CN114118848A - Quantitative recognition and disaster risk assessment method for urban rock litholytic collapse factor - Google Patents
Quantitative recognition and disaster risk assessment method for urban rock litholytic collapse factor Download PDFInfo
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Abstract
The invention provides a method for quantitatively identifying urban rock litholytic collapse factors and evaluating disaster risks. The method comprises the following steps: extracting the ground surface deformation rate and the ground surface displacement time sequence of a research area according to a long-time sequence TerrraSAR-X high-resolution SAR image of the research area; establishing a cross wavelet transformation model between the ground surface displacement time sequence and the underground water level time sequence and between the ground surface displacement time sequence and the Yangtze river water level time sequence, and quantitatively extracting a time-lag effect from a special point location scale; establishing a space geographic detection model between earth surface deformation rate and the certainty coefficients of all grades of all potential influence factors, and determining the influence degree of all potential influence factors on karst earth surface collapse from a space scale according to the calculated space differentiation degree; selecting important influence factors by combining point-by-point multi-time sequence analysis results and space differentiation factor detection results; and establishing a binary logistic regression model between the karst surface subsidence event and the surface deformation rate and the certainty coefficient of the important influence factor, and realizing risk zoning on the karst surface subsidence of the research area.
Description
Technical Field
The invention relates to the technical field of geological disaster monitoring, mechanism analysis and disaster assessment, in particular to a method for quantitatively identifying urban rock lyotropic collapse factors and assessing disaster risks.
Background
Karst surface subsidence refers to a geological disaster that loose soil mass covering the erosion cave suddenly subsides under the action of natural factors or human factors to form a conical collapse pit. The karst surface collapse event not only directly causes economic loss, but also seriously harms the safety of residential areas, traffic, engineering construction and water supply.
The current karst surface monitoring technology method mainly carries out karst collapse investigation and monitoring around the development degree of karst (the development and distribution of karst caves, cracks and dissolving tanks), overlying soil structure and thickness, hydrodynamics (groundwater level, water/air pressure, flow speed/direction, slope drop, PH value and the like) and surface displacement. The monitoring technology mainly comprises a contact type in-situ monitoring sensor, a geological radar, an optical fiber technology, a time domain reflection technology, a comprehensive geophysical prospecting method, a GPS observation method and the like. The methods are directly oriented to object monitoring, the monitoring precision is high, but due to frequent landform changes caused by intensive urban construction and natural erosion processes, the in-situ contact (even destructive) field monitoring or investigation method of the objects has the problems of low working efficiency and insufficient density on wide-area karst surface monitoring. In view of the clear structure and hierarchy of karst surface collapse influence factor layers, the risk assessment of karst surface collapse disasters in China is mainly carried out based on an Analytic Hierarchy Process (AHP) method at present. However, the qualitative method is too dependent on the scoring of each factor layer by experts, the influence of surface deformation factors accompanied by collapse pits is not considered, and the method is more blind to areas lacking prior information.
Disclosure of Invention
Aiming at the problems that the potential influence factors of wide area karst surface subsidence are difficult to quantitatively identify and disaster risk grade is difficult to partition in the traditional method, the invention provides a method for quantitatively identifying the urban karst surface subsidence factors and evaluating disaster risk, which fully utilizes the surface deformation results with high space-time resolution and long time sequence to realize the quantitative identification of the influence degree of different water systems on the karst surface subsidence and the quantitative identification of the influence degree of all the potential influence factors on the karst surface subsidence.
The invention provides a method for quantitative recognition of urban rock litholytic collapse factors and evaluation of disaster risks, which comprises the following steps:
step 1: extracting the surface deformation rate and the surface displacement time sequence of the research area by using a StaMPS-SBAS method according to the long-time-sequence TerrasAR-X high-resolution SAR image of the research area;
step 2: establishing a cross wavelet transformation model among the earth surface deformation rate, the point-by-point earth surface displacement time sequence, the underground water level time sequence and the Yangtze river water level time sequence, and quantitatively extracting a time-lag effect from a special point position scale so as to identify the influence degree of different water systems on the karst earth surface collapse;
and step 3: grading potential influence factors of karst surface subsidence of a research area, and determining the graded certainty factor of all the potential influence factors based on the karst surface subsidence accidents recorded historically;
and 4, step 4: establishing a space geographic detection model between the earth surface deformation rate and the graded certainty coefficients of all the potential influence factors, and determining the influence degree of all the potential influence factors on karst earth surface collapse from a space scale according to the space diversity degree calculated by the space geographic detection model;
and 5: combining the point-by-point multi-time sequence analysis result in the step 2 and the space differentiation factor detection result in the step 4 to select the first n potential influence factors with larger influence degree as important influence factors of karst surface subsidence;
step 6: and establishing a binary logistic regression model between the karst surface subsidence event and the surface deformation rate and the certainty coefficient of each important influence factor, and realizing risk zoning on the karst surface subsidence of the research area.
