Dual-band InSar and GNSS fused high-precision three-dimensional deformation adaptive monitoring method

Abstract

The invention discloses a dual-band InSar and GNSS fused high-precision three-dimensional deformation adaptive monitoring method, and the method comprises the following steps: constructing a dual-band observation equation, and decomposing the dual-band observation equation to obtain a dual-band InSAR resolving residual error; calculating the comprehensive weight of each GNSS observation point through an entropy weight method, selecting a plurality of GNSS observation points with high comprehensive weights as an optimal GNSS reference point combination, and converting the three-dimensional deformation observed by the optimal GNSS reference points into an LOS direction; expanding a two-dimensional radial basis function to a three-dimensional radial basis function, optimizing the number and distribution of the three-dimensional radial basis function according to the residual drop rate in the expansion process, and performing basis function encryption on the key area by combining the LOS direction result of the reference point; an original two-dimensional state equation in a Kalman filtering algorithm is expanded to be three-dimensional, meanwhile, a GNSS three-dimensional observation value serves as a strong constraint condition of the Kalman filtering algorithm, and a final three-dimensional deformation rate field is output through iteration and 3D-Va analysis.

Description

Technical Field

[0001] This invention relates to the field of deformation monitoring technology, and in particular to a high-precision three-dimensional adaptive deformation monitoring method that integrates dual-band InSar and GNSS. Background Technology

[0002] With the accelerated progress of infrastructure projects, the combined effects of human activities and natural factors have led to an increased risk of geological disasters. Structural deformation monitoring has become crucial for ensuring project safety—even minor deformations can accumulate and trigger major accidents. Due to its significant elevation differences, the Yunnan-Guizhou Plateau region of my country has seen the construction of numerous large hydropower stations in recent years. In this complex geological environment, accurately acquiring three-dimensional deformation data of the dam body and identifying potential landslide hazards is of great significance for ensuring the safe operation of hydropower facilities and establishing an intelligent early warning system.

[0003] In monitoring slope deformation of reservoirs in high-altitude mountainous areas, traditional ground-based monitoring methods (such as total stations and levels) suffer from low efficiency, poor real-time performance, and safety hazards in severe weather. While Synthetic Aperture Radar Interferometry (InSAR) technology offers wide-area coverage in all weather conditions, it is limited by atmospheric errors and line-of-sight deformation, making it difficult to reflect true three-dimensional deformation. Furthermore, its reliance on fixed satellite revisit periods fails to meet the arbitrary time interval requirements of geological disaster emergency monitoring. Global Navigation Satellite System (GNSS) can acquire high-precision three-dimensional coordinates, but its sparse deployment of monitoring points in complex terrain can easily miss potential hazards. Current technological trends emphasize multi-source fusion: by combining InSAR and GNSS monitoring, integrating intelligent algorithms to suppress interference and reconstruct the three-dimensional deformation field, and supplementing this with multi-sensor data integration and analysis, the limitations of single technologies can be overcome, enabling all-weather dynamic early warning for high-risk areas and providing precise support for engineering safety in complex geological environments.

[0004] In summary, conventional single GNSS or InSAR deformation monitoring technologies are insufficient to meet the actual needs of reservoir dam and slope deformation monitoring in complex environments. It is essential to study a combined InSAR and GNSS measurement method that integrates satellite and ground systems. However, how to unify the spatiotemporal reference of InSAR and GNSS data, how to improve accuracy in complex environments, and how to obtain the three-dimensional deformation required for observation at any time interval are still problems that urgently need to be solved. Summary of the Invention

[0005] In view of the above-mentioned prior art, the present invention provides a high-precision three-dimensional deformation adaptive monitoring method that integrates dual-band InSar and GNSS, which mainly solves the technical problems existing in the background art.

