CN117114415B - Landslide hazard rapid distinguishing method for evaluating rainfall on shallow landslide induction probability - Google Patents
Landslide hazard rapid distinguishing method for evaluating rainfall on shallow landslide induction probability Download PDFInfo
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Abstract
The invention discloses a landslide hazard rapid partitioning method for evaluating the induction probability of rainfall on a shallow landslide, which comprises the steps of firstly inputting a geospatial data file and a parameter control text file, converting the data file into a space matrix, simulating the influence of rainfall on the shallow landslide based on a proposed transient rainfall infiltration model PRL-STIM with the depth of a regional scale slope wetting front changing with time and space, and performing a reconstructed first-order reliability method to evaluate the probability of the occurrence of the regional landslide; the PRL-STIM v1.0 software which can take account of the change of coupling pore water pressure under the surface runoff condition, simulates the change of the slope wetting front depth under instantaneous rainfall infiltration along with space-time, and innovatively develops a corresponding model is provided, so that the probability danger division of the rainfall induced shallow landslide in the region range based on a Windows system is realized, the whole calculation and analysis process does not depend on any other geographic information system software, and the defects of time consumption and labor waste in landslide susceptibility assessment under the region scale are overcome.
Description
Technical Field
The invention relates to the field of rainfall slope stability risk assessment, in particular to a landslide hazard rapid division method for assessing the probability of rainfall on shallow landslide induction.
Background
Landslide is one of the most frequent geological disasters in China, and typical inducing factors include rainfall and the like. Under natural conditions, landslide is mostly a shallow landslide with depth of only a few meters, but is considered as one of important factors which seriously threaten human life and property due to strong concealment, the fact that the landslide is mostly close to an ergonomic activity area and serious disaster results. In order to effectively prevent the occurrence of such disasters, landslide prediction and disaster assessment under the action of rainfall are necessary.
And a physical model is used for analyzing the susceptibility of the rainfall landslide on the regional scale, and the influence of rainfall on the slope stability is analyzed by mostly considering the rising of the ground water level and the increase of pore water pressure caused by rainfall infiltration. However, accurate rainfall infiltration simulation is a very difficult process, and if a model is complex, the calculation time is long, the efficiency is low, and the rapid assessment of regional landslide is not facilitated. Therefore, from the standpoint of computational efficiency and accuracy, a physical model that is applicable on a regional scale and that takes into account rainfall infiltration is important for rapid assessment of shallow landslides.
The landslide vulnerability analysis using a physical model on a regional scale requires as much detailed geotechnical parameters of the study site as possible, however accurate knowledge of every strength parameter of the site is impractical. Therefore, the problem of uncertainty of rock and soil parameters is inevitably encountered when landslide sensitivity analysis is carried out based on a physical model. In the stability assessment of rock-soil slopes, probability analysis and reliability methods are often used to qualitatively assess their risk of instability due to the uncertainty and spatial variability of their intensity parameters. One widely used method is the First Order Reliability (FORM) algorithm. The first-order reliability algorithm needs to perform iterative computation, but it is difficult to directly realize the FORM process by solely relying on the grid computation of the GIS, if no external tool is used for assisting, the iterative algorithm is realized by using the common grid computation, and becomes extremely complex; in addition, when considering data analysis of topography, topography and hydrologic parameters, interaction between data must be performed on a plurality of different platforms using geospatial techniques, which makes the calculation process time-consuming and laborious, so constructing one calculation and analysis process without depending on any other geographic information system software is extremely important for rainfall-induced shallow landslide early warning at a regional scale.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a landslide hazard zone method for evaluating the shallow landslide induction probability under rainfall conditions, and provide an instantaneous rainfall infiltration model PRL-STIM with the regional scale slope wetting front depth changing with time and space, so that the influence of rainfall on the shallow landslide is fully simulated. Determining the soil thickness of a corresponding position according to the soil thickness model, and simulating the depth of a wetting front, the surface runoff and the slope stability under the influence of rainfall by using an infinite-length slope stability model considering the water pressure change of the coupling pore under the condition of the surface runoff, so that the problem that the conventional physical model of the shallow landslide induced by regional scale rainfall is difficult to effectively consider the rainfall instantaneous infiltration process is solved, and the calculation efficiency is improved.