Further, the method further comprises:
and 7: extracting the sedimentation horizontal gradient of the research area according to the surface deformation rate obtained by calculation in the step 1;
and 8: calculating a karst surface collapse risk value of the research area by adopting a weighted angle value deformation method according to the sedimentation horizontal gradient, the surface deformation rate and the human engineering construction space distribution;
and step 9: according to the risk value, carrying out risk zoning on the karst surface subsidence of the research area based on a natural breakpoint algorithm in the GIS;
step 10: and combining the point-by-point multi-time sequence analysis result in the step 2, the risk partition result of the binary logistic regression model in the step 6 and the risk partition result of the weighted angular value deformation method in the step 9 to finally obtain the karst surface subsidence risk evaluation result of the research area.
Further, the potential impact factors include: surface deformation rate, stratum lithology, karst development degree, overburden structure and thickness, distance from a water system with more than four levels, water-rich property of a sediment with a fourth level, distance from subway and large construction site and urban legal drawing rules.
Further, step 2 specifically includes:
step 2.1: determining historical karst surface subsidence point positions in a research area, and removing trend items of surface displacement time sequences, underground water level time sequences and Yangtze river water level time sequences of the historical karst surface subsidence point positions;
step 2.2: respectively extracting general power spectrums and phase angles of the earth surface displacement time sequence, the underground water level time sequence and the Yangtze river water level time sequence after the trend items are removed by utilizing cross wavelet transformation, and converting the phase angles into time delays;
step 2.3: and quantitatively identifying the influence degree of different water systems on the karst surface collapse based on the universal power spectrum and the time delay.
Further, in step 4, the space geographic detection model between the earth surface deformation rate and the certainty coefficients of all the potential influence factors in each grade is specifically: and taking the deterministic coefficients of all the potential influence factors in each grade as independent variables, and taking the earth surface deformation rate as an attribute variable to obtain the spatial differentiation degree of the coupling effect of a single potential influence factor and a plurality of potential factors on the spatial distribution of the karst earth surface deformation.
Further, in step 6, a binary logistic regression model between the karst surface collapse event and the surface deformation rate and the certainty coefficient of each important influence factor is established according to the formula (1):
wherein p is the probability of occurrence of karst surface collapse; b0Is the intercept; b1,…bnIs a coefficient of n significant impact factors; x1,…XnAnd determining the certainty factor for the n important influence factors, wherein the n important influence factors comprise the surface deformation rate.
Further, in step 8, the Risk value Risk of karst surface collapse of the research area is calculated according to the formula (4):
Risk＝((3×SHG)+Vsub)×MCden (4)
wherein SHG represents a sedimentation horizontal gradient, VsubRepresenting the rate of surface deformation, MCdenRepresenting the degree of contribution of the spatial distribution of the human engineering construction to the subsidence of the karst earth surface.
The invention has the beneficial effects that:
compared with the traditional karst surface subsidence factor identification method (mainly carrying out qualitative analysis on karst distribution and subsidence characteristics in space, or carrying out quantitative analysis on displacement time sequence and underground water change based on comparison of a few single points, but not quantitatively comparing contribution degree and factor significance of a single potential influence factor or coupling effect between the potential influence factors to karst surface subsidence from a space scale), the urban karst surface subsidence factor quantitative identification and disaster risk assessment method provided by the invention fully considers high-precision wide-area surface deformation rate obtained based on multi-temporal InSAR, establishes the spatial corresponding relation between the high-precision wide-area surface deformation rate and the karst surface subsidence potential influence factor as an intuitive index of the karst surface subsidence, and can quantitatively and objectively reflect the contribution degree of different potential influence factors to the karst surface subsidence distribution, and then risk assessment modeling is carried out on karst surface collapse disasters in a targeted mode, the redundancy problem of factors is avoided, and the model precision is improved. In addition, the weighted angle value deformation method of the karst surface deformation rate and the important influence factors directly based on InSAR actual measurement can also provide the most objective risk condition of the research area, so that the method is used for guiding the karst area lacking prior information to carry out influence factor scale determination and classification, and further the modeling precision of the binary logistic regression model is improved in turn.