[0006] To achieve the above objectives, the technical solution of this invention is implemented as follows: a high-precision three-dimensional deformation adaptive monitoring method integrating dual-band InSar and GNSS, the method comprising the following steps: Based on radar geometric parameters and the LOS deformation time series observed by dual-band InSAR, a dual-band observation equation is constructed, and the dual-band InSAR solution residual is obtained after decomposing the dual-band observation equation. The comprehensive weight of each GNSS observation point is calculated by the entropy weight method. Several GNSS observation points with high comprehensive weights are selected as the optimal combination of GNSS reference points, and the three-dimensional deformation observed by the optimal GNSS reference points is converted to the LOS direction. The two-dimensional radial basis functions are extended to three-dimensional radial basis functions. During the extension process, the number and distribution of the three-dimensional radial basis functions are optimized according to the residual descent rate. At the same time, the basis functions in the key areas are densified by combining the LOS direction results of the reference points. The original two-dimensional state equation in the Kalman filter algorithm is extended to three dimensions, and a rate coupling term is added. At the same time, GNSS three-dimensional observations are used as strong constraints for the Kalman filter algorithm. After iteration and 3D-Va analysis, the final three-dimensional deformation rate field is output.

[0007] Optionally, the LOS-direction deformation time series of InSAR in C-band and L-band are obtained, and a geometric projection equation is established based on the incident angle, azimuth angle and LOS-direction deformation time series of InSAR in C-band and L-band in radar geometric parameters. Ionospheric delay compensation is introduced into the geometric projection equation to obtain the final dual-band observation equation.

[0008] Optionally, singular value decomposition is performed on the dual-band observation equation to remove singular values ​​caused by noise, retain the eigenvalues ​​and corresponding eigenvectors that characterize the main deformation trends, and obtain the initial three-dimensional deformation rate fields in the east, north, and vertical directions and the dual-band InSAR solution residuals.

[0009] Optionally, based on site stability, real-time data loss rate, and spatiotemporal distribution uniformity entropy, the comprehensive weight of each GNSS observation point can be calculated using the entropy weight method.

[0010] Optionally, after obtaining the optimal combination of GNSS reference points, the GNSS three-dimensional observations of the optimal GNSS reference points are converted to the LOS direction, and the elevation change error is compensated during the conversion process.

[0011] Optionally, after converting the GNSS three-dimensional observations of the optimal GNSS reference point to the LOS direction, the standard deviations of the GNSS reference point in the east, north, and vertical directions are calculated, and the fusion weights in the east, north, and vertical directions are calculated based on the standard deviations of the GNSS reference point in the east, north, and vertical directions and the dual-band InSAR solution residuals.

[0012] Optionally, a state three-dimensional deformation rate field is generated based on the constructed three-dimensional radial basis function. The state three-dimensional deformation rate field is transformed to the LOS direction through the geometric projection equation to obtain a simulated LOS-direction deformation time series. The residual between the simulated LOS-direction deformation time series and the LOS-direction deformation time series actually observed by dual-band InSAR is calculated, and the root mean square (RMS) statistics are performed on the residuals of all spatial pixels and time points to obtain the root mean square error.

[0013] Optionally, the residual reduction rate is calculated based on the root mean square error of two adjacent measurements. If the residual reduction rate is greater than a preset threshold, the number of three-dimensional radial basis functions is increased, and the spatial position of the three-dimensional radial basis functions is readjusted according to the spatial distribution of the residuals.

[0014] Optionally, the coupling coefficients of the eastward, northward, and vertical deformation rates are determined based on the spatial correlation between the three-dimensional radial basis functions, forming rate coupling terms. The resulting three-dimensional state equation serves as the mathematical model for the Kalman filter prediction stage.

[0015] Optionally, in the state iterative update stage of Kalman filter prediction, each iteration first predicts the state through the three-dimensional state equation, then calculates the Kalman gain by combining the GNSS three-dimensional observations of the optimal GNSS reference point and the fusion weight, corrects the predicted state using the Kalman gain, and then introduces 3D-Var analysis to optimize the corrected state, reduce the state estimation error, and repeats the iteration until the state estimation converges, outputting the final three-dimensional deformation rate field.

