Ground subsidence and disaster risk assessment method, system and equipment based on three-dimensional deformation

Abstract

The invention discloses a three-dimensional deformation-based land subsidence and disaster risk assessment method, system and device, and relates to the field of land subsidence disaster prevention, and the method comprises the steps: obtaining a synthetic aperture radar image set, underground water level dynamic characteristics and global navigation satellite system reference station data of a research region; based on the synthetic aperture radar image set, determining an unwrapping graph queue by adopting a coherence-based small baseline subset differential interference synthetic aperture radar method; estimating a vertical rate and a horizontal rate according to the unwrapping graph queue and the reference station data of the global navigation satellite system; performing high-coherence point selection and two-dimensional deformation decomposition on the direction data, further determining the surface displacement of the stratum in the research area, and describing the surface subsidence intensity of the research area by adopting subsidence difference and angle deformation; and evaluating the disaster risk of land subsidence in the research area according to the earth surface displacement of the stratum in the research area and the dynamic characteristics of the underground water level. According to the invention, the accuracy of land subsidence and disaster risk assessment is improved.

Description

Technical Field

[0001] The present application relates to the field of ground subsidence disaster prevention, and in particular to a ground subsidence and disaster risk assessment method, system and equipment based on three-dimensional deformation. Background Art

[0002] With the development of socio-economic technology, large areas of flat alluvial plains composed of loose sediments in arid areas have been transformed from desert grasslands to arable land. The expansion has increased the demand for groundwater resources and led to over-exploitation of groundwater resources. Long-term over-exploitation of groundwater will cause the water level of the aquifer to drop, resulting in a decrease in the pore water pressure of the aquifer itself and the relative impermeable layer, compaction of the soil layer, and ultimately severe ground subsidence, which may cause damage to surrounding communities and infrastructure, and cause permanent compaction of the aquifer, reducing the storage capacity of the aquifer. Therefore, measuring surface deformation, controlling and mitigating hazards associated with ground subsidence are essential to support groundwater management to maintain sustainable groundwater extraction activities.

[0003] Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology can measure surface deformation with an accuracy of millimeters per year. However, the factors that affect the spatial distribution and intensity of ground deformation are complex. Factors such as groundwater level drop, clay layer thickness, tectonic activity and building load all contribute to ground subsidence. The dual hydrogeological structure of the transition from a single-structure phreatic aquifer to a phreatic-confined aquifer in inland arid oases further exacerbates the uncertainty of the spatiotemporal evolution of ground subsidence and groundwater overexploitation. At present, the response mechanism of surface deformation in arid oases to continuous compaction of aquifers is still unclear, and there is a lack of systematic assessment of the hazards caused by surface deformation. In addition, Synthetic Aperture Radar (SAR) images of oases in arid areas have serious decoherence, low density of coherent points, and difficulty in separating noise. In this type of area, the inversion error of terrain deformation is large.

[0004] Therefore, it is urgent to solve the problems of severe coherence in the confused areas and quantification of surface deformation in arid areas, reveal the characteristics of stratum deformation during the development of irrigated agriculture, and clarify the response mechanism of the surface to the continuous compaction of aquifers, so as to achieve high-precision assessment of ground subsidence disaster risks. Summary of the invention

[0005] The purpose of this application is to provide a three-dimensional deformation-based ground subsidence and disaster risk assessment method, system and equipment, which can achieve high-precision assessment of ground subsidence and disaster risks.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for land subsidence and disaster risk assessment based on three-dimensional deformation, comprising:

[0008] Acquire a synthetic aperture radar image set, groundwater level dynamic characteristics and global navigation satellite system base station data of the study area; the images in the synthetic aperture radar image set include ascending orbit data and descending orbit data;

[0009] Based on the synthetic aperture radar image set, a disentanglement map queue is determined using a coherence-based small baseline subset differential interferometric synthetic aperture radar method;

[0010] estimating a vertical velocity and a horizontal velocity according to the unwrapping graph queue and the global navigation satellite system reference station data to obtain direction data;

[0011] Selecting high coherence points and performing two-dimensional deformation decomposition on the directional data to obtain a radar line of sight direction displacement;

[0012] Determine the surface displacement of the strata in the study area according to the displacement in the radar line of sight direction;

[0013] According to the surface displacement of the strata in the study area, the ground settlement intensity in the study area is described by using settlement difference and angle deformation;

[0014] Based on the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level, the disaster risk of ground subsidence in the study area is evaluated.

