Slope deformation monitoring data processing method based on InSAR technology

Abstract

The invention provides a slope deformation monitoring data processing method based on an InSAR technology, and the method comprises the steps: generating an observation control point and an initial deformation phase sequence of a target slope region in a preset analysis domain through a deformation calculation engine, and carrying out the spatial-temporal sorting operation of the initial deformation phase sequence, and establishing a deformation association network between observation control points and outputting a result. And verifying whether an association network result meets a dynamic deformation constraint criterion, if yes, executing observation control point migration processing to form a to-be-optimized phase sequence, and if not, constructing a differential interference channel through adjacent observation control points. The deformation phase sequence is reconstructed based on the processing strategies of the phase sequence to be optimized, a stable deformation phase field is generated, and the processing strategies comprise the phase fusion strategy and the phase reconstruction strategy. According to the invention, the precision and reliability of slope deformation monitoring can be improved, and the dynamic change characteristics of the slope can be accurately reflected in real time.

Description

Technical Field

[0001] This invention relates to the field of synthetic aperture radar interferometry technology, and more specifically, to a method for processing slope deformation monitoring data based on InSAR technology. Background Technology

[0002] In the field of slope deformation monitoring, with the continuous development of technology, Synthetic Aperture Radar Interferometry (InSAR) technology has been widely used due to its high precision and wide-area monitoring capabilities. InSAR technology, by interferometric processing of multi-temporal SAR images of the same area, can acquire surface deformation information, providing important basis for slope stability assessment. However, traditional InSAR data processing methods have some limitations when processing slope deformation monitoring data. For example, traditional methods usually rely on a single interferometric pair combination, which is difficult to effectively process deformation monitoring data under the influence of complex terrain and atmospheric delay, resulting in limited accuracy and reliability of deformation monitoring. Furthermore, existing technologies lack effective dynamic deformation constraint mechanisms in the deformation phase unwrapping and phase fusion processes, failing to accurately reflect the spatiotemporal continuity and stability of slope deformation. In practical applications, slope deformation monitoring not only requires high-precision deformation data but also needs to reflect the dynamic changes of the slope in real time and accurately, so as to promptly detect potential landslide risks and take corresponding early warning measures.

[0003] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: First, traditional InSAR data processing methods struggle to effectively extract accurate deformation information when faced with complex terrain and atmospheric delay effects, resulting in insufficient deformation monitoring accuracy. Second, the prior art lacks effective constraints on the dynamic deformation characteristics of slopes during deformation phase unwrapping and fusion, failing to accurately reflect the spatiotemporal continuity of slope deformation. Furthermore, the prior art, when processing slope deformation monitoring data, fails to fully integrate slope geomechanical models and historical deformation data, making it difficult to accurately predict and warn of slope deformation trends. Summary of the Invention

[0004] This invention provides a method for processing slope deformation monitoring data based on InSAR technology, including: The deformation calculation engine generates observation control points and initial deformation phase sequence of the target slope area in the preset analysis domain. The initial deformation phase sequence is subjected to spatiotemporal sorting operation to establish a deformation correlation network between observation control points and output the correlation network result. The process verifies whether the correlation network results satisfy the dynamic deformation constraint criterion. If they do, observation control point migration processing is performed to form an optimized phase sequence; otherwise, a differential interferometric channel is constructed using adjacent observation control points. Specifically, this includes: detecting whether the phase difference value of each initial deformation phase sequence in the correlation network results exceeds a preset phase tolerance range; when the phase difference value exceeds the phase tolerance range, observation control point migration processing is performed on the initial deformation phase sequence in the analysis domain to generate a phase migration sequence, and phase unwrapping operation is performed on the phase migration sequence to form a first optimized phase sequence; when the phase difference value does not exceed the phase tolerance range, differential interferometric nodes are deployed between adjacent observation control points to generate an interferometric extension sequence, and phase unwrapping operation is performed on the interferometric extension sequence to form a second optimized phase sequence; the second optimized phase sequence is a continuously differentiable deformation surface. Based on the processing strategy of the phase sequence to be optimized, the deformed phase sequence is reconstructed to generate a stable deformed phase field; the processing strategy includes a phase fusion strategy and a phase reconstruction strategy.

[0005] Furthermore, the generation of observation control points and initial deformation phase sequences includes: acquiring multi-temporal SAR image datasets and performing interferometric pair combination optimization; performing precise orbit correction and de-flattening phase processing on the optimized interferometric pairs; extracting discrete interferometric measurements of the slope area and calculating the spatial coherence matrix; constructing observation control points and initial deformation phase sequences based on spatial coherence matrix mapping in the analysis domain; establishing the phase binding relationship between observation control points and physical monitoring piles, as well as the deformation mapping relationship between the initial deformation phase sequence and the measured deformation field; Alternatively, based on preset deformation model parameters, the initial deformation phase sequence and associated observation control points can be directly configured in the analysis domain, while a historical deformation database can be introduced for model parameter verification and correction.

[0006] Furthermore, the reconstructed deformed phase sequence includes: when the processing strategy is a phase fusion strategy, performing weighted fusion processing on all initial deformed phase sequences in the associated network results, calculating the phase quality index of each sequence and assigning fusion weight coefficients, and performing multi-source phase data fusion based on the weight coefficients to generate a stable deformed phase field; When the processing strategy is a phase reconstruction strategy, the starting observation control point of the first initial deformation phase sequence and its three-dimensional displacement vector and elevation change rate are collected, and the ending observation control point of the last initial deformation phase sequence and its three-dimensional displacement vector and elevation change rate are collected. Based on the starting observation control point, the starting elevation change vector, the ending observation control point and the ending elevation change vector, a stable deformation phase field is constructed, and a slope geomechanical model is introduced to constrain the deformation trend.

[0007] Furthermore, the construction of a stable deformable phase field includes: generating an initial phase field surface in the analysis domain based on the spatial coordinates and elevation change rate of the initial observation control point; generating a termination phase field surface based on the spatial coordinates and elevation change rate of the termination observation control point; generating a phase field to be fused based on the initial and termination phase field surfaces; performing spatiotemporal superposition processing on the phase field to be fused and the initial deformable phase sequence to form a phase group to be corrected; and performing elevation constraint processing based on least squares adjustment on the initial deformable phase sequence and the phase group to be corrected to generate a stable deformable phase field that meets the requirements of continuity and smoothness.

