A method, device, medium and equipment for obtaining three-dimensional deformation field data

Abstract

The application relates to the technical field of surface measurement, and particularly provides a method, device, medium and equipment for obtaining three-dimensional deformation field data, which can comprise the following steps: constructing a three-dimensional deformation variable model to be solved based on multi-source data of observation points in any observation window in a target research area; wherein the multi-source data comprises synthetic aperture radar interferometric measurement data, system observation data of a global satellite navigation positioning system and leveling values; obtaining observation residuals of each type of data based on a weight matrix of each type of data in the multi-source data and the three-dimensional deformation variable model; calculating the observation residuals to obtain a variance component estimation value; and outputting three-dimensional deformation component data in the three-dimensional deformation variable model in the case that the variance component estimation value meets a preset condition. The application can efficiently and accurately obtain surface three-dimensional deformation data.

Description

Technical Field

[0001] This application relates to the field of surface measurement technology, and more specifically, to a method, apparatus, medium, and device for acquiring three-dimensional deformation field data. Background Technology

[0002] Surface deformation measurement refers to the observation and calculation process of using geodesy, remote sensing, geophysics and other technologies to quantitatively obtain geometric and physical parameters such as positional changes, displacement, velocity and strain of the Earth's surface in time and space. It is a core technical means for monitoring dynamic changes of the Earth's surface.

[0003] Interferometric Synthetic Aperture Radar (InSAR) is a side-looking remote sensing technique capable of acquiring surface deformation information with high spatial resolution and wide coverage. However, its observations are merely a one-dimensional projection of the radar line of sight, making it difficult to directly separate horizontal and vertical deformation components. Global Navigation Satellite Systems (GNSS) can acquire high temporal resolution and high-precision three-dimensional deformation data, but their spatial resolution is low and their station construction costs are high. Leveling, as a classic geodetic method, can directly reflect high-precision vertical deformation, but it is difficult to achieve large-scale, dense observations. While the above technologies each have their own advantages, they also have significant limitations.

[0004] Therefore, how to provide a more accurate method for acquiring three-dimensional deformation field data has become a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of some embodiments of this application is to provide a method, apparatus, medium and device for acquiring three-dimensional deformation field data. The technical solutions of the embodiments of this application can improve the accuracy and efficiency of acquiring three-dimensional deformation field data of the earth's surface, and have high practicality.

[0006] In a first aspect, some embodiments of this application provide a method for acquiring three-dimensional deformation field data, comprising: constructing a three-dimensional deformation model to be solved based on multi-source data of observation points in any observation window in the target study area; wherein the multi-source data includes synthetic aperture radar interferometry data, system observation data of the global satellite navigation and positioning system, and leveling measurements; obtaining the observation residuals of each type of data based on the weight matrix of each type of data in the multi-source data and the three-dimensional deformation model; calculating the observation residuals to obtain the variance component estimates; and outputting the three-dimensional deformation component data in the three-dimensional deformation model when the variance component estimates meet preset conditions.

[0007] Some embodiments of this application construct a corresponding three-dimensional deformation model from multi-source data of observation points within any observation window. Then, the variance component estimates are determined by combining the observation residuals calculated from the weight matrix of each data type. If the variance component estimates meet preset conditions, the corresponding three-dimensional deformation component data are output. This application's window partitioning method reduces the computational load per iteration. Furthermore, by constructing and solving the three-dimensional deformation model, the accuracy and efficiency of acquiring surface three-dimensional deformation field data are improved, making it highly practical.

[0008] In some embodiments, before constructing the three-dimensional deformation model to be solved based on multi-source data from observation points in any observation window of the target study area, the method further includes: determining the reference coordinates and spatial resolution of the three-dimensional deformation region according to the observation range of synthetic aperture radar interferometry and the distribution of stations and leveling points of the global satellite navigation and positioning system; wherein the reference coordinates and the spatial resolution are used to ensure that the multi-source data are within a unified framework.

[0009] Some embodiments of this application can ensure the consistency of multi-source data acquisition and achieve standardized data processing by constructing a unified reference coordinate and spatial resolution for synthetic aperture radar interferometry, global navigation satellite positioning system and leveling points.

[0010] In some embodiments, the multi-source data is obtained by inserting the system observation data and the leveling measurements into the synthetic aperture radar interferometry data to obtain the multi-source data.