[0016] The beneficial effects of this invention are as follows: 1. It directly calculates the initial value of the three-dimensional deformation rate using the geometric projection relationship of dual-band InSAR data, breaking through the dimensional limitations of traditional single LOS-to-conversion; 2. It achieves the unification of the spatiotemporal reference of multi-source heterogeneous three-dimensional data and the self-optimization of fusion accuracy by dynamically selecting GNSS reference points and using a direction-sensitive adaptive weighting strategy through the entropy weight method; 3. It dynamically constructs three-dimensional spatial basis functions based on the residual descent rate, adaptively optimizes the spatial resolution, and eliminates the dependence on external prior information; 4. It innovatively integrates GNSS three-dimensional observation as a strong constraint into the extended temporal state equation, and combines three-dimensional EM iteration and 3D-Var analysis to finally output a high-quality three-dimensional deformation rate field with high resolution, spatial continuity, temporal flexibility, and multi-source consistency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the three-dimensional deformation adaptive monitoring method in the embodiments of this application; Figure 2 This refers to L-band InSAR images used in the embodiments of this application; Figure 3 This refers to C-band InSAR images used in the embodiments of this application. Figure 4 This is a schematic diagram of InSAR three-dimensional deformation decomposition in an embodiment of this application. Detailed Implementation

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0019] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0020] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0021] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0022] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0023] Please refer to the attached document. Figure 1 This application provides a high-precision three-dimensional deformation adaptive monitoring method that integrates dual-band InSar and GNSS, the method comprising the following steps: S1. Based on radar geometric parameters and the LOS deformation time series observed by dual-band InSAR, a dual-band observation equation is constructed, and the dual-band InSAR solution residual is obtained after decomposing the dual-band observation equation.

[0024] In one or more embodiments of this application, the time series of LOS-direction deformation of InSAR in C-band and L-band are obtained, based on the incident angle in the radar geometric parameters. Azimuth Furthermore, a geometric projection equation is established for the LOS-direction deformation time series of InSAR in the C-band and L-band. This equation can be expressed as:

[0025] In the formula: The deformation rate in the LOS direction of the C-band. The deformation rate in the L-band LOS direction is... , , These represent the deformation rate fields in the east, north, and vertical directions, respectively. For a time series, e(t) is the error compensation term.

[0026] Since dual-band InSAR observations are susceptible to ionospheric delay, leading to a decrease in the stability of the projection equation, a topographic phase correction term is introduced into the geometric projection equation to compensate for ionospheric delay. The correction model can be expressed as:

[0027] In the formula, For the ionospheric delayed phase correction model, These are the weighting coefficients. For terrain elevation, The ionospheric sensitivity coefficient, The total electron content, For radar incident angle, This is the phase distortion function related to terrain elevation.

[0028] By substituting the topographic phase correction term into the geometric projection equation, the dual-band observation equation is obtained.

[0029] Furthermore, singular value decomposition is performed on the dual-band observation equation to remove singular values ​​caused by noise, retain the eigenvalues ​​and corresponding eigenvectors that characterize the main deformation trends, and obtain the initial three-dimensional deformation rate fields in the east, north, and vertical directions, as well as the dual-band InSAR solution residuals.

[0030] Specifically, the overdetermined equation set—the dual-band observation equations—is solved using a singular value decomposition (SVD) algorithm to obtain the corresponding singular values, left eigenvectors, and right eigenvectors. The magnitude of the singular values ​​reflects the deformation signal intensity represented by the corresponding eigenvectors. Singular values ​​are filtered according to a preset noise threshold, eliminating those smaller than the threshold (primarily caused by noise), and retaining only those greater than or equal to the threshold (i.e., eigenvalues ​​representing the main deformation trend) and their corresponding eigenvectors. The solution results are reconstructed using the retained eigenvalues ​​and eigenvectors, and the eastward-oriented eigenvectors are directly output. North Vertical The initial three-dimensional deformation rate field and the dual-band InSAR solution residuals.

[0031] By constructing dual-band observation equations using combined C-band and L-band InSAR data, and leveraging the difference in ionospheric sensitivity between the two bands, along with spatial projection relationships established using radar geometric parameters such as incident angle and azimuth angle, the three-dimensional deformation rate fields in the east, north, and vertical directions can be directly calculated. This overcomes the limitation of traditional single LOS-direction conversion, which struggles to reflect true three-dimensional deformation. Furthermore, the introduction of a terrain phase correction term to compensate for ionospheric delay effectively eliminates the interference of dual-band ionospheric delay on the observations, improving the stability of the equations. Solving the overdetermined equations through singular value decomposition avoids the ambiguity caused by insufficient geometric constraints in traditional methods, laying the foundation for obtaining a high-precision initial three-dimensional deformation rate field.