[0015] In the second aspect, the present application provides a land subsidence and disaster risk assessment system based on three-dimensional deformation, including:

[0016] A data acquisition module, used to acquire a synthetic aperture radar image set of a study area, groundwater level dynamic characteristics, and global navigation satellite system base station data; the images in the synthetic aperture radar image set include ascending orbit data and descending orbit data;

[0017] A phase unwrapping module is used to determine an unwrapping map queue based on the synthetic aperture radar image set by using a coherence-based small baseline subset differential interferometry synthetic aperture radar method;

[0018] A rate estimation module, configured to estimate a vertical rate and a horizontal rate according to the unwrapping graph queue and the GNSS reference station data to obtain direction data;

[0019] A displacement decomposition module is used to select high coherence points and perform two-dimensional deformation decomposition on the directional data to obtain a radar line of sight direction displacement;

[0020] A displacement determination module, used to determine the surface displacement of the strata in the study area according to the displacement in the direction of the radar line of sight;

[0021] An intensity determination module, for describing the ground settlement intensity of the study area by using settlement difference and angle deformation according to the surface displacement of the strata in the study area;

[0022] The risk assessment module is used to assess the disaster risk of ground subsidence in the study area based on the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level.

[0023] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned three-dimensional deformation-based ground subsidence and disaster risk assessment method.

[0024] According to the specific embodiments provided in this application, this application has the following technical effects:

[0025] The present application provides a method, system and device for assessing ground subsidence and disaster risk based on three-dimensional deformation. Based on a synthetic aperture radar image set, a method of small baseline subset differential interferometric synthetic aperture radar based on coherence is adopted to determine a disentangled map queue, which can effectively extract three-dimensional deformation information of the ground at different time points, and then construct a high-precision three-dimensional deformation field. The directional data is further subjected to high-coherence point selection and two-dimensional deformation decomposition to filter out noise sources, and the displacement in the direction of radar line of sight is obtained and the surface displacement of the strata in the study area is determined. The ground subsidence intensity in the study area is described by using settlement difference and angular deformation, and the disaster risk of ground subsidence in the study area is assessed according to the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level, thereby improving the assessment accuracy of ground subsidence and disaster risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 An application environment diagram of a ground subsidence and disaster risk assessment method based on three-dimensional deformation in one embodiment of the present application;

[0028] Figure 2 A schematic diagram of a flow chart of a method for land subsidence and disaster risk assessment based on three-dimensional deformation provided in one embodiment of the present application;

[0029] Figure 3A process flowchart of a method for land subsidence and disaster risk assessment based on three-dimensional deformation provided in one embodiment of the present application;

[0030] Figure 4 This is a schematic diagram of a relationship matrix of hazard levels in an embodiment of the present application;

[0031] Figure 5 A schematic diagram of functional modules of a land subsidence and disaster risk assessment system based on three-dimensional deformation provided in one embodiment of the present application;

[0032] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0035] The ground subsidence and disaster risk assessment method based on three-dimensional deformation provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send a synthetic aperture radar image set, groundwater level dynamic characteristics and global navigation satellite system base station data of the study area to the server 104. After receiving the synthetic aperture radar image set, groundwater level dynamic characteristics and global navigation satellite system base station data of the study area, the server 104 uses settlement differences and angle deformations to describe the ground settlement intensity of the study area, and evaluates the disaster risk of ground settlement in the study area. The server 104 can feedback the ground settlement intensity and disaster risk assessment results of the study area to the terminal 102. In addition, in some embodiments, the ground settlement and disaster risk assessment method based on three-dimensional deformation can also be implemented by the server 104 or the terminal 102 alone.