And (3) carrying out landslide hazard probability division by adopting a reconstructed first-order reliability (FORM) method, fully considering the randomness of the rock-soil body strength parameters in the model, reducing the uncertainty of the model, and summarizing the calculation result into a grid layer file. The interactive integration of the geospatial data does not depend on any other platform, so that geospatial data interaction on multiple platforms is avoided, the defect that data analysis is time-consuming and labor-consuming under the regional scale is overcome, the method can be quickly and directly used for probability dangerous regions of rainfall-induced shallow landslide in the regional scale, and early warning is provided for rainfall-induced shallow landslide and geological disasters under the regional scale.
In order to achieve the aim of the invention, the specific technical scheme is as follows:
the method comprises the steps of (1) inputting a geospatial data file and a parameter control text file, converting the automatic corresponding data file into a space matrix by software, simulating the influence of rainfall on a shallow landslide based on an instantaneous rainfall infiltration model PRL-STIM with the proposed regional scale slope wetting front depth changing with time and space, and performing a reconstructed first-order reliability method to evaluate the probability of the occurrence of the regional landslide; the PRL-STIM v1.0 software is provided, which can take account of the change of coupling pore water pressure under the surface runoff condition, simulates the change of the slope wetting front depth under the instantaneous rainfall infiltration along with time and space, innovatively develops a corresponding model, realizes the probability danger division of rainfall induced shallow landslide in the region range based on a Windows system, does not depend on any other geographic information system software in the whole calculation and analysis process, and outputs the calculation result in a grid layer file.
The landslide hazard rapid distinguishing method for evaluating the probability of rainfall on shallow landslide induction specifically comprises the following steps:
s1, inputting a geospatial data file and a parameter control text file, wherein the geospatial data file comprises a digital elevation model file (DEM), a maximum soil layer thickness data file, a minimum soil layer thickness data file, a soil type space distribution file (ESRIShapefile), a geotechnical hydrologic parameter data file (ESRIShapefile), a rainfall data file and a rainfall duration data file, and the parameter control text file comprises a correlation coefficient matrix and a correlation coefficient matrix (R). The soil type space distribution file (ESRIShapefile) and the soil hydrological parameter data file (ESRIShapefile) are 'shp' files, wherein the 'shp' files are Ploygen forms and can be matched with the input csv files;
s2, calculating the terrain gradient alpha of each terrain point according to a Digital Elevation Model (DEM);
s3, calculating the soil thickness h of each terrain point according to the maximum soil thickness data file and the minimum soil thickness data file;
s4, sampling and converting the geographic space data files represented by shp attributes into grid layer files according to the unchanged positions, converting the grid layer files into space matrixes, and respectively extracting data in the space matrixes converted by different grid layer files;
s5, calculating the infiltration quantity I of each terrain point based on an instantaneous rainfall infiltration model PRL-STIM with the space change of the regional scale slope wetting front depth according to the data in the space matrix R Depth of wetting front Z w Calculating the pore water pressure u of each topography point by taking into account an infinitely long slope stability model of the coupled pore water pressure change under the surface runoff condition w Safety factor FoS;
s6, carrying out landslide hazard probability analysis based on a reconstructed first-order reliability algorithm-HLRF_x algorithm, summarizing landslide hazard probability analysis calculation results into a grid layer file, and outputting soil thickness h and wetting front depth Z in the form of a layer w Safety factor FoS, reliability index beta and failure probability P f 。
Preferably, the geospatial data file is converted into a raster layer file according to the unchanged position, and the raster layer file is converted into a space matrix;
parameters in the space matrix comprise soil body topography parameters, hydrologic parameters and rainfall parameters; the soil parameters comprise soil cohesive force C s The internal friction angle phi of the soil body, the terrain gradient alpha, the soil body thickness h and the volume weight gamma of saturated soil sat The method comprises the steps of carrying out a first treatment on the surface of the The hydrologic parameter comprises the volume weight gamma of water w Porosity n of soil body and final saturation S f Initial saturation S o Saturation permeability coefficient k s The method comprises the steps of carrying out a first treatment on the surface of the The rainfall parameters comprise rainfall P and rainfall time t.