[0011] Some embodiments of this application can effectively fuse multiple data by inserting system observation data and leveling measurements into synthetic aperture radar interferometry data.

[0012] In some embodiments, constructing a three-dimensional deformation model to be solved based on multi-source data from observation points in any observation window of the target study area includes: using the observation value matrix constructed from the multi-source data as the output side, and the product of the data matrix of the three-dimensional deformation component data to be solved and the observation design matrix as the input side; and using the equality relationship between the output side and the input side as the three-dimensional deformation model.

[0013] Some embodiments of this application construct a three-dimensional deformation model using multi-source data, three-dimensional deformation component data, and an observation design matrix, providing a foundation for solving the three-dimensional deformation component data.

[0014] In some embodiments, obtaining the observation residuals for each data class based on the weight matrix of each data class in the multi-source data and the three-dimensional deformation model includes: obtaining the observation values ​​of each data class and the observation design matrix from the three-dimensional deformation model; and obtaining the observation residuals by calculating the observation values, the observation design matrix, and the weight matrix.

[0015] Some embodiments of this application calculate the observation residuals using observation values, observation design matrices, and weight matrices, providing data support for subsequent model solving.

[0016] In some embodiments, calculating the variance component estimate from the observation residuals includes: solving for the ratio of the product of the transpose matrix of the observation residuals, the weight matrix, and the number of observation points to obtain the variance component estimate.

[0017] Some embodiments of this application can provide data support for model solving by solving for variance component estimates.

[0018] In some embodiments, the step of outputting the three-dimensional deformation component data in the three-dimensional deformation model when the variance component estimate meets a preset condition includes: repeatedly performing the following operations until the variance component estimate remains unchanged, and then outputting the three-dimensional deformation component data: updating the weight matrix based on the variance component estimate; updating the observation residual using the weight matrix; updating the variance component estimate using the observation residual; and confirming that the variance component estimate has changed.

[0019] Some embodiments of this application perform cyclic operations on the relationship between variance component estimates, weight matrix, and observation residuals, and output three-dimensional deformation component data after the variance component estimates remain unchanged, thereby achieving accurate acquisition of three-dimensional deformation data.

[0020] Secondly, some embodiments of this application provide an apparatus for acquiring three-dimensional deformation field data, comprising: a model building module for constructing a three-dimensional deformation model to be solved based on multi-source data of observation points in any observation window in the target study area; wherein the multi-source data includes synthetic aperture radar interferometry data, system observation data of the global satellite navigation and positioning system, and leveling measurements; a residual acquisition module for acquiring the observation residuals of each type of data based on the weight matrix of each type of data in the multi-source data and the three-dimensional deformation model; a calculation module for calculating the observation residuals to obtain the variance component estimates; and an output module for outputting the three-dimensional deformation component data in the three-dimensional deformation model when the variance component estimates meet preset conditions.

[0021] Thirdly, some embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the method described in any embodiment of the first aspect.

[0022] Fourthly, some embodiments of this application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, can implement the method as described in any embodiment of the first aspect.

[0023] Fifthly, some embodiments of this application provide a computer program product, the computer program product including a computer program, wherein the computer program, when executed by a processor, can implement the method described in any embodiment of the first aspect. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of some embodiments of this application, the accompanying drawings used in some embodiments of this application will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 System diagrams for acquiring three-dimensional deformation field data provided for some embodiments of this application;

[0026] Figure 2 One of the flowcharts for a method of acquiring three-dimensional deformation field data provided in some embodiments of this application;

[0027] Figure 3 A second flowchart illustrating a method for acquiring three-dimensional deformation field data provided for some embodiments of this application;

[0028] Figure 4 Block diagram of an apparatus for acquiring three-dimensional deformation field data provided for some embodiments of this application;

[0029] Figure 5 A schematic diagram of an electronic device provided for some embodiments of this application. Detailed Implementation

[0030] The technical solutions of some embodiments of this application will now be described with reference to the accompanying drawings.

[0031] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0032] Among related technologies, Synthetic Aperture Radar Interferometry (InSAR) is a side-looking remote sensing technique that can acquire surface deformation information with high spatial resolution and wide coverage. However, its observations are only a one-dimensional projection of the radar line of sight, making it difficult to directly separate horizontal and vertical deformation components. Global Navigation Satellite Systems (GNSS) can acquire high temporal resolution and high precision three-dimensional deformation, but are limited by low spatial resolution and high station construction costs. Leveling, as a classic geodetic method, can directly reflect high-precision vertical deformation; however, it is inefficient, costly, and labor-intensive, making large-scale, intensive observation difficult.