[0036] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0037] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a method for land subsidence and disaster risk assessment based on three-dimensional deformation is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 207.

[0038] Step 201, obtain a synthetic aperture radar image set, groundwater level dynamic characteristics and Global Navigation Satellite System (GNSS) base station data of the study area. The images in the synthetic aperture radar image set include ascending orbit data and descending orbit data.

[0039] In an exemplary embodiment, a synthetic aperture radar image set, groundwater level observation data, historical annual runoff, groundwater extraction, groundwater level drop data, and global navigation satellite system base station data of the study area are obtained. The dynamic characteristics of the groundwater level are determined based on the synthetic aperture radar image set, the groundwater level observation data, the historical annual runoff, the groundwater extraction, and the groundwater level drop data.

[0040] This application obtains ascending and descending Sentinel-1A and Sentinel-1S time series SAR images in the study area, uses ascending and descending Sentinel-1A and Sentinel-1S SAR images to obtain the spatiotemporal characteristics of surface deformation in the study area, combines groundwater level observation wells and historical annual runoff, groundwater extraction and groundwater level drop to analyze the dynamic characteristics of groundwater level, and collects GNSS base station data as verification data.

[0041] Among them, the ascending and descending SAR images of Sentinel-1 were obtained in the Interferometric Wideswath (IW) scanning mode with a total scanning length of 250 kilometers. It consists of three sub-scanning modes: IW1, IW2 and IW3, and each sub-scanning mode consists of a series of pulse trains.

[0042] Step 202: Based on the synthetic aperture radar image set, a disentanglement map queue is determined by using a coherence-based small baseline subset (SBAS) differential interferometric synthetic aperture radar method.

[0043] In an exemplary embodiment, a coherence-based SBAS differential InSAR method is used to obtain an SBAS dataset, where the SBAS dataset is a time series of surface deformation, i.e., a disentangled map sequence. Step 202 includes the following steps (21) to (25):

[0044] (21) Selecting a reference image from the synthetic aperture radar image set.

[0045] (22) Using the reference image to geometrically align other images in the synthetic aperture radar image set, and resampling the geometrically aligned images to a spatial reference system of the reference image to obtain a resampled image set.

[0046] The geometric registration satisfies the following conditions:

[0047] ①For VV polarization mode level 1 synthetic space radar scenario.

[0048] ② Phase noise was mitigated by using azimuth and range multifocal tools in the Environment for Visualizing Images (ENVI) remote sensing image processing platform, and the terrain phase component was subtracted using the 30-meter resolution digital surface model of the Shuttle Radar Topography Mission (SRTM).

[0049] ③ The burst interferogram is corrected using the enhanced spectral diversity method, especially the enhanced spectral diversity (ESD) estimation by range and azimuth in the overlapping region of adjacent and consecutive bursts.

[0050] ④ Perform interference phase compensation by removing the phase slope. For dual-channel scenes, the differential phase should be the incident angle λ / 4π, and the line of sight displacement spacing is 75m.

[0051] (23) Based on the resampled image set, a multi-view interference map is generated according to a preset time baseline threshold and a spatial baseline threshold.

[0052] (24) The multi-view interferogram is filtered using a Goldstein filter to obtain a filtered interferogram. The interferogram is filtered using a Goldstein filter to enhance the coherence of the InSAR time series interferogram.

[0053] (25) The filtered interference graph is phase unwrapped using a minimum cost flow to generate a unwrapped graph queue with a smaller spatial baseline and a shorter time interval.

[0054] Step 203, estimating the vertical speed and the horizontal speed according to the unwrapping graph queue and the GNSS reference station data to obtain direction data.

[0055] In an exemplary embodiment, since the dewrapped graph queue has phase noise and decoherence problems, the present application performs post-processing and estimates the vertical rate and horizontal rate. Step 203 includes the following steps (31) to (34):

[0056] (31) Low spatial frequency signals are removed from the disentanglement map queue, and the radar line of sight (LOS) velocity of each coherent target is converted into a temporary vertical direction.