[0032] S2. Calculate the comprehensive weight of each GNSS observation point using the entropy weight method, select several GNSS observation points with high comprehensive weights as the optimal GNSS reference point combination, and convert the three-dimensional deformation observed by the optimal GNSS reference point to the LOS direction. In one or more embodiments of this application, based on site stability, real-time data loss rate, and spatiotemporal distribution uniformity entropy, the comprehensive weight of each GNSS observation point is calculated using the entropy weight method. The formula for calculating the comprehensive weight is as follows:

[0033] In the formula, For site stability, For real-time data missing rate, The entropy represents the spatiotemporal distribution uniformity. Station stability is quantified by the degree of fluctuation of observations within a sliding time window. The data missing rate is the ratio of effective observation time to total observation time. The spatiotemporal distribution uniformity is evaluated by the coefficient of variation of the distance between stations. Based on the entropy values ​​of these three indicators, their respective weights are calculated, and finally, the comprehensive weight of each GNSS observation point is obtained. Several GNSS observation points with high comprehensive weights are selected to form the optimal reference point combination. The combination is required to contain at least 3 stations to meet the requirements for establishing a spatial reference.

[0034] Furthermore, after obtaining the optimal combination of GNSS reference points, the GNSS three-dimensional observations of the optimal GNSS reference points are converted to the LOS direction, and the elevation change error is compensated during the conversion process.

[0035] Specifically, a nonlinear projection model is constructed based on terrain gradient constraints to transform the three-dimensional observations (eastward, northward, and vertical deformation) of the optimal GNSS reference point to the LOS direction of InSAR. This model introduces a terrain gradient-corrected projection coefficient, expressed as follows:

[0036] In the formula: These are GNSS observations. Terrain gradient Corrected projection coefficients As a time decay factor, during the conversion process, the terrain gradient information is first calculated based on the terrain elevation data of the area where the optimal GNSS reference point is located. Then, the projection coefficient is corrected using this information so that the projection coefficient can adapt to different terrain features. Especially in areas with drastic elevation changes, the coefficient can be dynamically adjusted to offset the impact of terrain changes on conversion accuracy, breaking through the limitations of traditional linear conversion in complex terrain.

[0037] In one or more embodiments of this application, after converting the GNSS three-dimensional observations of the optimal GNSS reference point to the LOS direction, the standard deviations of the GNSS reference point in the east, north, and vertical directions are calculated, and the fusion weights in the east, north, and vertical directions are calculated based on the standard deviations of the GNSS reference point in the east, north, and vertical directions and the dual-band InSAR solution residuals.

[0038] Specifically, when calculating the standard deviation of the GNSS reference point in the east, north, and vertical directions, this standard deviation is obtained by taking the square root of the average of the sum of squares of the deviations of the observations in each direction from the mean within the sliding time window. Simultaneously, the east, north, and vertical residuals obtained during the dual-band InSAR solution process are extracted, and the fusion weights for the three directions are calculated using a direction-sensitive weighting formula.

[0039] In the formula, The determination of residuals in dual-band InSAR solutions characterizes the accuracy of InSAR data in each direction. The smaller the residual, the higher the accuracy, thus avoiding the influence of low-precision vertical data on horizontal results. E represents the standard deviation of GNSS data in three directions. It reflects the degree of dispersion of GNSS data in different directions. The smaller the standard deviation, the more stable the data in that direction. E represents east, N represents north, and U represents vertical.

[0040] Its fusion weights are applied in the subsequent fusion of GNSS and InSAR data. Secondly, when the vertical weight is lower than the horizontal threshold, a feedback mechanism is triggered to reselect the GNSS reference point.

[0041] S3. Extend the two-dimensional radial basis function to a three-dimensional radial basis function, and optimize the number and distribution of the three-dimensional radial basis function according to the residual descent rate during the extension process. At the same time, combine the LOS direction results of the reference point to densify the basis function in the key area. In one or more embodiments of this application, a state three-dimensional deformation rate field is generated based on the constructed three-dimensional radial basis function. The state three-dimensional deformation rate field is transformed to the LOS direction through the geometric projection equation to obtain a simulated LOS-direction deformation time series. The residual between the simulated LOS-direction deformation time series and the LOS-direction deformation time series actually observed by dual-band InSAR is calculated. The root mean square (RMS) statistics are performed on the residuals of all spatial pixels and time points to obtain the root mean square error.