Preferably, the terrain gradient alpha of each terrain point is calculated based on an eight-node adjacent pixel algorithm according to the digital elevation model file, and the calculation formula of the terrain gradient alpha is formula (1):
ATAN represents an arctangent function; dx, dy, dz denote the differentiation of each topographical point in the x, y, z direction.
Preferably, the soil thickness h of each terrain point is calculated based on a soil thickness model, the soil thickness model is divided into a Z model based on elevation change and an S model based on gradient change, and the calculation formulas are respectively formula (2) and formula (3):
wherein: h thickness of soil body, h max 、h min Respectively the maximum and minimum soil thickness, alpha is the terrain gradient, alpha max And alpha min Maximum and minimum terrain gradients, Z is elevation, Z max And Z min The maximum and minimum elevations, respectively.
Preferably, the instantaneous rainfall infiltration model PRL-STIM with the regional scale slope wetting front depth changing with space comprises a runoff model and a hydrologic model, and the infiltration amount I of each terrain point is calculated based on the runoff model R The calculation formula is formula (4):
wherein: i R For rainfall, P is rainfall, R u Is the runoff of any upward slope, k s Is the saturation permeability coefficient of the soil body.
Preferably, the instantaneous rainfall infiltration model PRL-STIM with the regional scale slope wetting front depth changing with space comprises a runoff model and a hydrologic model, and the wetting peak depth z of each terrain point is calculated based on the hydrologic model w Calculated by equation (5), equation (6), equation (7) and equation (8):
Z wti ＝Z wt(i-1) +ΔZ wti (5)
wherein: z w To wet peak depth, z wti Is t th i Depth of wetting front at time, z wt(i-1) Is t th (i-1) Time of wetting front depth, Δz wti Is t th i Increasing wetting front depth, z wti Is t th i The wetting front depth at time, deltat is the t (i-1) Time and t i The length of time between moments, the saturation permeability coefficient k s H is soil thickness, alpha is terrain gradient, n is soil porosity, S f For final saturation, S o For initial saturation S o ，I R For infiltration amount, θ o For the initial volume moisture content, θ s Is the final volume moisture content.
Preferably, the pore water pressure u of each topography point is calculated based on an infinitely long slope stability model considering the coupled pore water pressure change under the surface runoff condition w The calculation formula is formula (9):
wherein: u (u) w (z) is the pore water pressure at the depth of z below the surface of the earth, z is greater than or equal to 0 and less than or equal to z w Z is the depth below the surface, z w For wet peak depth, gamma w Is the volume weight of water, alpha is the terrain gradient, h c Indicating z=z before rainfall infiltration w An initial pressure head at;
based on an infinitely long slope stability model considering the change of the coupling pore water pressure under the surface runoff condition, judging the stability of each terrain point by using a safety coefficient FoS, wherein the calculation formula is formula (10):
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, z w For the depth of the wetting peak, χ is an effective stress parameter, χ=1, u in the saturated soil slope w Is the pore pressure at the depth of z below the ground surface, and z is more than or equal to 0 and less than or equal to z w 。
Preferably, the reliability index beta is calculated iteratively based on a reconstructed first-order reliability method-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:
P f ＝Φ(-β) (14)
wherein:is a transformation matrix; r is the correlation matrix of all random variables, R -1 Is the inverse of the correlation matrix R; />Representing an equivalent normal standard deviation of a kth variable in a normal space; x is x k Is the k-th iteration in x-space comprisingA vector of random variables; x-space refers to the original data space, i.e. the distribution space obeyed by parameters in the real world, x k+1 Is a vector containing a random variable in the x-space in the k+1-th iteration, and the x satisfying the formula (12) 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; />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; />A most likely point of failure vector component value representing an ith variable evaluated in x-space; u (u) i N And->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; />Represents a limit state function g (x k ) Gradient vector of>Representing gradient vector +.>Is the transposed vector of (1), initial x k Taking the average value x of the vectors of the historical random variables 0 。
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:
1. the invention provides a landslide hazard division method for shallow landslide induction probability under rainfall conditions based on PRL-STIM v1.0 software which is independently developed, which is used as a quick and direct tool and can be used for shallow landslide prediction in a region range under rainfall influence.