[0008] Further, after the elevation constraint processing, the following steps are performed: extracting the first ordered deformation observation set of the initial deformation phase sequence and the second ordered deformation observation set of the phase group to be corrected; performing time series analysis on the first ordered deformation observation set to extract the deformation trend direction; constructing a normal projection operator based on the deformation trend direction; performing orthogonal projection operation of the deformation field on the second ordered deformation observation set using the normal projection operator to generate a third ordered deformation observation set; performing atmospheric delay correction and noise filtering on the third ordered deformation observation set; and transmitting the processed third ordered deformation observation set as a reliable deformation expression of the stable deformation phase field to the slope early warning system for real-time monitoring.

[0009] Furthermore, after the data is transmitted to the slope early warning system, the following steps are performed: comparing the first deformation accuracy index of the expected deformation expression with the second deformation accuracy index of the initial deformation phase sequence; calculating the deformation rate error and displacement cumulative error; when the deformation rate error exceeds a preset rate threshold or the displacement cumulative error exceeds a preset displacement threshold, it is determined that the first deformation accuracy index has not reached the dynamic deformation accuracy benchmark; at this time, deformation control markers are inserted into the stable deformation phase field and their spatial distribution density is optimized, or the elevation change vector is reconstructed and the three-dimensional displacement field is recalculated, or the phase unwrapping parameters are adjusted until the slope monitoring accuracy standard is met and the Kolmogorov-Smirnov test is passed.

[0010] Furthermore, the spatiotemporal sorting operation includes generating a time distribution sequence of observation control points based on satellite orbit parameters and revisit period, extracting topographic slope maps and rock mass structural characteristic parameters of the slope area, classifying the deformation sensitivity of the time distribution sequence according to rock mass stability and topographic complexity, assigning phase unwrapping priority weight coefficients to the initial deformation phase sequence according to the classification results, and finally performing interferometric baseline optimization sorting on the initial deformation phase sequence based on topographic relief and atmospheric change characteristics to generate the optimal interferometric pair combination sequence.

[0011] Furthermore, the establishment of the deformation correlation network includes configuring coherent domain identifiers for observation control points of adjacent initial deformation phase sequences, calculating the spatial distance and phase correlation index between control points, constructing a distributed phase unwrapping tree structure based on the coherent domain identifiers and optimizing the tree node connection paths, registering the unwrapping path topology relationship in the correlation network results and calculating the path reliability score, generating a phase continuity constraint matrix based on the unwrapping path topology relationship and reliability score, and applying the constraint matrix to the phase unwrapping operation process.

[0012] Furthermore, the elevation constraint processing includes generating a first elevation constraint surface for the initial deformation phase sequence and calculating its curvature characteristics; generating a second elevation constraint surface for the phase group to be corrected and calculating its gradient distribution; performing minimum energy fusion calculation based on the finite element method on the first and second elevation constraint surfaces; inverting the three-dimensional deformation displacement field of the slope based on the fusion result and calculating the displacement vector direction; finally, performing atmospheric phase correction and terrain phase compensation on the three-dimensional deformation displacement field, and introducing external GNSS monitoring data for result verification.

[0013] Furthermore, the verification process of the dynamic deformation accuracy benchmark includes acquiring deformation displacement residual data fed back by the slope early warning system and performing time series modeling; monitoring the spatial continuity index of the stable deformation phase field and calculating the distribution characteristics of the fault zone; quantifying the phase unwrapping confidence parameter and assessing the degree of atmospheric influence; activating the deformation control marker insertion mechanism and optimizing the marker density when the deformation displacement residual exceeds the preset residual threshold; initiating the elevation change vector reconstruction mechanism and recalculating the displacement field when the spatial continuity index is lower than the preset continuity threshold; triggering the differential interferometric node strengthening process and increasing the number of interferometric pairs when the phase unwrapping confidence is lower than the preset confidence threshold; and generating a slope stability report including risk level assessment after all verification parameters meet the standards.

[0014] The embodiments of the present invention have at least the following beneficial effects: 1. By generating observation control points and initial deformation phase sequences through the deformation calculation engine, and combining the spatiotemporal sorting and the establishment of deformation correlation networks, it can effectively process slope deformation monitoring data under the influence of complex terrain and atmospheric delay. This solves the problem of insufficient accuracy in extracting deformation information in complex environments by traditional methods, and significantly improves the accuracy and reliability of slope deformation monitoring.

[0015] 2. The dynamic deformation constraint criterion was used to verify the results of the associated network, and an appropriate processing strategy was selected based on the results. This effectively constrained the spatiotemporal continuity of slope deformation, solved the defect in the existing technology that lacked a dynamic deformation constraint mechanism and could not accurately reflect the spatiotemporal continuity of slope deformation, and enhanced the stability of the deformation monitoring results.

[0016] 3. Based on the phase fusion strategy and phase reconstruction strategy of the phase sequence to be optimized, the deformation phase sequence is reconstructed to generate a stable deformation phase field. The slope geomechanical model is introduced to constrain the deformation trend. It fully combines historical deformation data and geomechanical model, solves the problem that the existing technology cannot effectively use this information for deformation trend prediction and early warning, and improves the early warning capability and practicality of slope deformation monitoring. Attached Figure Description

[0017] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating a slope deformation monitoring data processing method based on InSAR technology, provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] 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.

[0020] like Figure 1 As shown, this application proposes a slope deformation monitoring data processing method based on InSAR technology, which performs the following operations: S1. Generate observation control points and initial deformation phase sequence of the target slope area in the preset analysis domain through the deformation calculation engine, perform spatiotemporal sorting operation on the initial deformation phase sequence, establish deformation association network between observation control points and output association network results; S2. Verify whether the correlation network result satisfies the dynamic deformation constraint criterion. If it does, perform observation control point migration processing to form a phase sequence to be optimized. If it does not, construct a differential interferometric channel through adjacent observation control points. Specifically, this includes: detecting whether the phase difference value of each initial deformation phase sequence in the correlation network result exceeds the preset phase tolerance range; when the phase difference value exceeds the phase tolerance range, perform observation control point migration processing on the initial deformation phase sequence in the analysis domain to generate a phase migration sequence, and perform phase unwrapping operation on the phase migration sequence to form a first phase sequence to be optimized; when the phase difference value does not exceed the phase tolerance range, deploy differential interferometric nodes between adjacent observation control points to generate an interferometric extension sequence, and perform phase unwrapping operation on the interferometric extension sequence to form a second phase sequence to be optimized; the second phase sequence to be optimized is a continuously differentiable deformation surface. S3. Based on the processing strategy of the phase sequence to be optimized, reconstruct the deformed phase sequence to generate a stable deformed phase field; the processing strategy includes a phase fusion strategy and a phase reconstruction strategy.