[0042] Specifically, the radial basis functions Extending to three-dimensional space, it can simultaneously characterize the deformation attenuation characteristics in the eastward, northward, and vertical directions, yielding a three-dimensional radial basis function, the expression of which is:

[0043]

[0044] In the formula: For the spatial location of a point, For time variables, and These are the spatial and temporal center points of the basis functions, respectively. and Control the decay rate in both spatial and temporal dimensions.

[0045] The three-dimensional basis function is characterized by the spatial center point. The core is the local deformation attenuation characteristic. Calculate the three-dimensional spatial distance including elevation differences. Control the radius of influence of deformation. By regulating the duration of the deformation trend, after adaptive optimization, the set of basis functions generates a three-dimensional deformation rate field of the state through linear combination.

[0046] The generated state-based three-dimensional deformation rate field is transformed to the LOS direction using the aforementioned geometric projection equation, thereby obtaining a simulated LOS-direction deformation time series. This simulated LOS-direction deformation time series is then compared with the actual observed deformation time series. , The residuals are calculated by comparing each phase and each time point, and finally the root mean square error of the i-th iteration is obtained by performing global root mean square statistics on all residuals.

[0047] Similarly, the root mean square error (RMSE) of the j-th iteration is calculated. It should be noted that the RMSEs of the i-th and j-th iterations are adjacent. Based on the RMSEs of two adjacent iterations, the residual reduction rate is calculated using the following formula:

[0048] In the formula, This represents the root mean square error of the i-th iteration. This represents the root mean square error of the j-th iteration. Let represent the number of basis functions in the i-th iteration. This represents the number of basis functions in the j-th iteration. The initial value field... As the starting point of the iteration, compare the residual with that of subsequent iterations. If This increases the number of three-dimensional radial basis functions and increases the density of basis functions.

[0049] Furthermore, when increasing the density of basis functions, the three-dimensional deformation gradient field of the GNSS station is calculated as the basis for determining the density region of the basis functions, replacing the traditional manual experience selection.

[0050] Specifically, the GNSS 3D observations from the optimal GNSS reference point are converted to the LOS direction, and the 3D deformation gradient field is calculated. When the 3D deformation gradient value of a certain region exceeds a set threshold, the region is determined to be a region of severe or complex deformation, and the number of 3D radial basis functions needs to be increased to improve the ability to represent subtle deformations. Conversely, in regions with small and gentle deformation gradients, the number of basis functions is kept small. For example, when the elevation gradient of a certain region is greater than the threshold, the region is marked as a densified region, and the basis function density is increased within the coordinate range. The spatial position of the 3D radial basis functions is readjusted according to the spatial distribution of the residuals.

[0051] This implementation method guides the selection of basis function densification regions through the three-dimensional deformation gradient field of GNSS stations, replacing the traditional method that relies on manual experience. This makes the spatial distribution of basis functions more closely match the actual deformation characteristics, especially in areas with complex deformation, thereby improving the fitting accuracy of the three-dimensional spatial basis to the deformation field. At the same time, it avoids unnecessary basis function redundancy and optimizes computational efficiency while ensuring monitoring accuracy.

[0052] S4. Extend the original two-dimensional state equation in the Kalman filter algorithm to three dimensions and add a rate coupling term. At the same time, use the GNSS three-dimensional observations as strong constraints for the Kalman filter algorithm. After iteration and 3D-Va analysis, the final three-dimensional deformation rate field is output.

[0053] In one or more embodiments of this application, the coupling coefficients of the eastward, northward, and vertical deformation rates are determined through the spatial correlation between basis functions, forming rate coupling terms. This extends the traditional two-dimensional state equation to three-dimensional space, establishing a model that includes eastward, northward, and vertical coupling relationships. The model expression is as follows:

[0054] In the formula, As a rate coupling term, it integrates the GNSS deformation rate time series. express The three-dimensional state vector at time t. express The three-dimensional state vector at time t. Represents the state transition matrix. This indicates process noise.