2. The regional scale slope wetting front depth is provided with the instantaneous rainfall infiltration model PRL-STIM which changes with time and space, and the influence of rainfall on shallow landslide is fully simulated. The method comprises the steps of determining the soil thickness of a corresponding position according to a soil thickness model, using an infinite slope stability model capable of considering the change of the coupling pore water pressure under the condition of surface runoff to simulate the depth of a wetting front, the surface runoff and the slope stability under the influence of rainfall, solving the problem that the conventional physical model of the shallow landslide induced by regional scale rainfall is difficult to effectively consider the rainfall instantaneous infiltration process, and improving the calculation precision and the calculation efficiency.
3. And (3) carrying out regional landslide probability assessment by adopting a reconstructed first-order reliability method-HLRF_x algorithm, so as to realize landslide risk probability division, fully considering the randomness of the rock-soil mass intensity parameters in the model, reducing the uncertainty of the model, and summarizing the calculation result into a grid layer file.
4. The method realizes the probability danger division of the rainfall-induced shallow landslide in the regional scope based on the Windows system, the whole calculation and analysis process does not depend on any other geographic information system software, the defect that the landslide vulnerability assessment is time-consuming and labor-consuming in the regional scale is overcome, the method can be quickly and directly used for the probability danger division of the rainfall-induced shallow landslide in the regional scope, and early warning is provided for the rainfall-induced shallow landslide in the regional scale and geological disasters.
Drawings
FIG. 1 is a framework diagram of a landslide hazard zone method based on PRL-STIM v1.0 software for shallow landslide induction probability under rainfall conditions;
FIG. 2 is a pore water pressure profile (a, b, and c) of an unsaturated slope as may occur under rainfall infiltration;
FIG. 3 shows rainfall conditions from 7 months 21 to 22 days 7 months in example 2013;
FIG. 4 (a) is a graph showing the calculation result of the safety factor FoS after 4 hours of rainfall in the present example;
FIG. 4 (b) is a graph showing the calculation result of the safety factor FoS 7 hours after rainfall in the present example;
FIG. 4 (c) is a graph showing the calculation result of the safety factor FoS after 11 hours of rainfall in the present example;
FIG. 5 (a) shows the failure probability P after 4 hours of rainfall in the present example f Is a value distribution diagram of the number of the parts;
FIG. 5 (b) shows the failure probability P after 7 hours of rainfall in the present example f Is a value distribution diagram of the number of the parts;
FIG. 5 (c) shows the failure probability P after 11 hours of rainfall in the present example f Is a value distribution diagram of the number of the parts;
fig. 6 (a) shows the failure probability P after 13 hours of rainfall and when the coefficient of variation cov=0.10 f Is a value distribution diagram of the number of the parts;
fig. 6 (b) shows the failure probability P after 13 hours of rainfall and when the coefficient of variation cov=0.20 f Is a value distribution diagram of the number of the parts;
fig. 6 (c) shows the failure probability P after 13 hours of rainfall and when the coefficient of variation cov=0.30 f Is a value distribution map of the (c).
Detailed Description
The method for rapidly distinguishing landslide hazard according to the invention for evaluating the probability of inducing a shallow landslide by rainfall is further described in detail below with reference to the accompanying drawings and the specific examples.