Drawings
Fig. 1 is a schematic flow chart of a method for quantitative identification of urban rock litholytic notch factors and evaluation of disaster risks according to an embodiment of the present invention;
fig. 2 is a second schematic flowchart of a method for quantitative identification of urban rock litholytic collapse factors and evaluation of disaster risks according to an embodiment of the present invention;
FIG. 3 is a general power spectrum and phase angle plot of a surface displacement time sequence, a groundwater level time sequence, and a Yangtze river level time sequence provided by an embodiment of the present invention;
FIG. 4 is a block diagram of risk partitions and statistics of karst surface subsidence with and without surface deformation rate factors provided by embodiments of the present invention;
FIG. 5 is a diagram of a risk zone of karst surface collapse based on the WAD method according to an embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a method for evaluating risk of an urban karst surface collapse disaster, including the following steps:
s101: extracting the surface deformation rate and the surface displacement time sequence of the research area by using a StaMPS-SBAS method according to the long-time-sequence TerrasAR-X high-resolution SAR image of the research area;
s102: establishing a cross wavelet transformation model among the earth surface deformation rate, the point-by-point earth surface displacement time sequence, the underground water level time sequence and the Yangtze river water level time sequence, and quantitatively extracting a time-lag effect from a special point position scale so as to identify the influence degree of different water systems on the karst earth surface collapse;
s103: grading potential influence factors of karst surface subsidence of a research area, and determining the graded certainty factor of all the potential influence factors based on the karst surface subsidence accidents recorded historically;
s104: establishing a space geographic detection model between the earth surface deformation rate and the graded certainty coefficients of all the potential influence factors, and determining the influence degree of all the potential influence factors on karst earth surface collapse from a space scale according to the space diversity degree calculated by the space geographic detection model;
s105: combining the point-by-point multi-time sequence analysis result in the step 2 and the space differentiation factor detection result in the step 4 to select the first n potential influence factors with larger influence degree as important influence factors of karst surface subsidence;
s106: and establishing a binary logistic regression model between the karst surface subsidence event and the surface deformation rate and the certainty coefficient of each important influence factor, and realizing risk zoning on the karst surface subsidence of the research area.
According to the risk assessment method for the urban karst surface subsidence disasters, provided by the embodiment of the invention, the high-precision wide-area surface deformation rate based on the multi-temporal InSAR is fully considered and is used as an intuitive index of the karst surface subsidence, and the spatial correspondence between the high-precision wide-area surface deformation rate and potential influence factors of the karst surface subsidence is established, so that the contribution degree of different potential influence factors to the karst surface subsidence distribution is quantitatively and objectively reflected, further, the risk assessment modeling is carried out on the karst surface subsidence disasters in a targeted manner, the redundancy problem of the influence factors is avoided, and the precision of a risk assessment model is improved.
Example 2
On the basis of the above embodiment, the embodiment of the present invention further provides a method for quantitative identification of urban rock litholytic notch factors and evaluation of disaster risks, which is different from the above embodiment in that the embodiment of the present invention further includes the following steps:
s107: extracting a sedimentation horizontal gradient of the research area according to the earth surface deformation rate calculated in the step S101;
s108: calculating a karst surface collapse risk value of the research area by adopting a weighted angle value deformation method according to the sedimentation horizontal gradient, the surface deformation rate and the human engineering construction space distribution;
s109: according to the risk value, carrying out risk zoning on the karst surface subsidence of the research area based on a natural breakpoint algorithm in the GIS;
s110: and combining the point-by-point multi-time sequence analysis result in the step S102, the risk partitioning result of the binary logistic regression model in the step S106 and the risk partitioning result of the weighted angle value deformation method in the step S109 to finally obtain the karst surface collapse risk evaluation result of the research area.