[0057] Specifically, the low spatial frequency signals due to uncompensated orbit errors and atmospheric effects are removed from the unwrapped map queue (i.e., SBAS data set), and the LOS velocity of each coherent target is converted to a temporary vertical direction using the following formula;

[0058]

[0059] Among them, V Uj is the vertical velocity of the coherent target j, V LOSj is the coherent target j in the vertical direction of LOS, U LOSj is the cosine of the coherent target j in the horizontal direction of LOS.

[0060] (32) The position of each coherent target is adjusted according to the global navigation satellite system base station data to obtain a small baseline subset point data set.

[0061] Specifically, combined with the GNSS base station data, the following formula is used to recalculate the relative position of each coherent target at t n The displacement is adjusted relative to the original position of the initial time t0 of the time series:

[0062] d Uj (t n ) = d Uj' (t n )-V U' *{t n -t0};

[0063] Among them, t n is the current time, t0 is the initial time, d Uj (t n) is the current position of the relevant target j, d Uj' (t n ) is the original position of the coherent target j, V U' Vertical velocity offset resulting from reference point homogenization to remove low spatial frequency trends.

[0064] (33) Resample and interpolate the small baseline subset point data set to obtain a plurality of grid elements.

[0065] Specifically, the SBAS point datasets are initially resampled using a grid with a spacing of 100 meters to describe the intrinsic differences in the distribution and location of targets in the two geometries; the attributes of each dataset are then transferred to the grid using the nearest neighbor interpolation method. In particular, no interpolation is performed in areas without targets.

[0066] (34) Perform cosine vector inversion on each grid element to obtain an east-west velocity component, a north-south velocity component, and a vertical velocity component. The directional data includes the east-west velocity component, the north-south velocity component, and the vertical velocity component.

[0067] Specifically, for each grid element i, the direction cosines of the LOS of the Sentinel-1 IW in the east-west, north-south, and vertical directions are considered in each acquisition mode, and the following formula is used to invert the two-variable linear equations containing three unknown variables to obtain the east-west velocity component, the north-south velocity component, and the vertical velocity component:

[0068] V Ai =E Ai *V Ei +N Ai *V Ni +U Ai *V Ui ;

[0069] V Di =E Di *V Ei +N Di *V Ni +U Di *V Ui ;

[0070] Among them, V Ai is the annual velocity of the IW band of the ascending orbit at grid element i, E Ai is the known value of the east-west cosine of the IW band of the ascending orbit at grid element i, N Ai is the known value of the north-south direction cosine of the IW band of the ascending orbit at grid element i, U Ai is the known value of the vertical direction cosine of the IW band of the ascending orbit at grid element i, V Diis the annual velocity of the descending orbit IW band at each grid element i, E Di is the known value of the east-west cosine of the descending orbit IW band at each grid element i, N Di is the known value of the north-south direction cosine of the descending orbit IW band at each grid element i, U Di is the known value of the vertical direction cosine of the descending orbit IW band at each grid element i, V Ei is the east-west velocity component, V Ni is the north-south velocity component, V Ui is the vertical velocity component.

[0071] Step 204: performing high coherence point selection and two-dimensional deformation decomposition on the directional data to obtain LOS displacement.

[0072] In an exemplary embodiment, the directional data is imported into the Mintpy open source software, and noise is filtered out using high coherence point selection and two-dimensional deformation decomposition. After eliminating various noise sources, linear regression is applied to the resolved displacement time series to obtain the LOS displacement. Step 204 includes the following steps (41) to (44):

[0073] (41) Construct pixel stability index and coherence index:

[0074]

[0075]

[0076] Where X is the pixel stability index, Υ is the coherence index, N is the number of available interferograms, φ n is the phase value of the pixel of interest, is the mean of the phase values ​​at multiple moments at the pixel of interest, J() is the Bessel function, z1 and z2 represent the magnitudes of the two negative coherences, and the superscript * represents the complex conjugate.

[0077] (42) The directional data is imported into the Mintpy open source software, and high coherence points are selected based on the pixel stability index and the coherence index to obtain an interferometric synthetic aperture radar image.

