beta and failure probability P in the form of a layer f Safety factor FoS, yield acceleration a c Permanent displacement D n ；
Iteratively calculating a reliability index beta based on the HLRF_x algorithm, wherein the reliability index beta represents the minimum distance from the mean value vector to the most probable failure point vector;
the iterative formula for calculating the reliability index beta under the HLRF_x algorithm framework is as follows:
Figure FDA0004191410130000011
Figure FDA0004191410130000012
Figure FDA0004191410130000013
P f ＝Φ(-β) (4)
wherein:
Figure FDA0004191410130000021
is a transformation matrix; r is the correlation matrix of all random variables, R -1 Is the inverse of the correlation matrix R; />
Figure FDA0004191410130000022
Representing an equivalent normal standard deviation of a kth variable in a normal space; x is x k Is the vector of the kth iteration comprising a random variable in x space; x-space refers to the original data space, i.e. the distribution space to which parameters obey in the real world; x is x k+1 Is a vector containing a random variable in the x-space in the k+1th iteration, and the x satisfying the formula (2) is satisfied in the last iteration k+1 Defined as X new ，x k+1 ＝X new At this time X new Is the most probable failure point vector; />
Figure FDA0004191410130000023
The k-th iteration in the normal space N comprises a mean value vector of random variables, and N represents the normal space; epsilon 1 ,ε 2 Is a predetermined small amount; />
Figure FDA0004191410130000024
A most likely point of failure vector component value representing an ith variable evaluated in x-space; u (u) i N And->
Figure FDA0004191410130000025
Respectively representing the equivalent normal average value and standard deviation of the ith variable in the normal space; p (P) f Representing failure probability; g (x) is a limit state function; phi (·) represents a standard normal cumulative distribution function, n * Representing the most probable failure point vector in normal space parameters, n *T A transpose vector representing the most likely failure point vector; />
Figure FDA0004191410130000026
Represents a limit state function g (x k ) Gradient vector of>
Figure FDA0004191410130000027
Representing gradient vector +.>
Figure FDA0004191410130000028
Is a transposed vector of (a).
2. The landslide hazard zone method based on GIS platform for carrying out landslide process probability analysis according to claim 1, wherein,
initial x k Taking the average value x of the vectors of the historical random variables 0 。
3. The landslide hazard zone method based on GIS platform for carrying out landslide process probability analysis according to claim 1, wherein,
the soil type spatial distribution file and vegetation type spatial distribution file are '. Shp' files, which comprise seven columns:
the first column is "Unit" representing the names of soil and vegetation units;
the second column is "param" representing the soil parameter name or vegetation parameter name;
the third column is "dist", representing the probability distribution name of each variable;
the fourth column to the seventh column are respectively "stat1", "stat2", "stat3" and "stat4", which respectively represent corresponding probability distribution parameter values, and when the variable accords with the double-parameter statistical distribution, the statistical values are distributed into a "stat1" column and a "stat2" column, and zero values are filled in the "stat3" column and the "stat4" column.
4. The landslide hazard zone method based on GIS platform for carrying out landslide process probability analysis according to claim 1, wherein,
the data in the soil type space distribution file and the vegetation type space distribution file are sampled and converted into a grid layer file according to the unchanged positions, and the grid layer file is converted into a Numpy array;
the parameters in the Numpy array comprise soil parameters and vegetation parameters, and the soil parameters comprise soil cohesion C s The internal friction angle phi of the soil body, the sliding depth D, the terrain gradient alpha and the soil body dry-weight gamma m Volume weight gamma of saturated soil sat Volumetric weight of water gamma w And ground water height H w The method comprises the steps of carrying out a first treatment on the surface of the Vegetation parameters include root system cohesive force C r And vegetation weight q t 。
5. The landslide hazard zone method based on GIS platform for carrying out landslide process probability analysis according to claim 1, wherein,
calculating the terrain gradient alpha of each terrain point based on an eight-node adjacent pixel algorithm according to the digital elevation model file; the calculation formula of the terrain gradient alpha is as follows:
Figure FDA0004191410130000031
ATAN represents an arctangent function; dx, dy, dz represents the differentiation of each topographical point in the x, y, z direction;
calculating the safety coefficient FoS of each terrain point based on a shallow landslide physical model, wherein the shallow landslide physical model comprises a Hammond model and an infinitely long slope model, and obtaining the yield acceleration a of each terrain point according to a formula (8) and a formula (9) by seismic analysis c Permanent displacement D n ：
a c ＝(FoS-1)g·sinα (8)
Figure FDA0004191410130000032
Wherein: a, a c FoS is the safety coefficient of each terrain point, g is the gravity acceleration, alpha is the terrain gradient and D n For permanent displacement, a max Is the ground peak acceleration.
6. The landslide hazard zone method based on GIS platform for performing landslide process probability analysis according to claim 5, wherein,
calculating the safety coefficient FoS of each terrain point based on a Hammond model, wherein the calculation formula is formula (6):
Figure FDA0004191410130000033
wherein: c (C) r C is the cohesive force of root system s Is the cohesive force of soil mass, q t Is vegetation weight, phi is soil internal friction angle, alpha is terrain gradient, gamma m Is soil body dry and severe degree, gamma sat Is the volume weight of saturated soil, gamma w Is the volume weight of water, D is the sliding depth, H w Is the ground water level.
7. The landslide hazard zone method based on GIS platform for performing landslide process probability analysis according to claim 5, wherein,
calculating a safety coefficient FoS of each terrain point based on the infinite side slope model, wherein a calculation formula is a formula (7);
Figure FDA0004191410130000041
wherein: c (C) s Is soil cohesive force, phi is soil internal friction angle, alpha is terrain gradient, gamma sat Is the volume weight of saturated soil, gamma w Is the volume weight of water, D is the sliding depth, H w Is the ground water level.
8. The landslide hazard zone method based on GIS platform for carrying out landslide process probability analysis according to claim 1, wherein,
probability of failure P f The following five classes are used:
I：P f ≤1％；
II：1％<P f ≤10％；
III：10％<P f ≤50％；
IV：50％<P f ≤90％；
V：P f ≥90％。
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