According to the urban karst surface collapse disaster risk assessment method provided by the embodiment of the invention, the most objective risk condition of a research area can be provided directly based on the karst surface deformation rate actually measured by the multi-temporal InSAR and the weighted angular value deformation method of the important influence factors, and influence factor scale determination and classification can be conducted on karst areas lacking prior information, so that the modeling precision of a binary logistic regression model is improved in turn.
Example 3
On the basis of the above embodiments, taking the wuhan area as an example, as shown in fig. 2, an embodiment of the present invention provides a method for quantitative identification of urban rock solvus factors and evaluation of disaster risks, including the following steps:
s201: extracting the surface deformation rate and the surface displacement time sequence of the research area by using a StaMPS-SBAS method according to the long-time-sequence TerrasAR-X high-resolution SAR image of the research area;
specifically, TerrraSAR-X high spatial-temporal resolution SAR images covering a research area are collected, and registration resampling is carried out on main images. Combining and differentially interfering SAR time sequences according to a certain time-space baseline threshold value, selecting slow loss coherent filter points (SDFP), performing phase unwrapping, then performing atmosphere estimation and refining of phase unwrapping results by utilizing InSAR general atmosphere correction on-line products (GACOS) iteration, and finally solving the surface average deformation rate and surface displacement time sequence of the Wuhan region by utilizing a least square method for M unwrapping graphs obtained by correct unwrapping.
In this embodiment, the spatial baseline threshold is determined to be 250m and the temporal baseline threshold is determined to be 90 days according to the geometric incoherent theory and data processing experience.
S202: establishing a cross wavelet transformation model among the earth surface deformation rate, the point-by-point earth surface displacement time sequence, the underground water level time sequence and the Yangtze river water level time sequence, and quantitatively extracting a time-lag effect from a special point position scale so as to identify the influence degree of different water systems on the karst earth surface collapse;
specifically, the present step mainly includes the following substeps:
s2021: and (5) performing 90 m-90 m sampling processing on the karst surface deformation rate obtained in the step (S201), and performing null filling on null values in the sampled data in a proximity interpolation mode.
S2022: determining historical karst collapse points in a research area, and removing trend items of a surface displacement time sequence, an underground water level time sequence and a Yangtze river water level time sequence of the historical karst collapse points;
s2023: respectively extracting general power spectrums and phase angles of the earth surface displacement time sequence, the underground water level time sequence and the Yangtze river water level time sequence after removing the trend term by utilizing cross wavelet transformation, and converting the phase angles into time delay (as shown in figure 3);
s2024: and quantitatively identifying the influence degree of different water systems on the karst surface collapse based on the universal power spectrum and the time delay.
S203: grading potential influence factors of karst surface subsidence of a research area, and determining the graded certainty factor of all the potential influence factors based on the karst surface subsidence accidents recorded historically;
specifically, potential influence factors of the karst surface collapse are graded according to a natural breakpoint algorithm and karst collapse investigation specification (1: 50000); determining the deterministic coefficient CF of each level by using the deterministic coefficient method (CF) according to the number of the sky pits falling in each level and the area of the levelij{i＝1,2,3,4,5；j＝1,2,…,4}。
In the above formula, CFijA deterministic coefficient representing a jth rank of the ith influencing factor; PP (polypropylene)aIs the ratio of the number of the craters in the jth level of a certain potential influence factor to the area of the level; PP (polypropylene)sIs the ratio of the total number of the sky pits in the research area to the total karst area in Wuhan area.
In this embodiment, the potential influencing factors include: surface deformation rate, stratum lithology, karst development degree, overburden structure and thickness, distance from a water system with more than four levels, water-rich property of a sediment of a fourth system, distance from subway and large construction site and urban legal drawing rules.
From the aspect of surface deformation rate, the surface deformation rate in Wuhan region is classified according to-89.7-5.8 mm/yr, -5.7-1.3 mm/yr, -1.2-2.3 mm/yr and 2.4-29 mm/yr by using a natural discontinuity algorithm in GIS.
From the view of stratum lithology, the research area mainly comprises covering type karst, buried type karst and non-carbonate karst, and the covering type karst, the buried type karst and the non-carbonate karst together form a high sensitive area, a medium sensitive area and a low sensitive area of Wuhan karst surface collapse.
