CN-121706487-B - InSAR time sequence analysis and finite element method-based multi-seismic-area ice shingle bank risk assessment method and system

CN121706487BCN 121706487 BCN121706487 BCN 121706487BCN-121706487-B

Abstract

The invention provides a multi-seismic regional ice shingle bank risk assessment method and system based on InSAR time sequence analysis and a finite element method, comprising the steps of carrying out InSAR time sequence analysis and deformation characteristic extraction on a multi-scene SAR image to obtain a freeze thawing deformation index FDI of ice shingle bank, adopting a finite element simulation method to simulate a three-dimensional finite element model of a moraine dam, calculating mechanical response inside the dam, calculating to obtain a seismic response index SEI, establishing a risk index RI model, adopting a risk index RI model with determined weight coefficients, carrying out risk assessment on working conditions of ice shingle bank in different monitoring intervals, and determining weak parts of the ice shingle bank and a comprehensive risk index RI of the moraine dam. According to the invention, the deformation information of the surface of the moraine dam obtained by InSAR and the mechanical response of the interior of the dam body simulated by a finite element method are comprehensively utilized, so that quantitative evaluation of the risk of the moraine dam in a multi-seismic area is realized, and a scientific basis is provided for safety monitoring and disaster early warning of ice shingle bank.

Inventors

REN LV
WANG JILIN
ZHANG DAREN
JIA CHAO
ZHANG YAN
LI DENGSONG

Assignees

水电水利规划设计总院有限公司
水电水利规划设计总院
中国水利水电建设工程咨询有限公司

Dates

Publication Date: 20260512
Application Date: 20251218

Claims (9)