[0033] With the development of surface surveying technology, the fusion of GNSS and InSAR data to calculate the three-dimensional deformation field of the Earth's surface has become a hot topic in geodesy research, and related methods have developed rapidly and achieved a series of results. Early research mainly focused on the construction of the basic framework for fusion of the two types of data. Subsequently, methodological research gradually developed towards practicality and refinement, for example, by simplifying the energy equation to achieve analytical solutions for the three-dimensional deformation rate. Although existing research techniques have fully demonstrated the effectiveness of GNSS and InSAR data fusion in calculating the three-dimensional deformation field, existing methods still have the following limitations: current work mostly focuses on combining GNSS and InSAR spatial geodesy data, while rarely incorporating traditional high-precision leveling observation systems into a unified calculation framework. Leveling data has a recognized reliability advantage in vertical deformation monitoring, and its absence may restrict further improvement in the overall accuracy of the three-dimensional deformation field. In the fusion process, existing methods often struggle to maintain high spatial resolution while also considering computational efficiency. Specifically, the commonly used variance component estimation iterative weighting method has low overall solution efficiency due to the complexity of the iterative process and the large amount of computation; the traditional weighted least squares method usually adopts a globally unified weighting strategy, which makes it difficult to adaptively and finely allocate weights based on the spatial differences in the observation accuracy of different points.

[0034] As can be seen from the above-mentioned technologies, the accuracy and efficiency of surface deformation data measurement in existing technologies need to be improved.

[0035] In view of this, some embodiments of this application provide a method for acquiring three-dimensional deformation field data. This method divides the target study area into multiple observation windows, and by calculating the three-dimensional deformation component data of each observation window, the solution efficiency can be improved. When calculating for any observation window, a three-dimensional deformation model to be solved is constructed using multi-source data from that observation window; then, the variance component estimate is calculated by combining the weight matrix of each type of data in the multi-source data and the observation residuals determined by the three-dimensional deformation model; after the variance component estimate meets preset conditions, the three-dimensional deformation component data can be output. The embodiments of this application can achieve accurate calculation of three-dimensional deformation data with high solution efficiency through multi-source data fusion, and have high practicality.

[0036] The following is in conjunction with the appendix Figure 1 The overall structure of a system for acquiring three-dimensional deformation field data provided by some embodiments of this application is illustrated by way of example.

[0037] like Figure 1 As shown, some embodiments of this application provide a system diagram for acquiring three-dimensional deformation field data. This system for acquiring three-dimensional deformation field data may include a terminal 100 and a server 200. The terminal 100 can send Synthetic Aperture Radar Interferometry (InSAR) data, Global Navigation Satellite System (GNSS) system observation data, and leveling measurements from any observation window to the server 200. The server 200 can construct a three-dimensional deformation model to be solved based on the InSAR data, system observation data, and leveling measurements. Then, the Welch variance component estimation method is used to solve the three-dimensional deformation model to obtain the three-dimensional deformation component data.

[0038] In some embodiments of this application, the terminal 100 can be a mobile terminal or a non-portable computer terminal, and the embodiments of this application are not specifically limited here.

[0039] The following is in conjunction with the appendix Figure 2 The present application provides an exemplary embodiment of the process by which a server 200 acquires three-dimensional deformation field data.

[0040] Please see the appendix Figure 2 , Figure 2 A flowchart illustrating a method for acquiring three-dimensional deformation field data is provided for some embodiments of this application. The method for acquiring three-dimensional deformation field data may include:

[0041] S210, Based on multi-source data from observation points within any observation window in the target study area, construct a three-dimensional deformation model to be solved; wherein, the multi-source data includes synthetic aperture radar interferometry data, global satellite navigation and positioning system observation data, and leveling measurements.

[0042] For example, in a specific embodiment of this application, to address the problem of low solution efficiency caused by the large data volume during joint settlement of multi-source data, an adaptive solution window partitioning strategy is proposed. This involves dividing the target study area for surface deformation into observation windows of different sizes. The specific size of the observation window can be flexibly determined according to the actual application scenario. After partitioning, parallel or serial computation is used to calculate the three-dimensional deformation component data for each observation window. For ease of explanation, this embodiment only uses any one of the observation windows as an example to illustrate the construction of the three-dimensional deformation model and the process of solving the three-dimensional deformation model to obtain the three-dimensional deformation field data.

