A method and system for monitoring land subsidence

Abstract

The application discloses a kind of ground subsidence monitoring method and system, it is related to geological monitoring technical field, including: the airport is on multiple satellite platforms Ground deformation time sequence is unified to same reference datum;Select the SAR ground image of a satellite platform, and with each pixel point as center, according to the preset search radius, search the corresponding homonymy pixel point of each pixel point in the SAR ground image of another platform;According to search result, the ground deformation time sequence of each pixel point is fused with the ground deformation time sequence of the corresponding searched homonymy pixel point of the pixel point by singular value decomposition SVD, obtains the high time resolution deformation time sequence of long time span that can reflect real ground subsidence.The application can obtain long monitoring period high time resolution real ground deformation characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological monitoring, in particular to a monitoring method and system for ground subsidence. BACKGROUND

[0002] Ground subsidence is a phenomenon that the ground surface deforms downward due to natural or human activities. In particular, in the airport area, ground subsidence, cracks and collapse are prone to occur due to construction and the load of the runway by the aircraft, which affects the normal operation of the airport. Therefore, it is necessary to conduct full-coverage and high-precision ground surface deformation monitoring in the airport and surrounding areas, to obtain the spatio-temporal evolution of the airport deformation in a timely and accurate manner, and to ensure the safety of the airport infrastructure and the aircraft flight.

[0003] In the prior art, the conventional ground subsidence monitoring means (such as leveling and GPS) has high measurement accuracy, but can only obtain some discrete airport point deformation information, cannot conduct full-coverage monitoring on the entire airport and its surrounding areas, and cannot obtain the historical deformation characteristics of the airport. The synthetic aperture radar interferometric measurement (InSAR) technology has the characteristics of all-time, all-weather and non-contact, and is a very suitable monitoring means for the airport area. However, due to the limitations of the service life, revisit period and satellite attitude of a single satellite platform, it is often impossible to obtain long-time span high-time resolution real ground deformation characteristics.

[0004] In summary, how to obtain long monitoring period high-time resolution real ground deformation characteristics is an important problem to be solved. SUMMARY

[0005] The embodiment of the present application provides a monitoring method and system for ground subsidence, which can solve the problem that long monitoring period high-time resolution real ground deformation characteristics cannot be obtained in the prior art.

[0006] The embodiment of the present application provides a monitoring method for ground subsidence, comprising the following steps:

[0007] Obtaining SAR ground images of the airport on multiple satellite platforms, and obtaining ground deformation time series in the SAR ground images of each satellite platform by InSAR technology;

[0008] Unifying the geographic codes and LOS deformation in the ground deformation time series of the multiple satellite platforms to the same reference datum;

[0009] Selecting a SAR ground image of a satellite platform, and searching for corresponding homonymic pixel points of each pixel point in the SAR ground image of another satellite platform according to a preset search radius and taking each pixel point as the center; wherein the homonymic pixel points represent pixel points at the same geographic location;

[0010] The surface deformation time series of each pixel is fused with the surface deformation time series of the corresponding pixel found by the search using singular value decomposition (SVD) to obtain a high temporal resolution deformation time series that can reflect the long time span of real ground subsidence.

[0011] Furthermore, the specific steps for determining the temporal sequence of surface deformation of the searched pixels with the same name include:

[0012] If there is a corresponding pixel with the same name within the search radius, the time series of surface deformation at that point will be used as the search result.

[0013] If there are two or more pixels with the same name within the search radius, the average of the surface deformation time series of all pixels with the same name will be used as the search result.