The research area is located in a small basin at northeast of a dam town of a south foot of a west section of Qinling, gansu province in China, and as landslide in the area is induced by continuous heavy rainfall, in order to accurately simulate the landslide caused by the rainfall, two influence factors of rainfall duration and rainfall are considered, and a rainfall set middle period before the landslide occurs is selected as a research period (7 months, 21 days, 12:00 to 7 months, 22 days, 6:00) for rainfall landslide sensitivity assessment so as to prevent disasters from happening again.
The specific flow is as follows:
the file needed by creating the topographic space data of the downtown dam 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 body type space distribution file. The earth surface of the region of interest consisted of a fourth stratum comprising a landslide accumulation layer (Q4) del ) And punching laminate (Q4) pal ). Wherein Q4 pal The stratum is limited to the residual valley steps, has loose structure and lower density, and is easy to induce landslide during rainfall. The physical parameters of the rock and soil mass of each geological unit are determined by retrospective analysis based on geological survey reports of the study area and on the monitored parameters of the installed sensors and ground monitoring stations as shown in table 1.
TABLE 1 physical Properties of the rock and soil mass of the study area
In order to accurately simulate a landslide caused by rainfall, the middle period of a rainfall collection before the occurrence of the landslide is selected as a research period (7 months, 21 days, 12:00, 7 months, 22 days, 6:00) by considering two influencing factors of rainfall duration and rainfall, and the rainfall condition is shown in figure 3.
And combining the soil layer characteristics of the research area, considering that the local soil body contains a permeable layer, and the lower permeable layer is smaller, selecting a pore water pressure section C (section C in figure 3) to analyze pore water pressure data of the slope under the rainfall infiltration effect.
As shown in fig. 1, a rapid landslide hazard zoning method for evaluating the induction probability of rainfall on a shallow landslide is characterized in that a geospatial data file and a parameter control text file are input, software converts an automatic corresponding data file into a space matrix, and performs regional landslide probability evaluation based on an instantaneous rainfall infiltration model PRL-STIM for simulating the influence of rainfall on the shallow landslide when the depth of a proposed regional scale slope wetting front changes with time and space, and a first-order reliability method for reconstruction is performed; the invention provides a method capable of considering the ground surfaceThe infinite length slope stability model for coupling pore water pressure change under runoff condition simulates the change of slope wetting front depth along with time and space under instantaneous rainfall infiltration, and innovatively develops PRL-STIM v1.0 software of a corresponding model, so that probability danger division of rainfall induced shallow landslide in a region range under a Windows system is realized, the whole calculation and analysis process does not depend on any other geographic information system software, and the defects of time consumption and labor waste in landslide susceptibility assessment under a region scale are overcome; summarizing the calculation results into a grid layer file, and outputting soil depth h and wetting front depth Z in the form of a layer w Reliability index beta, failure probability P f And a safety factor FoS.
The method comprises the steps of inputting and creating a geospace data file and a parameter control text file of a wife dam, wherein the geospace data file comprises a digital elevation model file (DEM), a maximum soil layer thickness data file, a minimum soil layer thickness data file, a soil type space distribution file (ESRIShapefile), a geotechnical hydrographic parameter data file (ESRIShapefile), a rainfall data file and a rainfall time data file, and the parameter control text file comprises a correlation coefficient matrix (R). The soil type space distribution file (ESRIShapefile) and the soil hydrological parameter data file (ESRIShapefile) are 'shp' files, and the 'shp' files are Ploygen forms and can be matched with the input csv files.
Calculating the terrain gradient alpha of each terrain point according to a digital elevation model file (DEM) based on an eight-node adjacent pixel algorithm, wherein the calculation formula is formula (1):
the soil thickness is divided into a Z model and an S model to calculate the soil thickness h of each terrain point, wherein the calculation formulas are respectively formula (2) and formula (3), and the Z model is used for calculating the soil thickness h of each terrain point in the embodiment.