[0021] This application achieves a dual improvement in phase unwrapping accuracy and spatiotemporal continuity in slope deformation monitoring by combining a dynamic deformation constraint criterion with a deformation correlation network, along with observation control point migration processing and optimized selection of differential interferometric channels. Simultaneously, through the complementary application of phase fusion and reconstruction strategies, multi-source interferometric data are effectively fused and geomechanical constraints are introduced, solving the deformation field fracture problem caused by atmospheric delay and topographic complexity in traditional methods, thus improving the reliability and early warning timeliness of slope dynamic deformation monitoring.

[0022] The deformation calculation engine generates observation control points and an initial deformation phase sequence for the target slope area within a preset analysis domain. A spatiotemporal sorting operation is performed on the initial deformation phase sequence to establish a deformation correlation network between the observation control points, and the network results are output. The correlation network results are then verified to ensure they meet the dynamic deformation constraint criteria. If they do, observation control point migration processing is performed to form a phase sequence to be optimized; otherwise, differential interferometry channels are constructed using adjacent observation control points.

[0023] Specifically, the phase difference values ​​of each initial deformation phase sequence in the detection network result are checked to see if they exceed a preset phase tolerance range. When the phase difference value exceeds the phase tolerance range, observation control point migration processing is performed on the initial deformation phase sequence in the analysis domain to generate a phase migration sequence, and phase unwrapping operation is performed on the phase migration sequence to form the first phase sequence to be optimized. When the phase difference value does not exceed the phase tolerance range, differential interferometry nodes are deployed between adjacent observation control points to generate an interferometric spread sequence, and phase unwrapping operation is performed on the interferometric spread sequence to form the second phase sequence to be optimized. The second phase sequence to be optimized is a continuously differentiable deformation surface.

[0024] The deformed phase sequence is reconstructed based on the processing strategy of the phase sequence to be optimized, generating a stable deformed phase field. The processing strategy includes a phase fusion strategy and a phase reconstruction strategy.

[0025] Through the above steps, this application achieves dynamic constraint and optimization processing of the initial deformation phase sequence. The migration of observation control points and the construction of differential interferometric channels provide two complementary phase correction methods, effectively addressing the phase error accumulation problem under complex terrain conditions. The establishment of spatiotemporal sequencing and deformation correlation networks provides a topological basis for dynamic constraint verification, ensuring that the phase correction process conforms to the spatiotemporal continuity of slope deformation. The finally generated stable deformation phase field accurately reflects the dynamic change characteristics of the slope.

[0026] The deformation calculation engine is the core algorithm module that processes InSAR data and calculates deformation information. Specifically, it can be implemented using a distributed computing framework combined with a phase unwrapping algorithm to generate observation control points and initial deformation phase sequences. The preset analysis domain refers to a pre-defined three-dimensional spatial range, which can be achieved by defining the spatial boundaries and elevation range of the target slope area using a geographic information system, thus constraining the computational range of deformation calculation. The observation control points are physical monitoring points in the slope area with stable scattering characteristics, which can be generated using permanent scatterer identification technology from SAR imagery, serving as data nodes for establishing the deformation correlation network.

[0027] The initial deformation phase sequence refers to the set of phase change data obtained from multi-temporal SAR image interferometry. Specifically, it can be implemented using a phase unwrapping algorithm combined with time series analysis to characterize the spatiotemporal distribution characteristics of slope deformation. The spatiotemporal sorting operation refers to prioritizing the phase sequence according to the time and spatial dimensions. This can be implemented based on satellite orbital parameters, terrain slope, and atmospheric delay models to optimize the selection order of interferometric pair combinations.

[0028] A deformation correlation network refers to a topological structure describing the deformation transmission relationship between observation control points. Specifically, it can be implemented by constructing a graph structure based on the phase correlation and spatial distance between control points, used to constrain the continuity of the phase unwrapping path. The dynamic deformation constraint criterion is a verification standard for evaluating whether the deformation network meets the spatiotemporal continuity requirements. This can be achieved by combining statistical hypothesis testing with deformation rate threshold determination, ensuring the reliability of deformation monitoring results. The observation control point migration processing refers to adjusting the spatial distribution of control points to optimize the phase sequence. This can be implemented by combining phase gradient estimation with spatial interpolation algorithms, used to eliminate phase jumps and atmospheric interference errors.

[0029] Differential interferometry channels refer to interferometric nodes between adjacent control points used for phase difference calculations. This can be achieved by setting up virtual interferometric baselines and calculating phase gradients, enhancing the accuracy of local deformation information extraction. Phase unwrapping operations refer to the calculation process of restoring the wrapped phase to the true deformation, which can be implemented using least squares methods or graph theory algorithms to generate continuously differentiable deformation surfaces. The stable deformation phase field refers to the final deformation result after error correction and data fusion. This can be achieved through weighted fusion of multi-source phase data combined with geomechanical model constraints, outputting a high-confidence three-dimensional deformation field.

[0030] Phase fusion strategy refers to a weighted calculation method that integrates phase data from different interferometers. Specifically, it can be achieved by combining phase quality index evaluation with adaptive weight allocation to improve the spatial continuity of the deformation field. Phase reconstruction strategy, on the other hand, involves reconstructing the phase field based on the deformation characteristics of the starting and ending control points. This can be achieved through three-dimensional displacement vector interpolation combined with a slope stability model to ensure the physical rationality of the deformation trend.

[0031] As a preferred embodiment, the solution of this application is specifically implemented as follows: First, a network of observation control points is generated within a pre-defined slope analysis domain using a deformation calculation engine. The analysis domain covers the entire target slope area, including the slope crest, slope face, and slope toe. The observation control points are strategically placed based on topographic features and the expected deformation distribution, forming a uniformly distributed grid structure. An initial deformation phase sequence is generated for each observation control point, containing phase information from multiple time phases.

[0032] Secondly, a spatiotemporal sorting operation is performed on the initial deformation phase sequences. Observation time series are generated based on satellite orbital parameters and revisit periods. Combining slope topography maps and rock mass structural characteristics, the time series are classified according to deformation sensitivity. Phase unwrapping priority weights are assigned to each initial deformation phase sequence based on the classification results. Finally, the sequences are optimized and sorted using interferometric baselines based on topographic relief and atmospheric variation characteristics to generate the optimal interferometric pair combination sequence.