[0014] Furthermore, the step of fusing the land deformation time series of each pixel with the land deformation time series of the corresponding pixel found using Singular Value Decomposition (SVD) includes the following specific steps:

[0015] Record each pixel of the first platform at the 1st i The cumulative deformation of the scene image is: S i ( i =1, 2, 3, ... m In the second platform, the corresponding pixel of each pixel in the first platform is in the [missing information]. j Cumulative deformation of scene images T j ( j =1, 2, 3, ... n );in, m This represents the total number of scenes per pixel on the first platform. n This indicates the total number of mid-range pixels with the same name on the second platform;

[0016] Will ST k ( k =1, 2, 3, ... m + n ) indicates that the fused time series is at the 1st... k Cumulative deformation of the scene image;

[0017] t k and v k Indicates the fusion of the first k Jing and Di k +1 scene image corresponding to time difference and deformation rate;

[0018] Establish the relationship between velocity and displacement:

[0019]

[0020]

[0021] wherein, , are indexes of the first data corresponding to the first deformation and indexes of the second data corresponding to the second deformation in the fusion time sequence ST respectively; i j

[0022] According to the relationship between the rate and the displacement, a matrix equation is established:

[0023]

[0024] wherein, the matrix t is a matrix of order ( m + n -2) ( m + n -1); v represents a rate vector, S represents a deformation time sequence of the first platform, T represents a deformation time sequence of the second platform;

[0025] The singular value decomposition (SVD) is used on the matrix equation to obtain the rate vector v .

[0026] According to the time and the deformation rate of each image, the ground deformation time sequence of each pixel point and the fusion cumulative deformation of the ground deformation time sequence of the corresponding searched homonymic pixel point of the pixel point are obtained.

[0027] Further, the ground deformation time sequence in each satellite platform SAR image is obtained by the InSAR technology, and the specific steps include:

[0028] The airport ground surface in the SAR images of multiple satellite platforms is preprocessed to obtain a differential interferogram;

[0029] The deformation time sequence inversion is performed on the SAR images of each satellite platform by the InSAR technology to obtain the ground deformation time sequence of the airport area.

[0030] Further, the geographical coding and the LOS direction deformation in the ground deformation time sequences of multiple satellite platforms are unified to the same reference datum, and the specific steps include:

[0031] When the deformation time sequence inversion is performed, the latitude and longitude of the deformation reference point remain consistent;

[0032] ​​After the deformation time series inversion ends, the geographic codes of the deformation time series inversion results of the multiple satellite platforms are unified to the same geographic coordinate system; and the LOS direction deformation of the deformation time series inversion of the multiple satellite platforms is projected to the vertical direction.

[0033] The embodiment of the present application provides a monitoring system for ground subsidence, comprising:

[0034] A deformation time series acquisition module is configured to acquire SAR ground surface images of an airport on multiple satellite platforms, and acquire ground surface deformation time series in the SAR ground surface images of each satellite platform through InSAR technology.

[0035] A reference unification module is configured to unify the geographic codes and the LOS direction deformation in the ground surface deformation time series of the multiple satellite platforms to the same reference.

[0036] A same-named pixel point search module is configured to select a SAR ground surface image of one satellite platform, and search for corresponding same-named pixel points of each pixel point in a SAR ground surface image of another satellite platform according to a preset search radius with each pixel point as the center.

[0037] A fusion module is configured to fuse the ground surface deformation time series of each pixel point and the ground surface deformation time series of the corresponding searched same-named pixel point of the pixel point through singular value decomposition (SVD) to obtain a long-time-span high-time-resolution deformation time series capable of reflecting real ground subsidence.

[0038] The embodiment of the present application provides a monitoring method and system for ground subsidence, and has the following beneficial effects compared with the prior art:

[0039] By searching for corresponding same-named pixel points of the pixel points of one satellite platform on another satellite platform, the pixel points needing to be fused are determined, and then the ground surface deformation time series of each pixel point and the ground surface deformation time series of the corresponding searched same-named pixel point of the pixel point are fused together through singular value decomposition (SVD) to obtain a long-time ground surface deformation time series of the same geographic position pixel point, thereby increasing the monitoring resolution, and finally obtaining a long-time high-resolution ground surface deformation time series, which truly reflects the deformation of ground subsidence. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 A flowchart of different platform SAR data fusion of a monitoring method for ground subsidence is provided for the embodiment of the present application.