[0078] Specifically, pixels whose frequency distributions of pixel stability index and coherence index are both in the first 80% are regarded as high coherence points.

[0079] (43) Projecting the three-dimensional surface deformation of the interferometric synthetic aperture radar image in the LOS direction onto a two-dimensional plane to obtain a two-dimensional surface deformation time series.

[0080] Specifically, by constructing a multi-dimensional spatial projection transformation relationship, the three-dimensional surface deformation in the LOS direction of the InSAR image is projected onto a two-dimensional plane.

[0081] (44) Taking the spatial coverage of the ascending orbit data as a reference and combining it with the descending orbit data of the corresponding date, the LOS displacement of the two-dimensional surface deformation time series is decomposed into the vertical direction and the east-west direction to obtain the LOS displacement in the vertical direction and the LOS displacement in the east-west direction.

[0082] Specifically, the following formula is used to determine the LOS displacement in the vertical direction and the LOS displacement in the east-west direction:

[0083] d U cosθ-d E cosαsinθ+d N sinαsinθ=d LOS +δ LOS ;

[0084]

[0085] Among them, d U is the LOS displacement in the vertical direction, d E is the LOS displacement in the east-west direction, d N is the LOS displacement to the north, α is the heading angle of the satellite, θ is the incident angle of the satellite, and d LOS is the observed LOS direction position, δ LOS is the observed LOS direction error. The superscript A represents the parameter obtained from the orbit-ascending data, and the superscript D represents the parameter obtained from the orbit-descending data.

[0086] Step 205: determining the surface displacement of the strata in the study area according to the LOS displacement.

[0087] In an exemplary embodiment, step 205 includes the following steps (51) to (52):

[0088] (51) The strata in the study area are regarded as homogeneous, isotropic and elastic flat cylinders in half-space. The total displacement of a certain point caused by the contraction of the flat cylinder is obtained by superposing the displacements of countless infinitesimal strain units.

[0089] (52) Based on the flat cylinder and the LOS displacement, the surface displacement of the strata in the study area is determined using the following formula:

[0090]

[0091] Among them, L is the surface displacement of the stratum in the study area, represents the surface displacement on the vertical component, v is the Poisson's ratio, Δh is the changing thickness of the flat cylinder, R is the radius of the flat cylinder, r is the center distance of the flat cylinder, D is the depth of the flat cylinder, J1() is the zero-order Bessel function, J2() is the first-order Bessel function, and α' is the variable used for the integration operator, which represents the LOS displacement.

[0092] Step 206, based on the surface displacement of the strata in the study area, the ground settlement intensity in the study area is described using settlement difference and angle deformation.

[0093] In an exemplary embodiment, the relationship between the settlement difference and the angular deformation is:

[0094]

[0095] Where β is the angle of deformation, Δd ui is the difference in settlement between two points, i.e. vertical displacement, l res is the image resampling resolution of the LOS displacement, which is determined according to the surface displacement of the strata in the study area.

[0096] Step 207, assessing the disaster risk of ground subsidence in the study area based on the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level.

[0097] In an exemplary embodiment, based on the sedimentation gradient, combined with the exposure and vulnerability information of the risk factors, the optimal threshold of the empirical statistical model is used to divide the hazard level. Among them, the risk factors include population size, transportation infrastructure, housing density and groundwater level dynamic characteristics. Step 207 includes the following steps (71) to (75):

[0098] (71) Determine a plurality of subsidence gradients according to the surface displacement of the strata in the study area. Specifically, the different subsidence gradients are divided according to the surface displacement l of the strata in the study area, and the total vertical displacement Δd of each 100-meter grid cell compared with its adjacent cells is calculated as: ui The maximum percentage of

[0099] (72) Obtain the population, transportation infrastructure, and housing density of the study area.

[0100] (73) The dynamic characteristics of groundwater levels, population size, transportation infrastructure, and housing density in the study area were weighted to obtain a comprehensive risk index.