1. The multi-seismic regional ice shingle bank risk assessment method based on InSAR time sequence analysis and a finite element method is characterized by comprising the following steps of: Step S1, a sample set is constructed, wherein each sample in the sample set corresponds to different monitoring intervals, and each monitoring interval of each sample covers a moraine dam seismic activity period and a freeze thawing cycle period and comprises a plurality of SAR images, seismic monitoring data and an actual risk index of ice shingle bank in the monitoring interval, wherein the SAR images, the seismic monitoring data and the actual risk index of ice shingle bank are acquired in the monitoring interval; s2, performing InSAR time sequence analysis and deformation characteristic extraction on the multi-scene SAR images in each sample by adopting a freeze-thawing deformation index calculation model to obtain a freeze-thawing deformation index FDI of ice shingle bank in a freeze-thawing cycle period of the monitoring interval; Step S3, constructing an ice shingle bank three-dimensional finite element model, adopting a finite element simulation method to simulate the seismic characteristic and the freeze thawing characteristic corresponding to the ice dam three-dimensional finite element model in the monitoring interval and calculate the mechanical response in the dam body, wherein the method comprises the following steps: Taking the earthquake monitoring data in each sample as input, and simultaneously taking the effects of earthquake motion and freeze thawing cycle into consideration to perform earthquake-freeze thawing coupling simulation to obtain the mechanical response of ice shingle bank in the dam under the earthquake-freeze thawing coupling effect, so as to position the weak part of ice shingle bank under the combined action of earthquake and freeze thawing; s4, adopting a seismic response index calculation model, and calculating to obtain a seismic response index SEI according to the internal mechanical response of the dam body; step S5, establishing a risk index RI model: RI=w 1 ×FDI+w 2 ×SEI+w 3 ×FDI 2 +w 4 ×SEI 2 +w 5 ×(FDI×SEI) Wherein RI is the comprehensive risk index of the tillite dam, and w 1 ,w 2 ,w 3 ,w 4 ,w 5 is the weight coefficient to be determined; Taking the freeze-thawing deformation index FDI and the earthquake motion response index SEI of each sample as input, taking the actual risk index of the tillite dam as a target, and training the risk index RI model by adopting each sample to obtain a risk index RI model with each weight coefficient determined; Step S6, adopting a risk index RI model with determined weight coefficients to perform risk assessment on working conditions of ice shingle bank in different monitoring intervals, and determining weak parts of ice shingle bank and a combined risk index RI of a tillite dam; Training the risk index RI model by adopting each sample to obtain the risk index RI model with each weight coefficient determined, wherein the risk index RI model comprises the following specific steps: Let the sample amount in the sample set be First, a third step The actual risk index of each sample is , Based on the first FDI, SEI, FDI2, SEI2, and (fdi×sei) obtained by the samples are expressed as: based on the first The model predictive risk index obtained from each sample is expressed as ; When training the risk index RI model by adopting each sample, the loss function L is L= ; The matrix form of the loss function L is expressed as: ; Wherein: as a vector of coefficients, ; As a matrix of the independent variables, ; As an actual risk index vector of the model, ; Deriving the loss function L with respect to the coefficient vector w and letting the derivative be 0: ; and (5) sorting to obtain a normal equation: ; Solving the equation to obtain the optimal estimated value of the coefficient ; Is a matrix An inverse matrix of (a); Optimal estimation of coefficients And if the model is checked to pass, solving to obtain each weight coefficient, and obtaining a risk index RI model with each weight coefficient determined.
2. The method for evaluating the risk of ice shingle bank in a multi-seismic region based on InSAR time series analysis and finite element method according to claim 1, wherein the step S2 is specifically: step S21, carrying out data preprocessing on the multi-view SAR image in the sample to obtain a multi-view preprocessed SAR image; s22, selecting an image pair combination with shorter space and time baselines from the SAR image after the multi-view pretreatment to construct an interference network, and extracting a deformation rate field and an accumulated deformation quantity on the surface of the moraine dam in the monitoring interval by carrying out phase unwrapping, atmospheric delay correction and track error correction treatment on the interference network; S23, determining a freeze-thawing period according to the climate characteristics of a multi-seismic area, wherein N freeze-thawing periods are shared in the monitoring interval, so that the number of freeze-thawing cycles is N, and obtaining an average deformation D_T of the freeze-thawing periods by using a time sequence analysis method in each freeze-thawing period; and S24, obtaining a freeze-thawing deformation index FDI of the monitoring interval by adopting a formula FDI= |D_T|multiplied by N, wherein the index FDI is used for quantifying the influence degree of the freeze-thawing action on the morus dam.
3. The method for evaluating the risk of ice shingle bank in a multi-seismic region based on InSAR timing analysis and finite element method according to claim 2, wherein the data preprocessing includes radiometric calibration, geometric correction and image registration; the radiometric calibration is that the gray value of SAR image is converted into physical scattering coefficient, the influence of sensor gain and system noise factor is eliminated, and the images acquired at different time are comparable; Establishing an SAR image imaging geometric model through terrain elevation data provided by the DEM, and determining a spatial mapping relation between radar beams and ground points; comparing the deviation of the actual pixel position and the theoretical pixel position in the SAR image, carrying out coordinate correction on the SAR image by an interpolation method to eliminate geometric distortion caused by topography fluctuation, enabling the SAR image to be accurately matched with the actual geographic coordinates, and providing a reliable geometric reference for interference processing and deformation extraction; the image registration is to accurately register the multi-view SAR images, so that the positions of the same ground object in different SAR images are accurately corresponding, and a foundation is provided for interference processing.