The regional scale slope wetting front depth air-time-changing instantaneous rainfall infiltration model PRL-STIM comprises a runoff model and a hydrologic model, and the infiltration amount I is calculated based on the runoff model R The calculation model is as formula (4):
the PRL-STIM of the regional scale slope wetting front depth with the space change comprises a runoff model and a hydrologic model, and the wetting peak depth z of each terrain point is calculated based on the hydrologic model w Calculated by equation (5), equation (6), equation (7) and equation (8):
z wti ＝z wt(i-1) +Δz wti (5)
calculating the pore water pressure u of each topography point based on an infinitely long slope stability model considering the change of the coupled pore water pressure under the condition of surface runoff w The calculation formula is formula (9):
the pore water pressure profile is shown in figure (2).
Based on an infinitely long slope stability model considering the change of the water pressure of the coupling pore under the surface runoff condition, calculating the safety coefficient FoS of each terrain point, wherein the calculation formula is formula (10):
the following raster layers are converted into a spatial matrix: soil mass cohesive force C s The internal friction angle phi of the soil body, the terrain gradient alpha, the soil body thickness h and the volume weight gamma of saturated soil sat Volumetric weight of water gamma w Porosity n of soil body and final saturation S f Initial saturation S o Saturation permeability coefficient k s Infiltration amount I R And rainfall time t.
The program automatically extracts the corresponding position elements of the space matrix, and calculates the terrain gradient according to the selected eight-node adjacent pixel method; calculating the soil thickness h by a Z model; calculating infiltration quantity I by using runoff model in regional scale slope wetting front depth air-changing instantaneous rainfall infiltration model PRL-STIM R The method comprises the steps of carrying out a first treatment on the surface of the Calculating the wetting peak depth z by a hydrological model in an instantaneous rainfall infiltration model PRL-STIM with the wetting front depth of the regional scale side slope changing with space w The method comprises the steps of carrying out a first treatment on the surface of the Selecting a pore water pressure section C model by using an infinitely long slope stability model considering the change of coupled pore water pressure under the condition of surface runoff, and calculating the pore water pressure u w The method comprises the steps of carrying out a first treatment on the surface of the And calculating a safety coefficient FoS by considering an infinitely long slope stability model of the coupling pore water pressure change under the surface runoff condition.
As shown in FIG. 1, the reliability index beta and the failure probability P are calculated using an iterative formula f . The iterative formula for calculating the reliability index beta under the HLRF_x algorithm framework is as follows:
P f ＝Φ(-β) (14)
wherein:is a transformation matrix; r is the correlation matrix of all random variables, R -1 Is the inverse of the correlation matrix R; />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 a vector containing a random variable in the x-space in the k+1-th iteration, and the x satisfying the formula (12) 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; />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; />A most likely point of failure vector component value representing an ith variable evaluated in x-space; u (u) i N And->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; />Represents a limit state function g (x k ) Is of the gradient direction of (2)Quantity (S)>Representing gradient vector +.>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 Wherein DeltaX 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 a gradient vector ∈ (x) k,j )。
UsingCalculating vector x k+1 UsingCalculating 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 A, wherein ε 1 And epsilon 2 Is a predetermined small amount.
In this example, fig. 4 (a) shows a graph of the calculation result of the safety factor FoS of the investigation region after 4 hours of rainfall according to the present method. Fig. 4 (b) shows a graph of the safety factor FoS calculation of the study area after 7 hours of rainfall according to the present method. Fig. 4 (c) shows a graph of the calculation of the safety factor FoS of the investigation region after 11 hours of rainfall according to the present method. FoS < 0.75 is divided into high-risk areas, foS < 1.0 is more than or equal to 0.75 and is divided into dangerous areas, and FoS < 1.0 is more than or equal to 1.0 and is divided into safe areas. Figures 4a-4c show the change in landslide susceptibility over time at 4, 7 and 11 hours after onset of rainfall. It can be seen that as rainfall continues, the high risk area increases significantly.
Figures 5a-5c show the slope failure probability over time at 4, 7 and 11 hours after onset of rainfall. FIG. 5 (a) shows that after 4 hours of rainfall, no failure probability P is found f The area with a value greater than 50% indicates that the entire area is in a steady state at this time. . Fig. 5 (b) shows that after 7 hours of rainfall the proportion of the hazardous area increases rapidly to 24.9%. FIG. 5 (c) shows the failure probability P in each region at 11 hours of rainfall f The latter tended to stabilize and 34.1% of the total area was identified as a hazardous area.