[0041] Figure 2Different data homonymy points determination of the ground subsidence monitoring method provided by the embodiment of the present application, wherein (a) is no TerraSAR-X point in the search radius R; (b) is one TerraSAR-X point in the search radius; (c) is two or more TerraSAR-X points in the search radius;

[0042] Figure 3 The time sequence fusion schematic diagram of the ground subsidence monitoring method provided by the embodiment of the present application;

[0043] Figure 4 The fusion InSAR deformation map of the ground subsidence monitoring method provided by the embodiment of the present application, wherein (a) is the vertical cumulative displacement of the first phase in October 2019, (b) is the vertical cumulative displacement of the first phase in October 2020, (c) is the vertical cumulative displacement of the first phase in October 2021, (d) is the vertical cumulative displacement of the first phase in October 2022, (e) is the vertical cumulative displacement of the first phase in October 2023, and (f) is the vertical cumulative displacement of the first phase in October 2024;

[0044] Figure 5 The feature point time sequence fusion result of the ground subsidence monitoring method provided by the embodiment of the present application, wherein (a) is the displacement time sequence of the airport maintenance area P1, (b) is the displacement time sequence of the freight area P2, and (c) is the displacement time sequence of the terminal P3.

[0045] Figure 6 The InSAR and GNSS result comparison and verification of each station of the ground subsidence monitoring method provided by the embodiment of the present application, wherein (a) is the G1 station, (b) is the G2 station, (c) is the G3 station, (d) is the G4 station, (e) is the G5 station, and (f) is the G6 station. DETAILED DESCRIPTION

[0046] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. In the following description, a large number of specific details are set forth in order to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the concept of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0047] Reference Figure 1 The embodiment of the present application provides a ground subsidence monitoring method, which comprises the following steps:

[0048] Step one: Obtain SAR ground surface images of the airport in multiple satellite platforms, and obtain ground surface deformation time series in each satellite platform SAR ground surface image through InSAR technology.

[0049] Step two: Unify the geographic coding and LOS deformation in the ground surface deformation time series of multiple satellite platforms to the same reference.

[0050] Step three: Select a SAR ground surface image of a satellite platform, and search for the corresponding homonym pixel point of each pixel point in the SAR ground surface image of another satellite platform according to the preset search radius.

[0051] Step four: Fuse the ground surface deformation time series of each pixel point and the ground surface deformation time series of the corresponding searched homonym pixel point of the pixel point through singular value decomposition SVD to obtain a long-time span high-time resolution deformation time series that can reflect the true ground subsidence.

[0052] The detailed steps are as follows:

[0053] 1. Obtain the ground surface deformation of the airport. First, pre-process various SAR data to obtain respective differential interferograms, then use time series InSAR technology to perform deformation time series inversion, and finally obtain the ground surface deformation time series of the airport area.

[0054] 2. Unify the reference. In order to unify the reference of different platforms, first, ensure that the deformation reference points are consistent when solving different data; second, after inverting the deformation of different data, geocode them to a unified geographic coordinate system; finally, the LOS of different data is inconsistent, and the terrain of the airport area is generally flat, so the LOS deformation of different data is projected to the vertical direction. In this way, the unification of the reference is completed.

[0055] 3. Homonym point identification. Since the deformation pixel points solved by different platform data do not completely coincide, the same name points of different data need to be determined before data fusion. The identification strategy is: take the pixel point of the first platform data as the center, set an appropriate search radius to find the homonym pixel point of the other platform corresponding to the point. There are three search situations, the first is that there is no second platform point within the search radius, then the point does not participate in fusion; the second is that there is 1 second platform point within the search radius, then the point is considered as the same name point, and this group of same name points is fused in time sequence; the third is that there are 2 or more second platform points within the search radius, then take the mean value of these points as the same name point to participate in fusion, as shown in Figure 2 .