[0043] Specifically, the multi-source data in this application embodiment includes orbital ascent and descent observation data from InSAR data, east-west, north-south, and vertical data from the system observation data of the Global Navigation Satellite System, as well as leveling measurements.

[0044] In some embodiments of this application, the method for acquiring three-dimensional deformation field data before executing S210 may include: determining the reference coordinates and spatial resolution of the three-dimensional deformation region based on the observation range of synthetic aperture radar interferometry and the distribution of observation stations and leveling points of the global satellite navigation and positioning system; wherein the reference coordinates and the spatial resolution are used to bring the multi-source data under a unified framework.

[0045] For example, in a specific embodiment of this application, InSAR deformation observation data (i.e., InSAR data) from different orbits are unified into the same grid data; then, based on the range of ascending and descending orbit observations, the distribution of GNSS stations (i.e., stations) and leveling points in the InSAR data, the reference coordinates and spatial resolution of the target study area are unified so that multi-source data can be used under a unified reference frame.

[0046] In some embodiments of this application, multi-source data is obtained by inserting the system observation data and the leveling measurement values ​​into the synthetic aperture radar interferometry data to obtain the multi-source data.

[0047] For example, in a specific embodiment of this application, the Kriging method is used to interpolate sparse GNSS data (as a specific example of system observation data) and leveling measurements into pixels within the InSAR data to obtain fused multi-source data. It is understood that, in addition to using the Kriging method and distance-weighted methods, spatial interpolation algorithms with the same function can also be used, and the embodiments of this application are not limited to these.

[0048] In some embodiments of this application, S210 may include: using the observation matrix constructed from the multi-source data as the output side, and the product of the data matrix of the three-dimensional deformation component data to be solved and the observation design matrix as the input side; and using the equality relationship between the output side and the input side as the three-dimensional deformation model.

[0049] For example, in a specific embodiment of this application, when each observation point simultaneously possesses InSAR data, GNSS data in the east-west, north-south, and vertical directions, as well as leveling observations, the constructed three-dimensional deformation mathematical model is as follows:

[0050]

[0051] in, This is the observation matrix, where each parameter represents the GNSS east-west, north-south, and vertical observation data, the vertical leveling measurement, and the observation data in the ascending and descending lines of sight from the InSAR data, respectively. and These are represented as the incident angle and azimuth angle in the SAR image reference frame, respectively. a and d These represent InSAR's ascending and descending orbit deformation observations, respectively. The data represents the three-dimensional deformation components (i.e., the data matrix) to be solved, indicating the components in the east-west, north-south, and vertical directions. The matrix being multiplied is the observation design matrix.

[0052] S220, based on the weight matrix of each type of data in the multi-source data and the three-dimensional deformation model, obtain the observation residuals of each type of data.

[0053] For example, in a specific embodiment of this application, as described above, the multi-source data includes six categories: ascending and descending orbit observation data from InSAR data, east-west, north-south, and vertical data from GNSS data, and leveling measurements. The weight matrix for each category of data is related to the estimated variance components of that category. By combining some parameters from the aforementioned three-dimensional deformation model, the observation residual V for each category of data can be determined. i Where i represents the i-th type of data, i=1,2,3,4,5,6, representing InSAR ascending line-of-sight observations, InSAR descending line-of-sight observations, GNSS east-west observations, GNSS north-south observations, GNSS vertical observations, and leveling observations, respectively.

[0054] In some embodiments of this application, S220 may include: obtaining the observation values ​​of each type of data and the observation design matrix from the three-dimensional deformation model; and obtaining the observation residuals by calculating the observation values, the observation design matrix, and the weight matrix.

[0055] For example, in a specific embodiment of this application, the formula for calculating the observation residual is as follows:

[0056]

[0057] In the formula, B i This indicates the value of each type of data in the observation design matrix, which can be read directly; l i Represents the observed values ​​for each data category; P i Let represent the weight matrix for each data class. We can set where ... N i For each class of observations (i.e., each class of data), the normal equation matrix is... N The normal equation matrix for each type of observation data N i The sum, that is The value of i can be 1, 2, 3, 4, 5, or 6. N = N 1+ N 2+ N 3+ N 4+ N 5+ N 6.