[0033] Furthermore, a deformation correlation network is established among the observation control points. Coherence domain identifiers are configured for adjacent control points, and spatial distance and phase correlation indices between control points are calculated. A distributed phase unwrapping tree structure is constructed based on the coherence domain identifiers, and the connection paths between tree nodes are optimized. Unwrapping path topology relationships are registered in the correlation network, and path reliability scores are calculated. A phase continuity constraint matrix is ​​generated based on the topology relationships and scores and applied to subsequent phase unwrapping processes.

[0034] Therefore, it is verified whether the correlation network results meet the dynamic deformation constraint criteria. The phase difference values ​​of each initial deformation phase sequence are checked to see if they exceed the preset phase tolerance range. When the phase difference value exceeds the tolerance range, observation control point migration processing is performed. Control point positions are reassigned in the analysis domain to generate a phase migration sequence. Phase unwrapping operation is performed on the phase migration sequence to form the first phase sequence to be optimized.

[0035] When the phase difference value does not exceed the tolerance range, a differential interferometry channel is constructed. Differential interferometry nodes are deployed between adjacent observation control points to generate an interferometric spread sequence. Phase unwrapping operation is performed on the interferometric spread sequence to form a second phase sequence to be optimized. The second phase sequence to be optimized is a continuously differentiable deformable surface, ensuring continuity in the spatial dimension.

[0036] Finally, based on the processing strategy for the phase sequence to be optimized, the deformation phase sequence is reconstructed to generate a stable deformation phase field. The processing strategy includes a phase fusion strategy and a phase reconstruction strategy. The phase fusion strategy performs weighted fusion processing on all initial deformation phase sequences in the associated network. The phase quality index of each sequence is calculated and fusion weight coefficients are assigned, and multi-source phase data fusion is performed. The phase reconstruction strategy constructs a stable deformation phase field based on the three-dimensional displacement vectors and elevation change rates of the starting and ending observation control points, while introducing a slope geomechanical model to constrain the deformation trend.

[0037] This application further proposes a method for generating observation control points and initial deformation phase sequences, including: acquiring multi-temporal SAR image datasets and performing interferometric pair combination optimization; performing precise orbit correction and de-flattening phase processing on the optimized interferometric pairs; extracting discrete interferometric measurements of the slope area and calculating the spatial coherence matrix; constructing observation control points and initial deformation phase sequences in the analysis domain based on spatial coherence matrix mapping; establishing the phase binding relationship between observation control points and physical monitoring piles, as well as the deformation mapping relationship between the initial deformation phase sequence and the measured deformation field; or directly configuring the initial deformation phase sequence and associated observation control points in the analysis domain based on preset deformation model parameters, while introducing a historical deformation database for model parameter verification and correction.

[0038] Interferometric pair optimization involves adjusting the temporal and spatial baseline parameters of SAR imagery to select interferometric pairs with baseline lengths meeting preset thresholds, reducing the impact of terrain phase residuals and atmospheric noise. Precise orbit correction uses satellite orbit parameters and ground control point coordinates for phase error compensation, while de-flattening phase processing removes low-frequency phase components by fitting a terrain trend surface. The spatial coherence matrix, calculated based on the statistical characteristics of interferometric measurements, is used to evaluate the phase stability of each pixel and select high-coherence areas as observation control points. Phase binding relationships are established by matching the coordinates of observation control points with the location information of physical monitoring piles, and deformation mapping relationships are achieved by converting the phase change of the initial deformation phase sequence into a three-dimensional displacement field. Preset deformation model parameters include the slope's deformation rate, displacement direction, and geomechanical parameters. A historical deformation database is used to compare model predictions with actual monitoring data to correct for deviations in model parameters.

[0039] Specifically, after interferometric pair combination optimization, the baseline parameters of the multi-temporal SAR image dataset are limited to a reasonable range, reducing the complexity of phase unwrapping. Precise orbit correction and terrain-removing phase processing eliminate low-frequency phase interference caused by satellite orbit errors and terrain undulations, improving the accuracy of discrete interferometric measurements. The spatial coherence matrix is ​​constructed based on the phase stability of pixels, with high coherence regions prioritized as observation control points to ensure the reliability of the initial deformation phase sequence. The phase binding relationship between observation control points and physical monitoring piles is achieved through a coordinate matching algorithm, enabling the initial deformation phase sequence to accurately reflect the spatial distribution of the actual deformation field. When the initial deformation phase sequence is directly configured using preset deformation model parameters, the model parameters need to be iteratively corrected using historical deformation data, such as adjusting the deformation rate parameter using the least squares method to align it with the deformation trend of historical monitoring data. The introduction of a historical deformation database verifies the rationality of the model's prediction results. If the displacement field predicted by the model deviates significantly from historical data, a model parameter recalibration process is triggered, such as adjusting the elastic modulus parameter in the geomechanical model. Through the above steps, the spatial distribution of observation control points and the accuracy of the initial deformation phase sequence are effectively improved, providing a reliable data foundation for the subsequent construction of the deformation correlation network.

[0040] As a preferred embodiment, the solution of this application is specifically implemented as follows: The generation of observation control points and initial deformation phase sequences includes the following steps: First, acquire a multi-temporal SAR image dataset and perform interferometric pair combination optimization on the dataset. The optimized interferometric pairs undergo precise orbit correction and de-flattening phase processing. Then, extract discrete interferometric measurements of the slope area and calculate the spatial coherence matrix. In the analysis domain, construct the observation control points and initial deformation phase sequences based on the spatial coherence matrix mapping. Establish the phase binding relationship between the observation control points and physical monitoring pile locations, as well as the deformation mapping relationship between the initial deformation phase sequences and the measured deformation field.

[0041] Another approach is to directly configure the initial deformation phase sequence and associated observation control points in the analysis domain based on preset deformation model parameters. Simultaneously, a historical deformation database is used for model parameter verification and correction. This method can generate the initial deformation field through a theoretical model even in the absence of actual SAR data.

[0042] In practical implementation, the multi-temporal SAR image dataset can contain 20-30 SAR images covering the target slope area. Interferometric pair optimization uses the minimum baseline method, selecting interferometric pairs with a vertical baseline of less than 500 meters. Precise orbit correction uses precise ephemeris data, and 90-meter resolution SRTM-EM data is used for de-leveling phase processing. The spatial coherence matrix is ​​calculated using a 20×20 pixel sliding window. The density of observation control points is set to 50-100 per square kilometer. The phase binding between physical monitoring piles and observation control points uses the nearest neighbor interpolation method. The deformation mapping relationship is established using a polynomial fitting method.

[0043] Preset deformation model parameters can include factors such as slope gradient, lithology, and groundwater level. The historical deformation database contains slope monitoring data from the past 5 years. Model parameter validation uses the least squares method, and calibration uses an iterative optimization algorithm.