[0056] 4、SVD fusion deformation time series. The essence of using SVD to fuse time series of different satellite platforms is to transform the fusion of two time series into a matrix equation solving process. For example, let the cumulative deformation of the first platform data in the first scene image be i i =1, 2, 3, …, m , and the cumulative deformation of the second platform data in the first scene image be j j =1, 2, 3, …, n . The cumulative deformation of the fused time series in the first scene image is represented by k m n , and k represent the time difference and deformation rate corresponding to the first scene and the first scene + 1 scene image after fusion, respectively, k k is a known quantity, and is an unknown quantity. The relationship between the rate and the displacement is established as follows:

[0057] (1).

[0058] (2).

[0059] In the formula, are the index of the first data corresponding to the first deformation in the fused time series ST and the index of the second data corresponding to the first deformation in the fused time series ST, respectively. The above equations can be combined to establish the following matrix equation: i j

[0060] (3).

[0061] In the formula, the matrix t is a ( m + 1 n - 2) * ( m + 1 n - 1) order matrix. Since its rank is deficient, it cannot be directly solved, so the generalized inverse matrix is solved by SVD decomposition, and the rate vector v is solved. Finally, the corresponding cumulative deformation is solved according to each time and rate. Thus, the deformation time series fusion of different platform data is completed, such as Figure 4 ​​​​​​​​​​​​​​As shown, the fused time series has a longer time span, more monitoring nodes, and better continuity than a single time series, thus more accurately reflecting the actual spatiotemporal evolution of surface deformation at the airport. Figure 3 As shown, the deformation time series of Sentinel-1 is S, the deformation time series of TerraSAR-X is T, and the fused time series is ST.

[0062] And, as Figure 5 As shown in the figure, the dark blue dots represent the vertical displacement of TerraSAR-X, the red dots represent the vertical displacement of Sentinel-1, and the black circles represent the fused long-term displacement. Fusion of time series not only unifies two time series to the same reference, yielding a long-term deformation sequence containing both time periods, but also densifies monitoring nodes, improving the monitoring time resolution. For example... Figure 6 As shown, within the data overlap segment, the root mean square error (RMSE) of the InSAR and GNSS displacements at the six stations is less than 5 mm at all points except for G2 and G4, where the RMSE exceeds 5 mm (5.16 mm and 6.48 mm respectively). The results demonstrate the high accuracy of InSAR data processing and the fusion of different data sets.

[0063] The effects of this invention are as follows:

[0064] 1. By fusing data from different platforms, the impact of geometric distortion on the accuracy of airport deformation monitoring during single SAR satellite side-looking imaging can be effectively reduced.

[0065] 2. Through geocoding and LOS-to-vertical deformation conversion, InSAR data from different platforms are unified to the same reference datum. Furthermore, by using pixels from one type of satellite data as the search center and setting an appropriate search radius, corresponding pixels from another type of satellite data are found, improving the accuracy of data fusion.

[0066] 3. By using the SVD method and fusing InSAR deformation results from different platforms, a high temporal resolution deformation time series of airports over a long period of time can be obtained, which more accurately reflects the spatiotemporal evolution characteristics of airport deformation.

[0067] This invention provides a land subsidence monitoring system, comprising:

[0068] The deformation time series acquisition module is used to acquire SAR surface images of the airport on multiple satellite platforms and to acquire the surface deformation time series in the SAR surface images of each satellite platform using InSAR technology.

[0069] The reference benchmark unification module is used to unify the geocoding and LOS-oriented deformation in the time series of surface deformation from multiple satellite platforms to the same reference benchmark.

[0070] The same-named pixel point searching module is configured to select a SAR surface image of one satellite platform, and search for a same-named pixel point corresponding to each pixel point in a SAR surface image of another satellite platform according to a preset search radius with each pixel point as a center.

[0071] The fusion module is configured to fuse the surface deformation time sequence of each pixel point and the surface deformation time sequence of the same-named pixel point searched by the same-named pixel point searching module through singular value decomposition (SVD) to obtain a long-time-span high-time-resolution deformation time sequence capable of reflecting real ground subsidence.

[0072] One specific implementation is as follows:

[0073] The application discloses a monitoring method for ground subsidence.

[0074] S1, acquiring airport surface deformation of multiple platforms.

[0075] S2, unifying different platforms of data to a reference datum.