[0055] Furthermore, in the state iteration update phase, each iteration first predicts the state using the three-dimensional state equation, then calculates the Kalman gain by combining the GNSS three-dimensional observations from the optimal GNSS reference point and the fusion weights. The predicted state is then corrected using the Kalman gain. During this process, the GNSS three-dimensional observations... As a strong constraint condition for Kalman filtering, the state variables are corrected during the forward filtering stage of the FRF. :

[0056] In the formula: This represents the three-dimensional state vector to be corrected. This is the 3D spatiotemporal basis mapping matrix corresponding to the GNSS station. This matrix maps the 3D state vector to the GNSS station location (spatial index p=(x,y,z)), ensuring accurate 3D spatial matching. The Kalman gain is adjusted by the fusion weights of the three directions calculated using the aforementioned direction-sensitive weighting formula.

[0057] In the Kalman filter algorithm, the time-varying state variables and small-scale subtle variations are jointly estimated through a three-dimensional extended EM iterative algorithm. During the iteration process, the state parameters are continuously optimized to fit the observed data. Simultaneously, three-dimensional variational analysis (3D-Var) is incorporated, and the fusion result is smoothed by minimizing the cost function, which is:

[0058] In the formula: Represents the observation vector. Represents the observation operator. This represents the three-dimensional deformation state vector to be optimized. For the observation error covariance matrix, It is a spacetime Laplace operator that forces a smooth spacetime transition in the deformation field.

[0059] Repeat the iteration until the state estimation converges, and output the final three-dimensional deformation rate field.

[0060] To verify the effectiveness of this application's scheme, deformation monitoring of a reservoir slope in a plateau mountainous area is used as an example. During deformation monitoring of the reservoir slope in this plateau mountainous area, multiple L-band SAR images and multiple C-band InSAR images of the study area were acquired, such as... Figure 2-4 ,That Figure 2 The image displayed is an L-band InSAR image. Figure 3 The image displayed is a C-band InSAR image. Figure 4The diagram shows a schematic of InSAR three-dimensional deformation decomposition. Time-series InSAR processing is performed separately to obtain the LOS-direction deformation sequences of C-band and L-band SAR images. Based on radar geometric parameters and the spatial projection relationship of the dual-band observation vectors, a three-dimensional deformation rate least-squares solution model is constructed to generate an initial three-dimensional deformation rate field. The initial field is then calculated sequentially based on the steps and formulas proposed in this application, outputting a high-resolution, spatially continuous, and multi-source consistent three-dimensional deformation rate field that can characterize any time period within the observation interval. Table 1 below compares the three-dimensional deformation accuracy of the proposed method with that of traditional techniques, showing that the proposed method outperforms traditional single InSAR methods and GNSS single-point monitoring methods.

[0061] Table 1

[0062] In summary, this application utilizes a dual-band LOS deformation time series to construct a geometric projection relationship model. It directly outputs the initial three-dimensional deformation rate field through singular value decomposition, avoiding the ambiguity caused by insufficient geometric constraints in traditional methods. Furthermore, it introduces a terrain phase correction term to compensate for ionospheric delay. Based on the entropy weight method, it dynamically selects GNSS reference points, comprehensively considering station stability, data missing rate, and spatiotemporal distribution uniformity to calculate weights. A nonlinear projection model constrained by terrain gradients is established to achieve high-precision transformation. Simultaneously, a direction-sensitive weighting strategy is employed to independently calculate three-dimensional direction weights, balancing accuracy differences. Within the STRE model framework, radial basis functions are extended to three-dimensional space. The number and spatial distribution of basis functions are dynamically optimized based on the residual descent rate. The GNSS spatial gradient guides the densification of the region, eliminating dependence on external prior information. The three-dimensional state equation is extended to integrate the GNSS deformation rate time series as a rate coupling term. GNSS observations are embedded as strong constraints in a Kalman filter to correct the state variables. 3D-Var variational analysis is used to force smoothing of the deformation field through a spatiotemporal Laplace operator, outputting high-resolution, spatially continuous fusion results for any time period within the observation interval.