[0044] This application further proposes two processing strategies for reconstructing deformation phase sequences: When using a phase fusion strategy, all initial deformation phase sequences are weighted and fused, the phase quality index of each sequence is calculated and fusion weight coefficients are assigned, and multi-source phase data fusion is performed based on the weight coefficients to generate a stable deformation phase field; When using a phase reconstruction strategy, the starting observation control point of the first initial deformation phase sequence and its three-dimensional displacement vector and elevation change rate are collected, and the ending observation control point of the last initial deformation phase sequence and its three-dimensional displacement vector and elevation change rate are collected. A stable deformation phase field is constructed based on the starting observation control point, the starting elevation change vector, the ending observation control point, and the ending elevation change vector, while a slope geomechanical model is introduced to constrain the deformation trend.

[0045] The phase fusion strategy quantifies the reliability of each initial sequence using phase quality indices. For example, sequences with phase coherence higher than 0.6 are assigned a weighting coefficient of 0.8, while sequences with a phase noise standard deviation lower than 1.2 mm are assigned a weighting coefficient of 0.5. Multi-source data fusion uses a weighted average algorithm to generate a comprehensive deformation field. In the phase reconstruction strategy, the three-dimensional displacement vectors of the starting and ending observation control points are calibrated using GNSS measured data, the elevation change rate is obtained based on time-series InSAR calculations, and the geomechanical model uses the Mohr-Coulomb criterion to constrain the deformation trend direction. The switching condition between the two strategies is set based on the spatial coherence threshold of the slope area; the reconstruction strategy is activated when the average coherence is lower than 0.3.

[0046] Specifically, when executing the phase fusion strategy, the phase quality indices of each initial deformed phase sequence are first calculated, including the mean and variance of the spatial coherence matrix and the RMS value of the phase residuals. For example, sequences with a spatial coherence mean greater than 0.7 are assigned a fusion weight of 0.7, and sequences with a phase residual RMS less than 1.5 mm are given an additional weight coefficient of 0.3. Multi-source data fusion uses a signal-to-noise ratio-based weighted superposition algorithm, which normalizes the weight coefficients and then linearly combines the phase values ​​to generate a comprehensive deformed phase field. When executing the phase reconstruction strategy, the three-dimensional displacement vector of the starting control point is extracted from the first phase sequence, for example, a horizontal displacement of 2.5 mm, a vertical displacement of -4.8 mm, and an elevation change rate of -0.3 mm / day; the horizontal displacement of the ending control point is extracted from the last phase sequence, for example, a horizontal displacement of 3.1 mm, a vertical displacement of -5.2 mm, and an elevation change rate of -0.35 mm / day. Based on the spatial coordinates and elevation parameters of these two control points, a cubic spline interpolation algorithm is used to construct the initial and final phase field surfaces. The initial phase field to be fused is generated by superimposing these surfaces. The geomechanical model calculates the direction of the maximum deformation gradient by inputting the rock mass shear strength parameter of 35 kPa and the internal friction angle of 28°, constraining the deformation trend within the slope dip range of 15° ± 2°, and finally generating a stable deformation phase field that conforms to the laws of mechanics.

[0047] As a preferred embodiment, the solution of this application is specifically implemented as follows: Reconstructing the deformation phase sequence includes the following steps: When the processing strategy is phase fusion, all initial deformed phase sequences in the associated network results are weighted and fused. First, the phase quality indices of each sequence are calculated, including phase noise level, spatial coherence, and temporal correlation. Then, fusion weight coefficients are assigned based on the phase quality indices. For example, sequences with low phase noise and high spatial coherence are assigned larger weights. Finally, multi-source phase data fusion is performed based on the weight coefficients to generate a stable deformed phase field.

[0048] When the processing strategy is a phase reconstruction strategy, the initial observation control points, their three-dimensional displacement vectors, and elevation change rates of the first initial deformation phase sequence are first acquired. Next, the final observation control points, their three-dimensional displacement vectors, and elevation change rates of the last initial deformation phase sequence are acquired. Then, a stable deformation phase field is constructed based on the initial observation control points, the initial elevation change vector, the final observation control points, and the final elevation change vector. Specifically, a continuous deformation surface is generated between the initial and final control points using cubic spline interpolation. Simultaneously, a slope geomechanical model is introduced to constrain the deformation trend, ensuring that the reconstructed phase field conforms to the slope deformation law.

[0049] This application further proposes a specific method for constructing a stable deformable phase field, including: generating an initial phase field surface in the analysis domain based on the spatial coordinates and elevation change rate of the initial observation control point; generating a termination phase field surface based on the spatial coordinates and elevation change rate of the termination observation control point; generating a phase field to be fused based on the initial and termination phase field surfaces; performing spatiotemporal superposition processing on the phase field to be fused and the initial deformable phase sequence to form a phase group to be corrected; and performing elevation constraint processing based on least squares adjustment on the initial deformable phase sequence and the phase group to be corrected to generate a stable deformable phase field that meets the requirements of continuity and smoothness.

[0050] The initial and final phase field surfaces were generated using cubic spline interpolation, with the vertex coordinates corresponding to the spatial coordinates of the observation control points. The elevation change rate of the vertex was calculated using cubic polynomial fitting. The phase fields to be fused were generated using a surface weighted average method, with the weighting coefficients dynamically adjusted based on the time interval and spatial density of the observation control points. Spatiotemporal overlay processing employed spatiotemporal convolution operations, with a convolution kernel size of 5×5 pixels and a time window length of 3 interferometric periods. During elevation constraint processing, the objective function of least squares adjustment included phase continuity residuals and surface smoothness residuals, with a residual weight ratio of 2:1.

[0051] Specifically, during the generation of the initial phase field surface, spatial coordinates are transformed using satellite orbit parameters and topographic slope maps, and the elevation change rate is calculated based on the three-dimensional displacement vector and the time baseline length. The final phase field surface is generated using the same algorithm, but the cumulative displacement error of the final observation control point is introduced as a rate correction factor. In the weighted average calculation of the phase fields to be fused, phase fields with closer time intervals are assigned higher weights, while regions with higher spatial density are assigned lower weights, thus balancing the spatiotemporal resolution differences. During spatiotemporal overlay processing, the phase values ​​of the initial deformed phase sequence are convolved with the phase fields to be fused using a sliding window method, and the phase value at the center point of the convolution kernel is used as the overlay result. In the elevation constraint processing stage, the phase continuity residual term is calculated using the phase difference between adjacent pixels, and the surface smoothness residual term is calculated using the Laplacian operator. Finally, the minimum value of the objective function is solved through an iterative optimization algorithm to obtain a continuous and smooth stable deformed phase field.