[0076] S3, determining same-named points of different data before data fusion.

[0077] S4, fusing time sequences of different satellite platforms through SVD.

[0078] Finally, the effect of the application can be summarized as follows:

[0079] The application realizes the purpose of fusing different platform SAR data through SVD method and accurately monitoring an airport area. The application solves the problem that a single satellite platform cannot acquire long-time-span high-time-resolution surface deformation of an airport, and obtains more accurate time-space evolution characteristics of airport surface deformation. The method is applicable to other airports covered by multi-source SAR data and has high universality.

[0080] The above-described embodiments only express several implementation manners of the application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent right of the application. It should be noted that, for ordinary skilled persons in the art, without departing from the concept of the application, several modifications and improvements can be made, which all belong to the protection scope of the application. Therefore, the protection scope of the patent right of the application should be subject to the appended claims.

Claims

1. A method of monitoring ground settlement, characterized by, The method comprises the following steps: Obtaining SAR ground surface images of an airport in multiple satellite platforms, and obtaining ground surface deformation time series in the SAR ground surface images of each satellite platform through InSAR technology; Unifying geographic codes and LOS direction deformations in the ground surface deformation time series of the multiple satellite platforms to the same reference benchmark; Selecting a SAR ground surface image of a satellite platform, and searching for corresponding same-name pixel points of each pixel point in the SAR ground surface image of another satellite platform according to a preset search radius and taking each pixel point as a center; wherein the same-name pixel points represent pixel points at the same geographic position; Fusing the ground surface deformation time series of each pixel point and the ground surface deformation time series of the corresponding searched same-name pixel point of the pixel point through singular value decomposition SVD to obtain high-time-resolution deformation time series capable of reflecting real ground subsidence in a long time span; The step of fusing the ground surface deformation time series of each pixel point and the ground surface deformation time series of the corresponding searched same-name pixel point of the pixel point through singular value decomposition SVD comprises the following steps: The accumulated deformation of the same-named pixel point of the first platform in the jth scene image is i The accumulated deformation of the same-named pixel point of the second platform in the jth scene image is S i ( i =1, 2, 3, …, m ) and the accumulated deformation of the same-named pixel point of the second platform in the jth scene image is j T j ( j =1, 2, 3, …, n ); wherein m represents the total number of scenes of each pixel point of the first platform, n represents the total number of scenes of the same-named pixel point of the second platform.​ Will ST k ( k =1, 2, 3, ... m + n ) indicates that the fused time series is at the 1st... k Cumulative deformation of the scene image; t k and v k represent the time difference and the deformation rate corresponding to the image pair of the post-fusion k scene and the k +1 scene image pair Establishing a relationship between a rate and a displacement: wherein, , are an index in the deformation corresponding fusion time sequence ST of the first platform first scene image and an index in the deformation corresponding fusion time sequence ST of the second platform first scene image, respectively; i are an index in the deformation corresponding fusion time sequence ST of the first platform first scene image and an index in the deformation corresponding fusion time sequence ST of the second platform first scene image, respectively; j are an index in the deformation corresponding fusion time sequence ST of the first platform first scene image and an index in the deformation corresponding fusion time sequence ST of the second platform first Establishing a matrix equation according to the relationship between the rate and the displacement: wherein the matrix t is a (2*2+1*1-2)* m + n -2) matrix; m + n -1) order matrix; v denotes a rate vector, S denotes a deformation time series of the first platform, T denotes a deformation time series of the second platform; Using singular value decomposition (SVD) on the matrix equation to obtain the rate vector v ; According to the time of each image and the deformation rate, obtaining the fusion cumulative deformation of the ground surface deformation time series of each pixel point and the ground surface deformation time series of the corresponding searched same-name pixel point of the pixel point.

2. A method of monitoring ground settlement as claimed in claim 1, wherein, The step of obtaining the ground surface deformation time series of the searched same-name pixel point comprises the following steps: If there is one corresponding same-name pixel point in the search radius, the ground surface deformation time series of the point is taken as the search result; If there are two or more same-name pixel points in the search radius, the ground surface deformation time series of all the same-name pixel points are averaged to obtain the search result.