The failure probability P f The following five classes are used:
I：P f ≤1％；Q
II：1％<P f ≤10％；
III：10％<P f ≤50％；
IV：50％<P f ≤90％；
V：P f ≥90％。
consider the failure probability P of different coefficient of variation COV (ratio of standard deviation to average value of specific random variable) after 13 hours of rainfall f Comparison graph. The accuracy of the probability analysis depends on the accuracy with which the data used can be accurately considered, and one of the key factors is the coefficient of variation (COV) of the input probability parameters. The embodiment obtains the failure probability P with the variation coefficient COV ranging from 0.10 to 0.30 f Comparison figures (as shown in figures 6a to 6 c).
P under different coefficients of variation f The comparison shows that the failure probability P is increased with the increase of COV f The distribution exhibited significant differences. The larger the COV is, the more likely it is to fail f The more area of the region of class II to IV: as shown in figure 6 (a) of the drawings,the proportion is about 8.3% when COV is 0.1; FIG. 6 (b) shows that COV increases to 18.6% at 0.2; as shown in FIG. 6 (c), after COV was increased to 0.3, the corresponding area ratio was increased to 27%. This suggests that a high coefficient of variation means that there is a large variation in the random variable within the investigation region, and therefore the probability of failure P f And becomes large.
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 (2)
1. The rapid landslide hazard zoning method for evaluating the probability of rainfall induced on shallow landslide is characterized by comprising the following steps of: firstly, inputting a geospatial data file and a parameter control text file, converting the data file into a space matrix, simulating the influence of rainfall on a shallow landslide based on a proposed instantaneous rainfall infiltration model PRL-STIM with the regional scale slope infiltration peak depth changing with time and space, and performing a reconstructed first-order reliability algorithm to evaluate the probability of the regional landslide;
the method specifically comprises the following steps:
s1, inputting a geospatial data file and a parameter control text file, wherein the geospatial data file comprises a digital elevation model file, a maximum soil layer thickness data file, a minimum soil layer thickness data file, a soil body type space distribution file, a rock soil hydrology parameter data file, a rainfall data file and a rainfall time data file, and the parameter control text file comprises a correlation coefficient matrix;
s2, calculating the terrain gradient alpha of each terrain point according to the digital elevation model file;
s3, calculating the soil thickness h of each terrain point according to the maximum soil thickness data file and the minimum soil thickness data file;
s4, converting the geospatial data file into a grid layer file according to the unchanged position, converting the grid layer file into a space matrix, and respectively extracting data in the space matrix converted by different grid layer files;
s5, calculating the infiltration amount I of each terrain point based on an instantaneous rainfall infiltration model PRL-STIM with the space change of the regional scale slope infiltration peak depth according to the data in the space matrix R Depth of infiltration peak Z w Calculating the pore water pressure u of each topography point by taking into account an infinitely long slope stability model of the coupled pore water pressure change under the surface runoff condition w Safety factor FoS;
s6, carrying out landslide hazard probability analysis based on a reconstructed first-order reliability algorithm-HLRF_x algorithm, summarizing landslide hazard probability analysis calculation results into a grid layer file, and outputting soil thickness h and infiltration peak depth Z in the form of a layer w Safety factor FoS, reliability index beta and failure probability P f ；
Calculating the terrain gradient alpha of each terrain point based on an eight-node adjacent pixel algorithm according to the digital elevation model file, wherein the calculation formula of the terrain gradient alpha is formula (1):
ATAN represents an arctangent function; dx, dy, dz represents the differentiation of each topographical point in the x, y, z direction;
the soil thickness h of each terrain point is calculated based on a soil thickness model, the soil thickness model is divided into a Z model based on elevation change and an S model based on gradient change, and the calculation formulas are respectively formula (2) and formula (3):
wherein: h thickness of soil body, h max 、h min Respectively the maximum and minimum soil thickness, alpha is the terrain gradient, alpha max And alpha min Maximum and minimum terrain gradients, Z is elevation, Z max And Z min The maximum and minimum elevations, respectively;