[0052] As a preferred embodiment, the solution of this application is specifically implemented as follows: In the analysis domain, an initial phase field surface is generated based on the spatial coordinates of the initial observation control point and the rate of elevation change. Specifically, the top area of ​​the slope is selected as the initial observation control point, and its three-dimensional spatial coordinates (x, y, z) are obtained through GPS measurement, while the rate of elevation change v is measured using a level. Then, the initial phase field surface S1(x, y, z, v) is generated in the analysis domain using the bicubic spline interpolation method.

[0053] The termination phase field surface is generated based on the spatial coordinates and elevation change rate of the termination observation control point. Furthermore, the slope toe area is selected as the termination observation control point, and its spatial coordinates and elevation change rate are obtained through GPS and leveling. The termination phase field surface S2(x, y, z, v) is generated using the same interpolation method.

[0054] The phase fields to be fused are generated based on the initial and final phase field surfaces. Then, time series interpolation is performed on S1 and S2 to obtain the phase field surface sequence {St} at the intermediate time point, where t represents the time series.

[0055] The phase field to be fused is spatiotemporally superimposed with the initial deformed phase sequence to form a phase group to be corrected. Specifically, {St} is registered and superimposed with the initial deformed phase sequence in the time and space dimensions to generate the phase group Φ to be corrected.

[0056] The initial deformed phase sequence and the phase group to be corrected are subjected to elevation constraint processing based on least squares adjustment to generate a stable deformed phase field that meets the requirements of continuity and smoothness. For example, the observation equation system is established as: V = AX - L, where V is the residual vector, A is the coefficient matrix, X is the parameter vector to be solved, and L is the constant term vector. By solving X through least squares adjustment, the optimal estimate is obtained, and then the stable deformed phase field Φs is generated.

[0057] This application further proposes, after performing elevation constraint processing, to continue performing the following steps: extracting the first ordered deformation observation set of the initial deformation phase sequence and the second ordered deformation observation set of the phase group to be corrected; performing time series analysis on the first ordered deformation observation set to extract the deformation trend direction; constructing a normal projection operator based on the deformation trend direction; performing orthogonal projection operation of the deformation field on the second ordered deformation observation set using the normal projection operator to generate a third ordered deformation observation set; performing atmospheric delay correction and noise filtering on the third ordered deformation observation set; and transmitting the processed third ordered deformation observation set as a reliable deformation expression of the stable deformation phase field to the slope early warning system for real-time monitoring.

[0058] The first ordered deformation observation set is generated by extracting the deformation displacement and elevation change rate of each observation control point in the initial deformation phase sequence. The second ordered deformation observation set is generated by superimposing the deformation components of each phase field in the phase group to be corrected. Time series analysis uses the sliding window method to calculate the time gradient of the deformation displacement, and the deformation trend direction is determined by principal component analysis. The normal projection operator constructs an orthogonal projection matrix based on the trend direction to eliminate components in the deformation field that deviate from the trend direction. The orthogonal projection operation projects the displacement vectors in the second ordered deformation observation set onto the normal plane, generating the third ordered deformation observation set. Atmospheric delay correction uses external meteorological data or a regional atmospheric model for phase compensation, and noise filtering uses wavelet transform or Kalman filtering algorithms.

[0059] Specifically, after elevation constraint processing, the initial deformation phase sequence and the phase group to be corrected are used to extract deformation observation data to form first and second ordered deformation observation sets. By analyzing the time series of the first ordered deformation observation set, the dominant trend direction of slope deformation is determined. Based on the normal projection operator constructed in this direction, the displacement vectors in the second ordered deformation observation set are orthogonally decomposed, retaining the deformation components consistent with the trend direction and eliminating noise and anomalous fluctuations. The third ordered deformation observation set, after orthogonal projection, further eliminates ionospheric and tropospheric interference through atmospheric delay correction, and then removes high-frequency random errors through noise filtering. The final obtained deformation data has higher spatial continuity and temporal consistency, accurately reflecting the actual deformation state of the slope and providing reliable input for the early warning system.

[0060] As a preferred embodiment, the solution of this application is specifically implemented as follows: After performing elevation constraint processing, continue with the following steps: First, a first ordered deformation observation set and a second ordered deformation observation set for the phase group to be corrected are extracted from the initial deformation phase sequence. Specifically, observation points with temporal continuity are selected from the initial deformation phase sequence to construct the first ordered deformation observation set. Simultaneously, corresponding observation points are selected from the phase group to be corrected to construct the second ordered deformation observation set.

[0061] Secondly, time series analysis is performed on the first ordered deformation observation set to extract the deformation trend direction. Further, the least squares method is used to perform linear fitting on the first ordered deformation observation set to obtain the principal direction vector of the deformation trend.

[0062] Then, a normal projection operator is constructed based on the deformation trend direction. Thus, a normal plane is calculated based on the principal direction vector, and a projection matrix is ​​constructed to project the deformation vector onto this normal plane.

[0063] Next, the normal projection operator is used to perform orthogonal projection operation on the second ordered deformation observation set to generate the third ordered deformation observation set. Specifically, the deformation vector of each observation point in the second ordered deformation observation set is transformed onto the normal plane through a projection matrix to obtain the third ordered deformation observation set.

[0064] Subsequently, atmospheric delay correction and noise filtering were performed on the third ordered deformation observation set. For example, time series filtering was used to remove the effects of atmospheric delay, and wavelet transform was used for noise filtering.

[0065] Finally, the processed third ordered deformation observation set is used as a reliable deformation expression of the stable deformation phase field and transmitted to the slope early warning system for real-time monitoring. As a preferred implementation, the third ordered deformation observation set can be converted into a slope surface displacement field and presented visually on the early warning system interface.

[0066] This application further proposes that after transmission to the slope early warning system, the following steps are performed: comparing the first deformation accuracy index of the suspected deformation expression with the second deformation accuracy index of the initial deformation phase sequence; calculating the deformation rate error and displacement cumulative error; when the deformation rate error exceeds a preset rate threshold or the displacement cumulative error exceeds a preset displacement threshold, it is determined that the first deformation accuracy index has not reached the dynamic deformation accuracy benchmark; at this time, deformation control markers are inserted into the stable deformation phase field and their spatial distribution density is optimized, or the elevation change vector is reconstructed and the three-dimensional displacement field is recalculated, or the phase unwrapping parameters are adjusted until the slope monitoring accuracy standard is met and the Kolmogorov-Smirnov test is passed.