[0101] (74) A support vector machine (SVM) classification model was used with sedimentation gradient and comprehensive risk index as input variables and hazard level as output variable to find the optimal threshold for classifying hazard levels.

[0102] (75) Based on the multiple settlement gradients and the comprehensive risk index, multiple hazard levels are divided according to the optimal threshold to assess the disaster risk of ground settlement in the study area. Figure 4 As shown, the hazard levels are specifically divided into R0, R1, R2, R3 and R4.

[0103] This application obtains the surface deformation data set through time series InSAR analysis; uses high coherence point selection and two-dimensional deformation decomposition to filter out noise, applies linear regression to the analyzed displacement time series to obtain LOS displacement, extracts high coherence points and LOS displacement decomposition to filter out noise sources, uses Geertsma analytical method to model elastic medium surface displacement, and describes the intensity of ground subsidence through settlement differences and angle deformation; according to the settlement gradient, combined with the exposure degree and vulnerability information of risk factors, the optimal threshold of the SVM classification model is used to divide it into five hazard levels of R0, R1, R2, R3 and R4, to achieve risk assessment of the hazard of ground subsidence. The three-dimensional deformation field is obtained through SAR image analysis, and the ground subsidence modeling and disaster risk assessment of the study area are carried out, which has certain ecological and social value.

[0104] In summary, this application has the following advantages:

[0105] (1) The SBAS differential InSAR method is used to process multi-temporal synthetic aperture radar data, which can effectively extract the three-dimensional deformation information of the ground at different time points, including vertical settlement and horizontal displacement, and then construct a high-precision three-dimensional deformation field.

[0106] (2) Aiming at the problem of severe deterioration of SAR image interferometric phase quality (decoherence) in oasis areas with intense agricultural activities, the pixel stability index and coherence index were combined to extract high coherence points. The threshold for coherence point selection was adjusted based on the coherence point density and GNSS records. A coherence point selection method suitable for oases in arid areas was constructed to solve the problems of severe decoherence in oasis areas, low coherence point density, and difficulty in separating noise. This method can effectively monitor the surface deformation of oases in arid areas.

[0107] (3) Using formation deformation information as a constraint, the surface displacement of the elastic medium caused by the change in pore pressure of the underground reservoir is modeled and the hydrogeological parameters are inverted. The groundwater level drop and the thickness of the clay layer are innovatively integrated to evaluate the accuracy of the inversion parameters and to construct an accurate surface displacement model based on settlement differences and angular deformation.

[0108] Based on the same inventive concept, the embodiment of the present application also provides a three-dimensional deformation-based ground subsidence and disaster risk assessment system for implementing the three-dimensional deformation-based ground subsidence and disaster risk assessment method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more three-dimensional deformation-based ground subsidence and disaster risk assessment system embodiments provided below can refer to the limitations of the three-dimensional deformation-based ground subsidence and disaster risk assessment method above, and will not be repeated here.

[0109] In an exemplary embodiment, Figure 5 As shown, a ground subsidence and disaster risk assessment system based on three-dimensional deformation is provided, including: a data acquisition module 501, a phase unwrapping module 502, a rate estimation module 503, a displacement decomposition module 504, a displacement determination module 505, an intensity determination module 506 and a risk assessment module 507.

[0110] The data acquisition module 501 is used to acquire a synthetic aperture radar image set of the study area, groundwater level dynamic characteristics and global navigation satellite system reference station data. The images in the synthetic aperture radar image set include ascending orbit data and descending orbit data.

[0111] The phase unwrapping module 502 is used to determine an unwrapping map queue based on the synthetic aperture radar image set by adopting a coherence-based small baseline subset differential interferometry synthetic aperture radar method.

[0112] The speed estimation module 503 is used to estimate the vertical speed and the horizontal speed according to the unwrapping graph queue and the GNSS reference station data to obtain direction data.

[0113] The displacement decomposition module 504 is used to perform high coherence point selection and two-dimensional deformation decomposition on the direction data to obtain LOS displacement.

[0114] The displacement determination module 505 is used to determine the surface displacement of the strata in the study area according to the LOS displacement.