4. The method for evaluating the risk of ice shingle bank in a multi-seismic region based on InSAR time sequence analysis and finite element method according to claim 2, wherein the average deformation D_T of the freeze thawing period is obtained by using a time sequence analysis method in each freeze thawing period, specifically: And in each freeze thawing period, acquiring the time-period-by-time deformation quantity of each monitoring point of the ice shingle bank by using an InSAR time sequence analysis method, wherein the formula is as follows: ; Wherein: is the first Monitoring point is in the freezing and thawing period Deformation amount of the time period; is the first Monitoring the effective deformation length of the dam body at the point; is the first The water content of the volume of the tillite corresponding to the monitoring point; dry density of tillite; The mechanical parameters are the coefficient of linear expansion of freeze thawing of the tillite and are related to the water content and the dry density; is the first Monitoring point is in the freezing and thawing period Average temperature variation of the period; Measuring an arithmetic average value of time-interval deformation of the same monitoring point in the whole freeze thawing period to obtain an average deformation quantity of the monitoring point; And carrying out space statistics, such as weighted average, on the average deformation of all monitoring points in the key area of the dam body, wherein the weight is the area of the area represented by each monitoring point, and obtaining the average deformation D_T of the whole ice shingle bank in the freezing and thawing period.
5. The method for evaluating the risk of ice shingle bank in a multi-seismic region based on InSAR timing analysis and finite element method according to claim 1, wherein the step S3 is specifically: step S31, constructing an ice shingle bank three-dimensional finite element model: step S311, constructing a three-dimensional geometrical model of the tillite dam: Based on high-precision DEM data and geometric shape information of the tillite dam obtained by field investigation, establishing an ice shingle bank three-dimensional geometric model in finite element analysis software, and simulating the shape, size and upstream and downstream terrains of ice shingle bank; step S312, data preprocessing and coordinate system unification: the DEM data adaptation comprises the steps of importing high-precision DEM data into a three-dimensional geometric model of a tillite dam, reserving ice shingle bank and terrains with the periphery of 1-2km through a terrains data cutting module, and eliminating irrelevant areas; Step S313, importing in-situ investigation data, namely importing dam crest boundary line, upstream and downstream slope foot lines and dam body central axis vector data into a three-dimensional geometrical model of the tillus dam to generate a datum line of the three-dimensional geometrical model of the tillus dam; step S314, generating a basic terrain, namely generating a terrain curved surface based on the DEM point cloud, ensuring that the terrain curved surface is smooth and is attached to the real elevation; S315, determining material parameters, namely performing an indoor test on site sampling of the tillite dam, taking the center line and 2-3 meters on two sides of the dam crest, covering 0-1 meter on the surface layer of the dam crest and 1-3 meters on the shallow layer, taking 1 section of an upstream slope and a downstream slope every 10-15 meters according to the elevation, and taking 3 points in the slope foot, the slope and the slope top of each section; Step S316, boundary condition setting: Setting boundary conditions of a model according to actual stress conditions of the tillite dam in a monitoring zone, wherein the boundary conditions comprise fixing and restraining the bottom of the tillite dam, simulating supporting action of bedrock on the dam body, applying water pressure on an upstream surface and a downstream surface, simulating changes of the water level in front of the dam and the water level in the downstream surface, and upstream water pressure Downstream water pressure Wherein For the weight of the water to be high, Is the upstream and downstream water level elevation at the t moment, Applying a temperature load on the surface of the model to simulate the temperature change in the process of freeze thawing cycle, inputting earthquake acceleration time course according to the earthquake activity characteristics of the multi-earthquake area, and simulating the action of the earthquake on ice shingle bank; step S32, simulating seismic characteristics and freeze thawing characteristics of the three-dimensional finite element model of the tillite dam in the monitoring interval by adopting a finite element simulation method, and calculating internal mechanical responses of the dam, wherein the mechanical responses comprise stress field and strain field distribution of ice shingle bank under the earthquake-freeze thawing coupling effect, displacement and acceleration responses of key parts of the dam; Total stress tensor , Corresponding to three directions of space, the calculation formula is as follows: ; Wherein: temperature stress tensors induced for freeze-thaw cycles; Dynamic stress tensors induced for seismic action; Static stress tensor induced by self gravity and water pressure of the dam body; The acceleration response calculation formula is: ; ; ; Wherein: Is the key part At the position of Acceleration response at time; Is a freeze-thawing temperature acceleration component; Is a seismic dynamic acceleration component; Is the key part Effective deformation length of (2); Is the key part The coefficient of linear expansion of the tillite; Is the key part At the position of The difference between the moment and the initial temperature; Is the key part Is a concentrated mass of (2); is a damping coefficient; Is a rigidity coefficient; Representing in the direction of space Length bins of (a); Is the change of the ground acceleration with time under the action of earthquake; Is the key part At the position of Time edge A directional seismic dynamic velocity component; Is the key part At the position of Time edge A directional seismic dynamic displacement component.