[0067] The deformation accuracy index comparison is achieved by calculating the standard deviation of the deformation rate at the same monitoring point. The deformation rate error is calculated using the sliding window method to determine the average displacement difference between adjacent time slices. The cumulative displacement error is obtained by integrating the deformation rate curve and the difference between the measured displacement. The preset rate threshold is dynamically adjusted based on the shear strength parameters of the slope rock mass, and the preset displacement threshold is set based on historical landslide displacement data. The insertion of deformation control marker points uses Delaunay triangulation to optimize spatial density. The elevation change vector reconstruction is achieved through joint adjustment of the inverted three-dimensional displacement field and GNSS data. The phase unwrapping parameter adjustment includes iterative optimization of the interferometric baseline length threshold and the coherence coefficient threshold.

[0068] Specifically, after the stable deformation phase field is transmitted to the early warning system, the standard deviation of the deformation rate and the integral value of the displacement in the first deformation accuracy index are extracted and compared point by point with the second deformation accuracy index of the initial deformation phase sequence. When the deformation rate error exceeds the rate threshold calculated dynamically based on the rock mass shear strength, or the cumulative displacement error exceeds the displacement threshold statistically derived from historical landslide data, the accuracy correction mechanism is triggered. The density of monitoring points is increased by inserting deformation control markers, and the spatial distribution of markers is optimized using Delaunay triangulation to improve the resolution of the local deformation field. When the deviation between the elevation change vector and the GNSS measured data exceeds a threshold, the three-dimensional displacement field is re-inverted and joint adjustment is performed. If the phase unwrapping confidence is insufficient, the interferometric baseline length threshold and coherence coefficient threshold are adjusted to increase the number of effective interferometric pairs. Finally, the Kolmogorov-Smirnov test is used to verify whether the corrected deformation field conforms to the normal distribution characteristics, ensuring that the deformation monitoring results meet the statistical requirements for slope stability assessment.

[0069] As a preferred embodiment, the solution of this application is specifically implemented as follows: In the slope monitoring system, the deformation accuracy index of the reliable deformation expression and the initial deformation phase sequence is compared. First, the deformation rate error and displacement cumulative error are calculated. When the deformation rate error exceeds a preset rate threshold, such as 0.5 mm / day, or the displacement cumulative error exceeds a preset displacement threshold, such as 10 mm, it is determined that the first deformation accuracy index has not reached the dynamic deformation accuracy benchmark.

[0070] At this point, the system performs the following optimization steps: 1. Insert deformation control markers into the stable deformation phase field. Specifically, add 10-20 evenly distributed control markers in key areas of the slope, optimizing the spatial distribution density to 50-100 per square kilometer.

[0071] 2. Reconstruct the elevation change vector. Using the elevation information of the newly added control markers, the three-dimensional displacement field of the entire monitoring area is recalculated using the Kriging interpolation method.

[0072] 3. Adjust the phase unwrapping parameters. Increase the number of iterations of the phase unwrapping algorithm from 100 to 500, while reducing the phase noise threshold from 0.3 radians to 0.1 radians.

[0073] The system iteratively executes the above optimization steps until the slope monitoring accuracy standard is met. Finally, the optimized deformation data is subjected to the Kolmogorov-Smirnov test to verify the consistency of its statistical distribution.

[0074] This application further proposes a spatiotemporal sorting operation that includes generating a time distribution sequence of observation control points based on satellite orbital parameters and revisit period, extracting topographic slope maps and rock mass structural characteristic parameters of the slope area, classifying the deformation sensitivity of the time distribution sequence according to rock mass stability and topographic complexity, assigning phase unwrapping priority weight coefficients to the initial deformation phase sequence based on the classification results, and finally performing interferometric baseline optimization sorting on the initial deformation phase sequence based on topographic relief and atmospheric change characteristics to generate the optimal interferometric pair combination sequence.

[0075] The satellite orbital parameters include orbital inclination, orbital altitude, and repetition period. The revisit period is calculated based on the satellite's operational parameters to generate a time distribution sequence. The topographic slope map extracts slope angle and aspect parameters using a digital elevation model. Rock mass structural characteristic parameters include rock mass fracture density, weathering degree, and shear strength. Deformation sensitivity grading classifies rock mass stability into three levels: stable, substable, and unstable. Topographic complexity is categorized into low-slope, medium-slope, and high-slope regions based on slope angle. The phase unwrapping priority weight coefficient is dynamically adjusted based on the grading results, with higher weight coefficients for unstable rock mass regions and higher weight coefficients for high-slope regions than low-slope regions. Interferometric baseline optimization and ranking uses a baseline length threshold and topographic relief to construct an optimization function. Atmospheric variation characteristics are calculated using water vapor pressure data to determine atmospheric phase noise levels, and the optimal interferometric pair combination sequence is selected based on topographic relief.

[0076] Specifically, a time distribution sequence is generated based on satellite orbital parameters and revisit cycles to ensure that the time distribution of observation control points is synchronized with the satellite transit cycle, avoiding decoherence caused by excessively long time baselines. The extraction of topographic slope maps and rock mass structural characteristic parameters provides a quantitative basis for subsequent deformation sensitivity classification. By dividing rock mass stability and topographic complexity into multi-level indicators, a mapping relationship between deformation sensitivity and phase unwrapping priority is established, giving higher unwrapping weights to areas with unstable rock masses or complex topography, prioritizing the processing of phase data from highly sensitive areas. The dynamic allocation of phase unwrapping priority weight coefficients ensures that limited computational resources are concentrated in key areas, improving unwrapping efficiency. During the interferometric baseline optimization and sorting process, topographic relief is used to correct the baseline length threshold, reducing the impact of geometric distortion caused by elevation differences, and atmospheric variation characteristics are used to screen interferometric pairs with lower atmospheric noise, thereby generating the optimal interferometric pair combination sequence. This sequence effectively suppresses phase unwrapping errors by balancing baseline length, topographic relief, and atmospheric noise, improving the reliability and accuracy of deformation monitoring results.

[0077] As a preferred embodiment, the solution of this application is specifically implemented as follows: The spatiotemporal sequencing operation involves generating a time distribution sequence of observation control points based on satellite orbital parameters and revisit periods. First, satellite orbital parameters are obtained, including satellite altitude, inclination, and orbital period. Then, the sampling time for each observation control point is calculated based on the satellite revisit period. For example, for the Sentinel-1 satellite, with a revisit period of 12 days, a time series containing multiple 12-day intervals can be generated.