[0058] S230, calculate the observed residuals to obtain the estimated values ​​of the variance components.

[0059] Specifically, S230 may include: solving the ratio of the transpose matrix of the observation residuals, the weight matrix, the product of the observation residuals and the number of observation points to obtain the variance component estimate.

[0060] For example, in a specific embodiment of this application, the variance component estimate of each type of data... The calculation formula is as follows:

[0061]

[0062] in, The transpose matrix of the observation residuals, The number of observations for each data class is given by tr(), and tr() is the trace of the matrix (i.e., the sum of the diagonal elements).

[0063] S240, if the variance component estimate meets the preset conditions, output the three-dimensional deformation component data in the three-dimensional deformation model.

[0064] For example, in a specific embodiment of this application, the preset condition is that during the solution of the three-dimensional deformation model, the variance component estimates tend to a stable state, that is, when the variance component estimates remain unchanged in two consecutive iterations during the solution process, the three-dimensional deformation component data in the three-dimensional deformation model at this time is taken as the final result. Alternatively, if the variance component estimates of each type of data are equal or approximately equal during the iterative calculation process, the three-dimensional deformation component data in the three-dimensional deformation model at this time is taken as the final result. It should be understood that the preset condition can be flexibly adjusted according to the actual application scenario to serve as the iterative termination condition for model solution, and the embodiments of this application are not limited to this.

[0065] Specifically, S240 may include: repeatedly performing the following operations until the variance component estimate remains unchanged, then outputting the three-dimensional deformation component data: updating the weight matrix based on the variance component estimate; updating the observation residual using the weight matrix; updating the variance component estimate using the observation residual; and confirming that the variance component estimate has changed.

[0066] For example, in a specific embodiment of this application, during the first loop, the prior weights of each type of data (as the weight matrix for the first loop) are known, i.e. (1) will Substituting these values ​​into the formula for solving the observation residuals, we obtain the observation residuals. V i (2) Use and V i Substitute the values ​​into the formula for the variance component estimates to obtain the current variance component estimates; (3) Use the variance component estimates to readjust the weight matrix. Where C is a constant, usually taken as... A value in. Repeat (1) to (3) until the current loop and the previous loop are both executed. When they are equal or approximately equal, output three-dimensional deformation component data. This result serves as the result of the three-dimensional deformation analysis for this observation window.

[0067] Understandably, in practical applications, after calculating the three-dimensional deformation component data for one observation window, the calculation of the three-dimensional deformation component data for the next observation window is performed. When all observation points within the target study area are used, the three-dimensional deformation field of the target study area is obtained.

[0068] The following is in conjunction with the appendix Figure 3 The present application provides an exemplary description of the specific process for obtaining three-dimensional deformation field data through some embodiments.

[0069] Please see the appendix Figure 3 , Figure 3 A flowchart illustrating a method for acquiring three-dimensional deformation field data is provided for some embodiments of this application.

[0070] The above process is illustrated below using any one of the multiple observation windows in the target study area as an example.

[0071] S310: Based on multi-source data from observation points in any observation window, construct a three-dimensional deformation model to be solved.

[0072] S320: Obtain the initial values ​​of the weight matrix and variance components for each class of data in the multi-source data.

[0073] In the initial iteration, the weight matrix consists of prior weights. These prior weights can be used to obtain the initial values ​​of the variance components.

[0074] S330 calculates the observation residuals for each type of data based on prior weights.

[0075] S340, solve for the transpose matrix, weight matrix, and ratio of the product of the observation residuals to the number of observation points to obtain the variance component estimate.

[0076] S350: Determine whether the initial value of the variance component and the estimated value of the variance component are equal. If they are equal, proceed to S370; otherwise, proceed to S360.

[0077] S360, update the weight matrix based on the variance component estimates, and use the current variance component estimates as the initial values ​​of the variance components, then return to S320.

[0078] S370 outputs the three-dimensional deformation component data in the three-dimensional deformation model.

[0079] It is understood that the specific implementation process of S310~S370 can be referred to the method embodiment provided above. To avoid repetition, detailed descriptions are omitted here.