6. The method for evaluating the risk of ice shingle bank in a multi-seismic region based on InSAR time series analysis and finite element method according to claim 1, wherein the step S4 is specifically: The earthquake motion response index calculation model is SEI=PGA multiplied by D, wherein PGA is earthquake motion peak acceleration and is obtained through regional earthquake monitoring data, D is a distance attenuation coefficient from a fracture zone and is calculated quantitatively according to the distance relation between ice shingle bank and a main movable fracture zone in a region, D=1/(r+1), and r is the linear distance from the center of a dam body of the tillus dam to the main movable fracture zone; And obtaining the earthquake motion response index SEI according to the earthquake motion response index calculation model.
7. The method for evaluating the risk of ice shingle bank in a multi-seismic area based on InSAR timing analysis and finite element method according to claim 1, further comprising, after determining the weak point of ice shingle bank and the combined risk index RI of the tillering dam: Step S7, risk classification and early warning: the risk grade of ice shingle bank is divided into low risk, medium risk and high risk according to the comprehensive risk index RI of the tillite dam, a corresponding risk threshold is set, when RI is smaller than 0.4, the risk is judged to be low, the dam body structure is basically stable, conventional monitoring can be conducted, when RI is smaller than or equal to 0.4 and smaller than 0.7, the risk is judged to be medium, the development of freeze thawing cracks is started, the local damage can be triggered by earthquakes, the monitoring frequency needs to be enhanced, when RI is larger than or equal to 0.7, the high risk is judged, the freeze thawing-earthquake coupling effect is obvious, the potential fracture surface is close to be through, early warning information needs to be issued immediately, and corresponding emergency measures are adopted.
8. The method for evaluating the risk of ice shingle bank in a multi-seismic region based on InSAR timing analysis and finite element method according to claim 1, further comprising: According to the newly acquired multi-view SAR image of the tillite dam and the earthquake monitoring data, updating the three-dimensional finite element model of the ice shingle bank in real time, calculating the latest freeze thawing deformation index FDI and the earthquake response index SEI, further obtaining the latest comprehensive risk index RI of the tillite dam, and dynamically evaluating the risk condition of the ice shingle bank according to the latest comprehensive risk index RI of the tillite dam.
9. The utility model provides a multi-seismic area moraine dam risk evaluation system based on InSAR time sequence analysis and finite element method which characterized in that includes: The system comprises a sample set construction module, a monitoring interval detection module and a monitoring module, wherein each sample in the sample set corresponds to different monitoring intervals, and the monitoring interval of each sample covers the seismic activity period and the freeze thawing cycle period of the tillus dam and comprises a plurality of SAR images, seismic monitoring data and the actual risk index of ice shingle bank in the monitoring interval, wherein the SAR images, the seismic monitoring data and the actual risk index of ice shingle bank are acquired in the monitoring interval; The freeze-thawing deformation index calculation model is used for performing InSAR time sequence analysis and deformation characteristic extraction on the multi-scene SAR images in each sample to obtain a freeze-thawing deformation index FDI of ice shingle bank in a freeze-thawing cycle period of the monitoring interval; the finite element simulation module is used for constructing a three-dimensional finite element model of the ice shingle bank, adopting a finite element simulation method to simulate the seismic characteristic and the freeze thawing characteristic corresponding to the monitoring interval of the three-dimensional finite element model of the ice tillbar dam and calculate the mechanical response inside the dam body, wherein the method comprises the following steps: Taking the earthquake monitoring data in each sample as input, and simultaneously taking the effects of earthquake motion and freeze thawing cycle into consideration to perform earthquake-freeze thawing coupling simulation to obtain the mechanical response of ice shingle bank in the dam under the earthquake-freeze thawing coupling effect, so as to position the weak part of ice shingle bank under the combined action of earthquake and freeze thawing; the earthquake motion response index calculation model is used for calculating and obtaining an earthquake motion response index SEI according to the internal mechanical response of the dam body; the risk index RI model and the training module are used for establishing the risk index RI model: RI=w 1 ×FDI+w 2 ×SEI+w 3 ×FDI 2 +w 4 ×SEI 2 +w 5 ×(FDI×SEI) Wherein RI is the comprehensive risk index of the tillite dam, and w 1 ,w 2 ,w 3 ,w 4 ,w 5 is the weight coefficient to be determined; taking the freeze-thawing deformation index FDI and the earthquake motion response index SEI of each sample as input, taking the actual risk index of the tillite dam as a target, and training the risk index RI model by adopting each sample to obtain a risk index RI model with each weight coefficient determined; The risk assessment module is used for carrying out risk assessment on the working conditions of the ice shingle bank in different monitoring intervals by adopting a risk index RI model with determined weight coefficients, and determining the weak part of the ice shingle bank and the comprehensive risk index RI of the tillering dam; Training the risk index RI model by adopting each sample to obtain the risk index RI model with each weight coefficient determined, wherein the risk index RI model comprises the following specific steps: Let the sample amount in the sample set be First, a third step The actual risk index of each sample is , Based on the first FDI, SEI, FDI2, SEI2, and (fdi×sei) obtained by the samples are expressed as: based on the first The model predictive risk index obtained from each sample is expressed as ; When training the risk index RI model by adopting each sample, the loss function L is L= ; The matrix form of the loss function L is expressed as: ; Wherein: as a vector of coefficients, ; As a matrix of the independent variables, ; As an actual risk index vector of the model, ; Deriving the loss function L with respect to the coefficient vector w and letting the derivative be 0: ; and (5) sorting to obtain a normal equation: ; Solving the equation to obtain the optimal estimated value of the coefficient ; Is a matrix An inverse matrix of (a); Optimal estimation of coefficients And if the model is checked to pass, solving to obtain each weight coefficient, and obtaining a risk index RI model with each weight coefficient determined.