the regional scale slope infiltration peak depth-time-space-change instantaneous rainfall infiltration model PRL-STIM comprises a runoff model and a hydrologic model, and the infiltration quantity I of each terrain point is calculated based on the runoff model R The calculation formula is formula (4):
wherein: i R For infiltration, P is rainfall, R u Is the runoff of any upward slope, k s The saturation osmotic coefficient of the soil body;
the regional scale slope infiltration peak depth air-time-varying transient rainfall infiltration model PRL-STIM comprises a runoff model and a hydrologic model, and the wetting peak depth z of each terrain point is calculated based on the hydrologic model w Calculated by equation (5), equation (6), equation (7) and equation (8):
Z wti ＝Z wt(i-1) +ΔZ wti (5)
wherein: z w To wet peak depth, z wti Is t th i Time of immersion peak depth，z wt(i-1) Is t th (i-1) Time of immersion peak depth, Δz wti Is t th i Increasing immersion peak depth, z wti Is t th i The depth of the infiltration peak at the moment, delta t is t (i-1) Time and t i The length of time between moments, the saturation permeability coefficient k s H is soil thickness, alpha is terrain gradient, n is soil porosity, S f For final saturation, S o For initial saturation S o ，I R For infiltration amount, θ o For the initial volume moisture content, θ s Is the final volume moisture content;
calculating the pore water pressure u of each topography point based on an infinitely long slope stability model considering the change of the coupled pore water pressure under the condition of surface runoff w The calculation formula is formula (9):
wherein: u (u) w (z) is the pore water pressure at the depth of z below the surface of the earth, z is greater than or equal to 0 and less than or equal to z w Z is the depth below the surface, z w For wet peak depth, gamma w Is the volume weight of water, alpha is the terrain gradient, h c Indicating z=z before rainfall infiltration w An initial pressure head at;
based on an infinitely long slope stability model considering the change of the coupling pore water pressure under the surface runoff condition, judging the stability of each terrain point by using a safety coefficient FoS, wherein the calculation formula is formula (10):
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, z w For the depth of the wetting peak, χ is an effective stress parameter, χ=1, u in the saturated soil slope w Is the pore pressure at the depth of z below the ground surface, and z is more than or equal to 0 and less than or equal to z w ；
Iteratively calculating a reliability index beta based on a reconstructed first-order reliability method-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:
P f ＝Φ(-β) (14)
wherein:is a transformation matrix; r is the correlation matrix of all random variables, R -1 Is the inverse of the correlation matrix R; />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+1-th iteration, and the x satisfying the formula (12) 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; />Is the kth in normal space NStep iteration comprises a mean vector of random variables, and N represents a normal space; epsilon 1 ,ε 2 Is a predetermined small amount; />A most likely point of failure vector component value representing an ith variable evaluated in x-space; u (u) i N And->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; />Represents a limit state function g (x k ) Gradient vector of>Representing gradient vector +.>Is the transposed vector of (1), initial x k Taking the average value x of the vectors of the historical random variables 0 ；
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％。
2. the rapid landslide hazard zoning method for evaluating the probability of rain induced to shallow landslide of claim 1 wherein,
the geospatial data file is converted into a grid layer file according to the unchanged position, and the grid layer file is converted into a space matrix;
parameters in the space matrix comprise soil body topography parameters, hydrologic parameters and rainfall parameters; the soil body topography parameters comprise soil body cohesion C s The internal friction angle phi of the soil body, the terrain gradient alpha, the soil body thickness h and the volume weight gamma of saturated soil sat The method comprises the steps of carrying out a first treatment on the surface of the The hydrologic parameter comprises the volume weight gamma of water w Porosity n of soil body and final saturation S f Initial saturation S o Saturation permeability coefficient k s The method comprises the steps of carrying out a first treatment on the surface of the The rainfall parameters comprise rainfall P and rainfall time t.
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