[0080] As can be seen from the embodiments of this application above, in response to the efficiency degradation caused by the excessively large coefficient matrix size in the joint solution of multi-source data, this application proposes an adaptive solution window partitioning strategy. By decomposing the large-scale overall solution into parallel or serial calculations of multiple local windows, the computational and storage difficulties caused by the increase in matrix dimension are effectively avoided, thus improving the feasibility and timeliness of the overall fusion process from the algorithm structure perspective. By introducing the Welch variance component estimation method, which directly calculates the variance components based on the residuals of each source data without the need for a complex iterative convergence process, the calculation process for determining weights is greatly simplified. While ensuring the accuracy of weight estimation, the solution efficiency of the weight determination stage is significantly improved. In other words, this application systematically improves the efficiency of multi-source deformable data fusion through both improved weight determination algorithms and optimized solution structures, providing a more efficient and reliable technical means for processing large-scale, high-dimensional deformable data.

[0081] Please refer to Figure 4 , Figure 4 The diagram illustrates a block diagram of an apparatus for acquiring three-dimensional deformation field data according to some embodiments of this application. It should be understood that this apparatus for acquiring three-dimensional deformation field data corresponds to the method embodiments described above and is capable of performing the various steps involved in the method embodiments. The specific functions of this apparatus for acquiring three-dimensional deformation field data can be found in the description above; detailed descriptions are omitted here to avoid repetition.

[0082] Figure 4 The device for acquiring three-dimensional deformation field data includes at least one software functional module that can be stored in a memory or embedded in the device in the form of software or firmware. The device for acquiring three-dimensional deformation field data includes: a model building module 410, used to construct a three-dimensional deformation model to be solved based on multi-source data of observation points in any observation window in the target study area; wherein, the multi-source data includes synthetic aperture radar interferometry data, global satellite navigation and positioning system observation data, and leveling measurement values; a residual acquisition module 420, used to acquire the observation residuals of each type of data based on the weight matrix of each type of data in the multi-source data and the three-dimensional deformation model; a calculation module 430, used to calculate the observation residuals to obtain the variance component estimates; and an output module 440, used to output the three-dimensional deformation component data in the three-dimensional deformation model when the variance component estimates meet preset conditions.

[0083] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the aforementioned method, and will not be elaborated further here.

[0084] Some embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can perform the operation of any of the methods corresponding to the methods provided in the above embodiments.

[0085] Some embodiments of this application also provide a computer program product, which includes a computer program, wherein when the computer program is executed by a processor, it can implement the operation of any of the methods corresponding to the above embodiments provided in the above embodiments.

[0086] like Figure 5 As shown, some embodiments of this application provide an electronic device 500, which includes a memory 510, a processor 520, and a computer program stored in the memory 510 and executable on the processor 520. When the processor 520 reads the program from the memory 510 via a bus 530 and executes the program, it can implement the methods of any of the above embodiments.

[0087] Processor 520 can process digital signals and can include various computing architectures. For example, it can be a complex instruction set computer architecture, a reduced instruction set computer architecture, or an architecture that implements multiple instruction set combinations. In some examples, processor 520 can be a microprocessor.

[0088] The memory 510 can be used to store instructions executed by the processor 520 or data related to the execution of instructions. These instructions and / or data may include code for implementing some or all of the functions of one or more modules described in the embodiments of this application. The processor 520 of this disclosure embodiment can be used to execute the instructions in the memory 510 to implement the methods shown above. The memory 510 includes dynamic random access memory, static random access memory, flash memory, optical memory, or other memories well known to those skilled in the art.

[0089] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0090] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for acquiring three-dimensional deformation field data, characterized in that, include: Based on multi-source data from observation points within any observation window in the target study area, a three-dimensional deformation model to be solved is constructed; wherein, the multi-source data includes synthetic aperture radar interferometry data, global satellite navigation and positioning system observation data, and leveling measurements; Based on the weight matrix of each type of data in the multi-source data and the three-dimensional deformation model, the observation residuals of each type of data are obtained; The variance component estimates are calculated from the observed residuals; wherein the variance component estimates are calculated using the Welch variance estimation method. If the variance component estimates meet the preset conditions, the three-dimensional deformation component data in the three-dimensional deformation model are output. The three-dimensional deformation model is: in, This is the observation matrix, where each parameter represents the GNSS east-west, north-south, and vertical observation data, the vertical leveling measurement, and the observation data in the ascending and descending lines of sight from the InSAR data, respectively. and These are represented as the incident angle and azimuth angle in the SAR image reference frame, respectively. a and d These represent InSAR's ascending and descending orbit deformation observations, respectively. The data is the three-dimensional deformation component data (i.e., the data matrix) to be solved, which represents the components in the east-west, north-south and vertical directions. The method for calculating the Welch variance component estimates is as follows: in, These are estimates of the variance components. The transpose matrix of the observation residuals, The number of observations for each data class is given by tr(), and tr() is the trace of the matrix. The target study area is divided into multiple observation windows, and model construction, observation residual acquisition, variance component estimation, and three-dimensional deformation component data output are performed in parallel or serially for each observation window.