[0115] The strength determination module 506 is used to describe the ground subsidence strength of the study area by using the settlement difference and the angle deformation according to the surface displacement of the strata in the study area.

[0116] The risk assessment module 507 is used to assess the disaster risk of ground subsidence in the study area according to the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level.

[0117] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a set of synthetic aperture radar images of the study area, dynamic characteristics of groundwater levels and global navigation satellite system base station data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a ground subsidence and disaster risk assessment method based on three-dimensional deformation is implemented.

[0118] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0120] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0122] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0123] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0124] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A land subsidence and disaster risk assessment method based on three-dimensional deformation, characterized in that: The three-dimensional deformation-based land subsidence and disaster risk assessment method includes: Acquire a synthetic aperture radar image set, groundwater level dynamic characteristics and global navigation satellite system base station data of the study area; the images in the synthetic aperture radar image set include ascending orbit data and descending orbit data; Based on the synthetic aperture radar image set, a disentanglement map queue is determined using a coherence-based small baseline subset differential interferometric synthetic aperture radar method; estimating a vertical velocity and a horizontal velocity according to the unwrapping graph queue and the global navigation satellite system reference station data to obtain direction data; Selecting high coherence points and performing two-dimensional deformation decomposition on the directional data to obtain a radar line of sight direction displacement; Determine the surface displacement of the strata in the study area according to the displacement in the radar line of sight direction; According to the surface displacement of the strata in the study area, the ground settlement intensity in the study area is described by using settlement difference and angle deformation; Based on the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level, the disaster risk of ground subsidence in the study area is evaluated.

2. The method for land subsidence and disaster risk assessment based on three-dimensional deformation according to claim 1 is characterized in that: Acquire synthetic aperture radar image sets, groundwater level dynamic characteristics and global navigation satellite system base station data of the study area, including: Acquire synthetic aperture radar image sets, groundwater level observation data, historical annual runoff, groundwater extraction, groundwater level drawdown data, and global navigation satellite system base station data of the study area; The dynamic characteristics of the groundwater level are determined based on the synthetic aperture radar image set, the groundwater level observation data, the historical annual runoff, the groundwater extraction volume and the groundwater level drop data.

3. The method for land subsidence and disaster risk assessment based on three-dimensional deformation according to claim 1 is characterized in that: Based on the synthetic aperture radar image set, a disentanglement map queue is determined by using a coherence-based small baseline subset differential interferometric synthetic aperture radar method, specifically comprising: Selecting a reference image from the synthetic aperture radar image set; Using the reference image to geometrically register other images in the synthetic aperture radar image set, and resampling the geometrically registered images to a spatial reference system of the reference image to obtain a resampled image set; Based on the resampled image set, generating a multi-view interference map according to a preset time baseline threshold and a spatial baseline threshold; The multi-view interference pattern is filtered using a Goldstein filter to obtain a filtered interference pattern; The filtered interference graph is phase unwrapped using a minimum cost flow to generate an unwrapped graph queue.

4. The method for land subsidence and disaster risk assessment based on three-dimensional deformation according to claim 1 is characterized in that: According to the unwrapping graph queue and the global navigation satellite system reference station data, the vertical speed and the horizontal speed are estimated to obtain the direction data, specifically including: Remove low spatial frequency signals from the disentanglement map queue and convert the radar line-of-sight direction velocity of each coherent target into a temporary vertical direction; adjusting the position of each coherent target according to the global navigation satellite system reference station data to obtain a small baseline subset point data set; Resampling and interpolating the small baseline subset point data set to obtain a plurality of grid elements; Cosine vector inversion is performed on each grid element to obtain an east-west velocity component, a north-south velocity component and a vertical velocity component; the direction data includes the east-west velocity component, the north-south velocity component and the vertical velocity component.