From the view of the development degree of the karst, according to the research code of karst collapse (1:50000), the development degree of the karst is divided into good development (kappa > 10%), medium development (10% ≧ kappa ≥ 3%), mild development (kappa < 3%) and non-karst areas, wherein kappa is the hole encountering rate of the drilled karst.
From the view of overlying soil structure and thickness, most of the pits occur in a typical binary structure coverage area according to historical karst collapse events, and the corresponding soil thickness is generally less than 15 m; then a multi-layer soft soil structure area with the thickness of 15-30m is covered, and then a buried karst and single-layer soil structure area with the thickness of 30-40m is covered. In addition, some karst collapse was also recorded in areas where the soil thickness exceeded 40 m.
From the viewpoint of the distance from the water system with more than four levels and the water-rich property of the fourth-series sediment, the water-rich property is 0-1000m, 3000 5000m and>5000m performs multi-buffer extraction for the distance from the water system of more than four levels. Classifying the fourth series of sediments as water-rich based on 361 well data>1000m3/d，100-1000m3D and<100m3/d。
from the distance to subway and large construction site and urban legal diagram, multivariate buffer area analysis is carried out on all subway lines and large construction sites according to the following conditions of <500m, 500-1000m, 1000-2000m and >2000 m. City planning maps are classified into manufacturing (M) -transportation (T) -municipal utility (U) -warehouse (W) -land, residential (R) -commercial (C) -land, greenland and agricultural (G) -land, and ecological control land and water.
S204: establishing a space geographic detection model between the earth surface deformation rate and the graded certainty coefficients of all the potential influence factors, and determining the influence degree of all the potential influence factors on karst earth surface collapse from a space scale according to the space diversity degree calculated by the space geographic detection model;
specifically, in this step, the spatial geographic detection model between the earth surface deformation rate and the certainty coefficients of all the potential influence factors in each stage is specifically: and taking the deterministic coefficients of all the potential influence factors in each grade as independent variables, and taking the earth surface deformation rate as an attribute variable to obtain the spatial differentiation degree of the coupling effect of a single potential influence factor and a plurality of potential factors on the spatial distribution of the karst earth surface deformation.
In this embodiment, the quantitative identification of the karst surface collapse influence factor from the special point location to the area can be realized through steps S203 and S204.
S205: the first n potential influence factors with larger influence degree are selected as important influence factors of the karst surface subsidence by combining the point-by-point multi-time sequence analysis result of the step S202 and the space differentiation factor detection result of the step S204,
specifically, the step realizes that the first n potential influence factors with larger influence degree are selected as important influence factors from the point-by-point scale and the space scale.
S206: establishing a binary logistic regression model (LR model) between the karst collapse event and the earth surface deformation rate and the certainty coefficient of each important influence factor to realize risk zoning on the karst earth surface collapse of the research area;
specifically, a binary logistic regression model between the karst collapse event and the earth surface deformation rate and the certainty coefficient of each important influence factor is established according to the formula (1):
wherein p is the probability of occurrence of karst surface collapse; b0Is the intercept; b1,…bnIs a coefficient of n significant impact factors; x1,…XnAnd determining the certainty factor for the n important influence factors, wherein the n important influence factors comprise the surface deformation rate.
And then, substituting the calculated logit (p) of each sampling unit into a formula (2) to obtain the probability of the karst collapse accident in each sampling unit:
in this embodiment, the earth surface deformation rates are respectively included and excluded as important influence factors, and the risk probability value p calculated based on the binary logistic regression is classified according to a natural break algorithm in the GIS, so as to obtain a risk partition map of karst earth surface collapse (as shown in fig. 4). Comparing the results of the risk zoning of the karst surface subsidence by the binary logistic regression model in both cases of considering the surface deformation rate and not considering the surface deformation rate, the effect of the surface deformation rate on the risk assessment results of the karst surface subsidence can be seen (as shown in fig. 4).