[0063] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A high-precision three-dimensional deformation adaptive monitoring method integrating dual-band InSar and GNSS, characterized in that, The method includes the following steps: Based on radar geometric parameters and the LOS deformation time series observed by dual-band InSAR, a dual-band observation equation is constructed, and the dual-band InSAR solution residual is obtained after decomposing the dual-band observation equation. The comprehensive weight of each GNSS observation point is calculated by the entropy weight method. Several GNSS observation points with high comprehensive weights are selected as the optimal combination of GNSS reference points, and the three-dimensional deformation observed by the optimal GNSS reference points is converted to the LOS direction. The two-dimensional radial basis functions are extended to three-dimensional radial basis functions. During the extension process, the number and distribution of the three-dimensional radial basis functions are optimized according to the residual descent rate. At the same time, the basis functions in the key areas are densified by combining the LOS direction results of the reference points. The original two-dimensional state equation in the Kalman filter algorithm is extended to three dimensions, and a rate coupling term is added. At the same time, GNSS three-dimensional observations are used as strong constraints for the Kalman filter algorithm. After iteration and 3D-Va analysis, the final three-dimensional deformation rate field is output.

2. The high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 1, characterized in that, The time series of LOS deformation of InSAR in C-band and L-band are obtained. Based on the incident angle, azimuth angle and the time series of LOS deformation of InSAR in C-band and L-band, a geometric projection equation is established. Ionospheric delay compensation is introduced into the geometric projection equation to obtain the final dual-band observation equation.

3. The high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 2, characterized in that, The dual-band observation equation is subjected to singular value decomposition to remove singular values ​​caused by noise, and retain the eigenvalues ​​and corresponding eigenvectors that characterize the main deformation trends, thereby obtaining the initial three-dimensional deformation rate fields in the east, north, and vertical directions and the dual-band InSAR solution residuals.

4. The high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 3, characterized in that, Based on site stability, real-time data loss rate, and spatiotemporal distribution uniformity entropy, the comprehensive weight of each GNSS observation point is calculated using the entropy weight method.

5. The high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 4, characterized in that, After obtaining the optimal combination of GNSS reference points, the GNSS three-dimensional observations of the optimal GNSS reference points are converted to the LOS direction, and the elevation change error is compensated during the conversion process.

6. The high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 5, characterized in that, After converting the GNSS 3D observations of the optimal GNSS reference point to the LOS direction, the standard deviations of the GNSS reference point in the east, north, and vertical directions are calculated. Based on the standard deviations of the GNSS reference point in the east, north, and vertical directions, as well as the dual-band InSAR solution residuals, the fusion weights in the east, north, and vertical directions are calculated.

7. A high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 5, characterized in that, Based on the constructed three-dimensional radial basis function, a state three-dimensional deformation rate field is generated. The state three-dimensional deformation rate field is transformed to the LOS direction through the geometric projection equation to obtain a simulated LOS-direction deformation time series. The residual between the simulated LOS-direction deformation time series and the LOS-direction deformation time series actually observed by dual-band InSAR is calculated. The root mean square (RMS) statistics are performed on the residuals of all spatial pixels and time points to obtain the root mean square error.

8. The high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 7, characterized in that, Based on the root mean square error of two consecutive intervals, the residual reduction rate is calculated. If the residual reduction rate is greater than a preset threshold, the number of three-dimensional radial basis functions is increased, and the spatial position of the three-dimensional radial basis functions is readjusted according to the spatial distribution of the residuals.

9. A high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 8, characterized in that, The coupling coefficients of the eastward, northward, and vertical deformation rates are determined by the spatial correlation between the three-dimensional radial basis functions, forming rate coupling terms.

10. A high-precision three-dimensional deformation adaptive monitoring method fusion of dual-band InSar and GNSS according to claim 9, characterized in that, In the state iterative update stage of Kalman filter prediction, each iteration first predicts the state through the three-dimensional state equation, then calculates the Kalman gain by combining the GNSS three-dimensional observations of the optimal GNSS reference point and the fusion weight, and uses the Kalman gain to correct the predicted state. Subsequently, 3D-Var analysis is introduced to optimize the corrected state and reduce the state estimation error. The iteration is repeated until the state estimation converges, and the final three-dimensional deformation rate field is output.

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