5. The method for land subsidence and disaster risk assessment based on three-dimensional deformation according to claim 1 is characterized in that: The directional data is subjected to high coherence point selection and two-dimensional deformation decomposition to obtain the radar line of sight direction displacement, specifically including: Construct pixel stability index and coherence index; The directional data is imported into the Mintpy open source software, and high coherence points are selected based on the pixel stability index and the coherence index to obtain an interferometric synthetic aperture radar image; Projecting the three-dimensional surface deformation of the interferometric synthetic aperture radar image in the radar line of sight direction onto a two-dimensional plane to obtain a two-dimensional surface deformation time series; Taking the spatial coverage of the ascending orbit data as a reference and combining it with the descending orbit data of the corresponding date, the radar line of sight displacement of the two-dimensional surface deformation time series is decomposed into the vertical direction and the east-west direction to obtain the radar line of sight displacement.

6. The method for land subsidence and disaster risk assessment based on three-dimensional deformation according to claim 1, characterized in that: According to the displacement in the radar line of sight direction, the surface displacement of the strata in the study area is determined, specifically including: The strata in the study area are considered as flat cylinders in a homogeneous, isotropic, and elastic half-space; Based on the displacement of the flat cylinder and the radar line of sight, the following formula is used to determine the surface displacement of the strata in the study area: Where l is the surface displacement of the stratum in the study area, v is the Poisson's ratio, Δh is the changing thickness of the flat cylinder, R is the radius of the flat cylinder, r is the center distance of the flat cylinder, D is the depth of the flat cylinder, J1( ) is the zero-order Bessel function, J2( ) is the first-order Bessel function, and α' is the variable used for the integral operator, which represents the displacement in the radar line of sight direction.

7. The method for land subsidence and disaster risk assessment based on three-dimensional deformation according to claim 1, characterized in that: The relationship between settlement difference and angular deformation is: Where β is the angle of deformation, Δd ui is based on the difference in settlement between two points, l res It is the image resampling resolution of the displacement in the radar line of sight direction, which is determined according to the surface displacement of the strata in the study area.

8. The method for land subsidence and disaster risk assessment based on three-dimensional deformation according to claim 1 is characterized in that: Based on the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level, the disaster risk of ground subsidence in the study area is assessed, including: determining a plurality of subsidence gradients based on surface displacements of strata in the study area; Obtain the population, transportation infrastructure, and housing density of the study area; The dynamic characteristics of groundwater levels, population, transportation infrastructure and housing density in the study area were weighted to obtain a comprehensive risk index; The support vector machine classification model is used, with the sedimentation gradient and comprehensive risk index as input variables and the hazard level as output variable, to find the optimal threshold for dividing the hazard level; According to the multiple settlement gradients and the comprehensive risk index, multiple hazard levels are divided according to the optimal threshold value to evaluate the disaster risk of ground subsidence in the study area.

9. A land subsidence and disaster risk assessment system based on three-dimensional deformation, applied to the land subsidence and disaster risk assessment method based on three-dimensional deformation according to any one of claims 1 to 8, characterized in that: The three-dimensional deformation-based land subsidence and disaster risk assessment system includes: A data acquisition module, used to acquire a synthetic aperture radar image set of a study area, groundwater level dynamic characteristics, and global navigation satellite system base station data; the images in the synthetic aperture radar image set include ascending orbit data and descending orbit data; A phase unwrapping module is used to determine an unwrapping map queue based on the synthetic aperture radar image set by using a coherence-based small baseline subset differential interferometry synthetic aperture radar method; A rate estimation module, configured to estimate a vertical rate and a horizontal rate according to the unwrapping graph queue and the GNSS reference station data to obtain direction data; A displacement decomposition module is used to select high coherence points and perform two-dimensional deformation decomposition on the directional data to obtain a radar line of sight direction displacement; A displacement determination module, used to determine the surface displacement of the strata in the study area according to the displacement in the direction of the radar line of sight; An intensity determination module, for describing the ground settlement intensity of the study area by using settlement difference and angle deformation according to the surface displacement of the strata in the study area; The risk assessment module is used to assess the disaster risk of ground subsidence in the study area based on the surface displacement of the strata in the study area and the dynamic characteristics of the groundwater level.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the three-dimensional deformation-based ground subsidence and disaster risk assessment method described in any one of claims 1 to 8.

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