In this example, the evaluation indexes are:
1) percentage of disaster sites falling in high incidence areas
The maximum and low susceptibility zones should be a percentage of the total area of the study
Should be the largest;
2) percentage of disaster sites falling in high incidence areas
Area of each rank zone as a percentage of the total area of the entire study zone
Should be the largest;
as can be seen in fig. 4: the binary logistic regression model under the two conditions of considering the earth surface deformation rate and not considering the earth surface deformation rate has obvious difference on the risk zoning results of karst earth surface collapse. For example, the areas of the high, medium and low risk categories were reduced by 3.9%, reduced by 36.7% and increased by 37.1%, respectively. Risk zoning of the binary logistic regression model excluding the earth surface deformation rate results in 13% reduction in karst collapse incident prediction falling into high risk classes, while 8% increase in randomly generated non-karst collapse points falling into karst zones. Therefore, in the embodiment of the invention, the rationality and the accuracy of the karst surface collapse risk partition can be improved by incorporating the binary logistic regression model of the surface deformation rate factor based on the multi-temporal InSAR.
S207: extracting a sedimentation horizontal gradient of the research area according to the earth surface deformation rate calculated in the step S201;
specifically, the settlement horizontal gradient of adjacent SDFP points is extracted by using an angular deformation method (AD), specifically, after projection of karst surface deformation under a geographic coordinate system, the settlement horizontal gradient of the research area is calculated according to formula (3):
sedimentation horizontal gradient ═ (difference in surface deformation rate of adjacent SDFP points/horizontal distance of adjacent SDFP points)
×100％(3)
The settlement level gradient can reflect the tearing damage degree of the karst surface collapse to the surface structure.
S208: according to the sedimentation horizontal gradient and the earth surface deformation rate VsubAnd the space distribution of human engineering construction, and calculating the risk value of karst surface collapse of the research area by adopting a weighted angular value deformation method (WAD);
in the embodiment of the invention, the distance from the subway and the large construction site has great influence on the deformation of the karst ground surface, and the distance point-line density from the subway and the large construction site is used as the contribution degree MC of the space distribution of the human engineering construction to the subsidence of the karst ground surfaceden。
Therefore, the risk value of karst surface collapse of the research area is calculated according to the formula (4):
Risk＝((3×SHG)+Vsub)×MCden (4)
s209: according to the risk value, carrying out risk zoning on the karst surface subsidence of the research area based on a natural breakpoint algorithm in the GIS;
s210: and combining the point-by-point multi-time sequence analysis result in the step S202, the risk partitioning result of the binary logistic regression model in the step S206 and the risk partitioning result of the weighted angular value deformation method in the step S209 to finally obtain the karst surface collapse risk evaluation result of the research area.
Comparing fig. 4 and 5, the greater the likelihood that a region identified by the WAD method has a greater SHG value will be identified as a medium high risk level by the binary logistic regression model. For example, the rear lake avenue and the concord avenue (marked r in fig. 4 and 5), the ink lake north road, the hamyang parrot-hong Kong road (marked r in fig. 4 and 5), and the townhancuancun area (marked c in fig. 4 and 5), all of which are identified by two methods as regions of high and high risk.
In addition, compared with the traditional Analytic Hierarchy Process (AHP) based method, the method has higher precision in predicting the karst surface collapse risk. Based on a binary logistic regression model
And
compared with an AHP method which does not introduce a ground surface deformation rate factor based on multi-temporal InSAR, the method improves the ground surface deformation rate factor by 12.5 percent and 18.9 percent respectively. The Areas (AUC) of the working characteristic curves of the testees obtained by the binary logistic regression model and the traditional AHP method are respectively 0.911 and 0.812, and are also improved.
The method can complete quantitative information acquisition in the whole process of deformation monitoring, incentive recognition and disaster assessment, and provides technical support for early warning and treatment of urban karst surface subsidence.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A quantitative identification and disaster risk assessment method for urban rock litholytic collapse factors is characterized by comprising the following steps:
step 1: extracting the surface deformation rate and the surface displacement time sequence of the research area by using a StaMPS-SBAS method according to the long-time-sequence TerrasAR-X high-resolution SAR image of the research area;
step 2: establishing a cross wavelet transformation model among the earth surface deformation rate, the point-by-point earth surface displacement time sequence, the underground water level time sequence and the Yangtze river water level time sequence, and quantitatively extracting a time-lag effect from a special point position scale so as to identify the influence degree of different water systems on the karst earth surface collapse;
and step 3: grading potential influence factors of karst surface subsidence of a research area, and determining the graded certainty factor of all the potential influence factors based on the karst surface subsidence accidents recorded historically;
and 4, step 4: establishing a space geographic detection model between the earth surface deformation rate and the graded certainty coefficients of all the potential influence factors, and determining the influence degree of all the potential influence factors on karst earth surface collapse from a space scale according to the space diversity degree calculated by the space geographic detection model;
and 5: combining the point-by-point multi-time sequence analysis result in the step 2 and the space differentiation factor detection result in the step 4 to select the first n potential influence factors with larger influence degree as important influence factors of karst surface subsidence;
step 6: and establishing a binary logistic regression model between the karst surface subsidence event and the surface deformation rate and the certainty coefficient of each important influence factor, and realizing risk zoning on the karst surface subsidence of the research area.