2. The method as described in claim 1, characterized in that, Before constructing the three-dimensional deformation model to be solved based on multi-source data from observation points within any observation window in the target study area, the method further includes: Based on the observation range of synthetic aperture radar interferometry and the distribution of stations and leveling points of the global satellite navigation and positioning system, the reference coordinates and spatial resolution of the three-dimensional deformation region are determined; wherein, the reference coordinates and spatial resolution are used to ensure that the multi-source data are within a unified framework.

3. The method as described in claim 1 or 2, characterized in that, The multi-source data was obtained through the following steps: The system observation data and the leveling measurement values ​​are inserted into the synthetic aperture radar interferometry data to obtain the multi-source data.

4. The method as described in claim 1 or 2, characterized in that, The construction of a three-dimensional deformation model to be solved based on multi-source data from observation points within any observation window in the target study area includes: The observation matrix constructed from the multi-source data is used as the output, and the product of the data matrix of the three-dimensional deformation component data to be solved and the observation design matrix is ​​used as the input. The equality relationship between the output side and the input side is used as the three-dimensional deformation model.

5. The method as described in claim 4, characterized in that, The step of obtaining the observation residuals for each data class based on the weight matrix of each data class in the multi-source data and the three-dimensional deformation model includes: The observation values ​​for each type of data and the observation design matrix are obtained from the three-dimensional deformation model; The observation residuals are obtained by calculating the observed values, the observation design matrix, and the weight matrix.

6. The method as described in claim 4, characterized in that, The calculation of the observed residuals to obtain the variance component estimate includes: The variance component estimate is obtained by solving the transpose matrix of the observation residuals, the weight matrix, and the ratio of the product of the observation residuals to the number of observation points.

7. The method as described in claim 1 or 2, characterized in that, When the variance component estimate meets the preset conditions, the step of outputting the three-dimensional deformation component data in the three-dimensional deformation model includes: The following operation is performed repeatedly until the variance component estimate remains constant, at which point the three-dimensional deformation component data is output: The weight matrix is ​​updated based on the variance component estimates; The observation residuals are updated using the weight matrix; The variance component estimates are updated using the observed residuals; It was confirmed that the estimated variance components had changed.

8. A device for acquiring three-dimensional deformation field data, characterized in that, include: The model building module is used to construct a three-dimensional deformation model to be solved based on multi-source data from observation points in any observation window in the target study area; wherein, the multi-source data includes synthetic aperture radar interferometry data, global satellite navigation and positioning system observation data, and leveling measurements; The residual acquisition module is used to acquire the observation residuals of each type of data based on the weight matrix of each type of data in the multi-source data and the three-dimensional deformation model; The calculation module is used to calculate the observed residuals to obtain the variance component estimates; wherein the variance component estimates are calculated by the Welch variance estimation method. The output module is used to output the three-dimensional deformation component data in the three-dimensional deformation model when the variance component estimate meets the preset conditions. The three-dimensional deformation model is: in, This is the observation matrix, where each parameter represents the GNSS east-west, north-south, and vertical observation data, the vertical leveling measurement, and the observation data in the ascending and descending lines of sight from the InSAR data, respectively. and These are represented as the incident angle and azimuth angle in the SAR image reference frame, respectively. a and d These represent InSAR's ascending and descending orbit deformation observations, respectively. The data is the three-dimensional deformation component data (i.e., the data matrix) to be solved, which represents the components in the east-west, north-south and vertical directions. The method for calculating the variance component estimate is as follows: in, These are estimates of the variance components. The transpose matrix of the observation residuals, The number of observations for each data class is given by tr(), and tr() is the trace of the matrix. The target study area is divided into multiple observation windows, and model construction, observation residual acquisition, variance component estimation, and three-dimensional deformation component data output are performed in parallel or serially for each observation window.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is executed by a processor to perform the method as described in any one of claims 1-7.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the computer program is executed by the processor to perform the method as described in any one of claims 1-7.

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