Description

InSAR time sequence analysis and finite element method-based multi-seismic-area ice shingle bank risk assessment method and system Technical Field The invention relates to the technical field of geological disaster monitoring and evaluation, in particular to a multi-seismic regional ice shingle bank risk evaluation method and system based on InSAR time sequence analysis and a finite element method. Background The stability of the moraine dam in a multi-seismic area faces serious challenges due to the combined influence of various factors such as seismic activity, freeze thawing cycle, glacier ablation, precipitation and the like. Once the moraine dam is broken, secondary disasters such as large-scale flood, mud-rock flow and the like are caused, and huge damages are caused to the life and property safety and the ecological environment of downstream residents. Therefore, the risk condition of the ice shingle bank is accurately estimated, dam break disasters are early warned, and the method has important significance for disaster prevention and reduction. The traditional tillite dam risk assessment method mainly depends on means such as field investigation, level gauge measurement and GPS monitoring. The method is characterized in that the method comprises the steps of carrying out real-time investigation, measuring and GPS monitoring, wherein the real-time investigation is limited by terrain conditions, the traffic is inconvenient and safety risks exist in mountain gorge areas in a multi-earthquake area, the whole information of ice shingle bank is difficult to comprehensively and timely obtain, the level gauge measurement and the GPS monitoring belong to point type monitoring, the monitoring range is limited, continuous monitoring cannot be carried out on large-area areas of a morus dam, and the fine deformation and the whole deformation trend of the dam body are difficult to capture. The InSAR (synthetic aperture radar interferometry) technology is used as an advanced remote sensing monitoring means, has the capability of monitoring the surface deformation in a large area, high precision, all weather and all-weather time, can acquire millimeter-sized deformation of the surface of ice shingle bank, and provides a new way for the deformation monitoring of ice shingle bank. However, single InSAR monitoring can only acquire deformation information of the surface of ice shingle bank, and mechanical response and damage mechanism inside the dam cannot be deeply analyzed. The finite element method is a powerful numerical simulation method, can carry out mechanical analysis on a complex engineering structure, and can simulate stress and strain distribution of a dam body under the action of loads such as earthquake, freeze thawing and the like by establishing a three-dimensional finite element model of ice shingle bank, so that the damage process and potential breaking risk of the dam body are revealed. However, the finite element simulation requires accurate boundary conditions and material parameters, and in practice, the material characteristics and boundary conditions of the ice shingle bank are complex and variable, and are difficult to accurately obtain. Therefore, there is an urgent need for a multi-seismic regional ice shingle bank risk assessment method combining InSAR time sequence analysis and a finite element method, which fully exerts the advantages of the two methods and realizes comprehensive, accurate and real-time risk assessment of the tillus dam. Disclosure of Invention Aiming at the defects existing in the prior art, the invention provides a multi-seismic regional ice shingle bank risk assessment method and system based on InSAR time sequence analysis and a finite element method, which can effectively solve the problems. The technical scheme adopted by the invention is as follows: the first object of the invention is to provide a multi-seismic regional ice shingle bank risk assessment method based on InSAR time sequence analysis and a finite element method, which comprises the following steps: Step S1, a sample set is constructed, wherein each sample in the sample set corresponds to different monitoring intervals, and each monitoring interval of each sample covers a moraine dam seismic activity period and a freeze thawing cycle period and comprises a plurality of SAR images, seismic monitoring data and an actual risk index of ice shingle bank in the monitoring interval, wherein the SAR images, the seismic monitoring data and the actual risk index of ice shingle bank are acquired in the monitoring interval; s2, performing InSAR time sequence analysis and deformation characteristic extraction on the multi-scene SAR images in each sample by adopting a freeze-thawing deformation index calculation model to obtain a freeze-thawing deformation index FDI of ice shingle bank in a freeze-thawing cycle period of the monitoring interval; Step S3, constructing an ice shingle bank three-dimensional finite elem