2. The method for quantitative identification of urban litholytic notch factors and assessment of disaster risk according to claim 1, further comprising:
and 7: extracting the sedimentation horizontal gradient of the research area according to the surface deformation rate obtained by calculation in the step 1;
and 8: calculating a karst surface collapse risk value of the research area by adopting a weighted angle value deformation method according to the sedimentation horizontal gradient, the surface deformation rate and the human engineering construction space distribution;
and step 9: according to the risk value, carrying out risk zoning on the karst surface subsidence of the research area based on a natural breakpoint algorithm in the GIS;
step 10: and combining the point-by-point multi-time sequence analysis result in the step 2, the risk partition result of the binary logistic regression model in the step 6 and the risk partition result of the weighted angular value deformation method in the step 9 to finally obtain the karst surface subsidence risk evaluation result of the research area.
3. The method for quantitative identification of urban rock litholytic notch factors and assessment of disaster risk according to claim 1, wherein the potential impact factors include: surface deformation rate, stratum lithology, karst development degree, overburden structure and thickness, distance from a water system with more than four levels, water-rich property of a sediment with a fourth level, distance from subway and large construction site and urban legal drawing rules.
4. The method for quantitative identification of urban rock litholytic notch factors and assessment of disaster risk according to claim 1, wherein the step 2 specifically comprises:
step 2.1: determining historical karst surface subsidence point positions in a research area, and removing trend items of surface displacement time sequences, underground water level time sequences and Yangtze river water level time sequences of the historical karst surface subsidence point positions;
step 2.2: respectively extracting general power spectrums and phase angles of the earth surface displacement time sequence, the underground water level time sequence and the Yangtze river water level time sequence after the trend items are removed by utilizing cross wavelet transformation, and converting the phase angles into time delays;
step 2.3: and quantitatively identifying the influence degree of different water systems on the karst surface collapse based on the universal power spectrum and the time delay.
5. The method for quantitative identification of urban rock litholytic factor and evaluation of disaster risk according to claim 1, wherein in step 4, the model for detecting the space geography between the earth surface deformation rate and the certainty coefficient of each grade of all potential influencing factors is specifically: and taking the deterministic coefficients of all the potential influence factors in each grade as independent variables, and taking the earth surface deformation rate as an attribute variable to obtain the spatial differentiation degree of the coupling effect of a single potential influence factor and a plurality of potential factors on the spatial distribution of the karst earth surface deformation.
6. The method for the quantitative recognition of the urban rock litholytic factor and the evaluation of the disaster risk according to the claim 1, wherein in the step 6, a binary logistic regression model between the karst surface subsidence event and the surface deformation rate and the certainty coefficient of each important influence factor is established according to the formula (1):
wherein p is the probability of occurrence of karst surface collapse; b0Is the intercept; b1,…bnIs a coefficient of n significant impact factors; x1,…XnAnd determining the certainty factor for the n important influence factors, wherein the n important influence factors comprise the surface deformation rate.
7. The method for quantitative recognition of urban litholytic subsidence factors and disaster Risk assessment according to claim 2, wherein in step 8, the Risk value Risk of karst surface subsidence of the research area is calculated according to formula (4):
Risk＝((3×SHG)+Vsub)×MCden (4)
wherein SHG represents a sedimentation horizontal gradient, VsubRepresenting the rate of surface deformation, MCdenRepresenting the degree of contribution of the spatial distribution of the human engineering construction to the subsidence of the karst earth surface.
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