3. The method of monitoring ground settlement according to claim 1, wherein, The step of obtaining the ground surface deformation time series in the SAR ground surface images of each satellite platform through InSAR technology comprises the following steps: Preprocessing the SAR images of the airport ground surface in multiple satellite platforms to obtain differential interferograms; Using InSAR technology to perform deformation time series inversion on the SAR images of each satellite platform to obtain the ground surface deformation time series of the airport region.

4. The method of monitoring ground settlement of claim 1, wherein, The step of unifying the geographic codes and the LOS direction deformations in the ground surface deformation time series of the multiple satellite platforms to the same reference benchmark comprises the following steps: During the deformation time series inversion, the latitude and longitude of the deformation reference point remain unchanged; After the deformation time series inversion is completed, the geographic codes of the deformation time series inversion results of the multiple satellite platforms are unified to the same geographic coordinate system, and the LOS direction deformations of the deformation time series inversion of the multiple satellite platforms are projected to the vertical direction.

5. A system for monitoring ground settlement, characterized in that The method comprises the following steps: A deformation time series acquisition module is configured to obtain SAR ground surface images of an airport in multiple satellite platforms, and obtain ground surface deformation time series in the SAR ground surface images of each satellite platform through InSAR technology; A reference benchmark unification module is configured to unify geographic codes and LOS direction deformations in the ground surface deformation time series of the multiple satellite platforms to the same reference benchmark; The same-named pixel point searching module is configured to select a SAR surface image of one satellite platform, and search for a same-named pixel point corresponding to each pixel point in a SAR surface image of another satellite platform according to a preset search radius with each pixel point as a center; The fusion module is configured to fuse the surface deformation time sequence of each pixel point and the surface deformation time sequence of the same-named pixel point searched for by the pixel point through singular value decomposition (SVD) to obtain a long-time-span high-time-resolution deformation time sequence capable of reflecting real ground subsidence. The fusion of the surface deformation time sequence of each pixel point and the surface deformation time sequence of the same-named pixel point searched for by the pixel point through singular value decomposition (SVD) includes the following steps: the total number of scenes of each pixel point of the first platform is represented by i the accumulated deformation of the scene image of the first platform is S i ( i =1, 2, 3, …, m ), in the second platform, the corresponding homonymous pixel point of each pixel point of the first platform has the accumulated deformation of the scene image of the second platform j T j j =1, 2, 3, …, n ); wherein m the total number of scenes of each pixel point of the first platform is represented by n the total number of scenes of the homonymous pixel point of the second platform is represented by​​ Will ST k ( k =1, 2, 3, ... m + n ) indicates that the fused time series is at the 1st... k Cumulative deformation of the scene image; t k and v k indicates the time difference and deformation rate corresponding to the image pair of the post-fusion k scene and the k +1 scene image pair A relationship between the rate and the displacement is established: wherein, , are an index in the deformation corresponding fusion time sequence ST of the first platform first scene image and an index in the deformation corresponding fusion time sequence ST of the second platform first scene image, respectively; i are an index in the deformation corresponding fusion time sequence ST of the first platform first scene image and an index in the deformation corresponding fusion time sequence ST of the second platform first scene image, respectively; j are an index in the deformation corresponding fusion time sequence ST of the first platform first scene image and an index in the deformation corresponding fusion time sequence ST of the second platform first A matrix equation is established according to the relationship between the rate and the displacement: wherein the matrix t is a (2 x 2) matrix; m n m n is a (2 x 2) matrix; v denotes a rate vector, S denotes a deformation time series of the first platform, T denotes a deformation time series of the second platform;​​​ Using singular value decomposition (SVD) on the matrix equation to obtain the rate vector v ; According to the time and the deformation rate of each image, the fusion cumulative deformation of the surface deformation time sequence of each pixel point and the surface deformation time sequence of the same-named pixel point searched for by the pixel point is obtained.

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