[0078] Next, the topographic slope map and rock mass structural characteristic parameters of the slope area are extracted. Using digital elevation model (DEM) data, the slope distribution of the slope area is calculated. Simultaneously, rock mass structural characteristic parameters, such as rock mass integrity index and joint density, are obtained through geological survey data or field investigation.

[0079] Based on the acquired topographic and rock mass parameters, the deformation sensitivity of the time distribution series is classified according to rock mass stability and topographic complexity. For example, slope areas can be divided into three sensitivity levels: high, medium, and low. High-sensitivity areas correspond to steep terrain and fractured rock masses, medium-sensitivity areas correspond to moderate slopes and relatively intact rock masses, and low-sensitivity areas correspond to gentle slopes and intact rock masses.

[0080] Based on the classification results, phase unwrapping priority weight coefficients are assigned to the initial deformation phase sequence. High-sensitivity regions are assigned higher weight coefficients, such as 0.8-1.0; medium-sensitivity regions are assigned medium weight coefficients, such as 0.5-0.7; and low-sensitivity regions are assigned lower weight coefficients, such as 0.2-0.4.

[0081] Finally, interferometric baseline optimization and sorting were performed on the initial deformation phase sequence based on topographic relief and atmospheric variation characteristics. The vertical and temporal baselines for each pair of SAR images were calculated, taking into account the influence of atmospheric phase screen APS. The optimal interferometric pair combination sequence was generated by minimizing baseline decoration correlation and atmospheric effects.

[0082] This application further proposes establishing a deformation correlation network, including configuring coherent domain identifiers for observation control points of adjacent initial deformation phase sequences, calculating spatial distance and phase correlation indices between control points, constructing a distributed phase unwrapping tree structure based on the coherent domain identifiers and optimizing the tree node connection paths, registering unwrapping path topology relationships in the correlation network results and calculating path reliability scores, generating a phase continuity constraint matrix based on the unwrapping path topology relationships and reliability scores, and applying the constraint matrix to the phase unwrapping operation process.

[0083] Among them, the coherence domain identifier is used to identify the set of observation control points with the same interference signal source; the spatial distance and phase correlation index are obtained by calculating the geometric distance and phase similarity between control points; the distributed phase unwrapping tree structure consists of nodes corresponding to multiple coherence domain identifiers, and the node connection path is optimized based on the spatial distance and phase correlation index; the unwrapping path topology records the connection order and hierarchical relationship between different nodes; the path reliability score is calculated by statistically analyzing the phase consistency degree and noise level on the path; the phase continuity constraint matrix contains the weight coefficients and unwrapping priority information of different paths.

[0084] Specifically, when generating the deformation correlation network, firstly, coherence domain identifiers are assigned to observation control points of adjacent initial deformation phase sequences. These identifiers are generated based on the interferometric signal characteristics and spatial location information of SAR images. Subsequently, the spatial distance between each control point is calculated, and their phase similarity is evaluated using phase correlation indices. Based on the coherence domain identifiers, control points with the same identifier are divided into the same subdomain, and a distributed phase unwrapping tree structure is constructed within each subdomain. The optimization of the tree node connection paths is achieved by minimizing the weighted sum of spatial distance and phase correlation indices, ensuring that path selection meets the requirements of terrain continuity and phase stability. The unwrapping path topology is registered in the correlation network results, forming the connection order and hierarchical mapping relationship between nodes. Path reliability scoring is calculated by analyzing the phase residual distribution and noise variance on the path; the scoring results are used to quantify the unwrapping reliability of different paths. Finally, a phase continuity constraint matrix is ​​generated based on the unwrapping path topology and reliability score. This matrix applies continuity constraints during the phase unwrapping operation, ensuring that the unwrapping results meet the requirements of spatial continuity and phase consistency.

[0085] As a preferred embodiment, the scheme of this application is implemented as follows: Observation control points of adjacent initial deformation phase sequences are configured with coherent domain identifiers, which include orbital numbers and interferometric pair combination index information. Spatial distance is calculated using the three-dimensional coordinate differential modulus value, and phase correlation index is quantified using normalized cross-correlation coefficients. The distributed phase unwrapping tree structure consists of multiple child nodes of coherent domains, each child node representing a set of observation control points with the same identifier. The tree node connection path is optimized using a dynamic programming algorithm, prioritizing adjacent nodes with a phase correlation higher than 0.85 and a spatial distance less than 200 meters. The unwrapping path topology is registered to the associated network results in the form of an adjacency matrix, and the path reliability score is calculated using the root mean square error of the phase residual. The phase continuity constraint matrix is ​​generated by weighting the path reliability score with the inverse of the spatial distance, and this matrix is ​​converted into a regularization term and added to the objective function of phase unwrapping.

[0086] This application further proposes configuring coherent domain identifiers for observation control points of adjacent initial deformation phase sequences, calculating the spatial distance and phase correlation index between control points, constructing a distributed phase unwrapping tree structure based on the coherent domain identifiers and optimizing the tree node connection paths, registering the unwrapping path topology relationship in the associated network results and calculating the path reliability score, generating a phase continuity constraint matrix based on the unwrapping path topology relationship and reliability score, and applying the constraint matrix to the phase unwrapping operation process.

[0087] The coherence domain identifiers are assigned by analyzing the interferometric signal sources and spatial coverage of adjacent observation control points, ensuring that control points within the same coherence domain have similar scattering characteristics and phase stability. Spatial distance and phase correlation indices are calculated using the Euclidean distance algorithm and covariance matrix to quantify the spatial proximity and phase variation consistency between control points. The distributed phase unwrapping tree structure is generated using a hierarchical clustering algorithm, and the optimization of tree node connection paths is based on a greedy strategy to select the path with the minimum phase residual. The unwrapping path topology is registered using a graph theory model, and the path reliability score is weighted by combining the phase gradient distribution and the interferometric baseline length. The phase continuity constraint matrix maps the path reliability score to weight coefficients, forming the continuity constraints in the phase unwrapping process.

[0088] Specifically, when generating the deformation correlation network, firstly, coherence domain identifiers are assigned to adjacent observation control points. These identifiers are generated based on the orbital parameters and spatial coverage of SAR images, ensuring that control points within the same coherence domain have consistent scattering characteristics. Subsequently, the spatial distance and phase correlation between control points are calculated. The spatial distance is calculated using three-dimensional coordinate difference, and the phase correlation is evaluated using the covariance matrix to assess the consistency of the interferometric phase. Based on these indicators, a distributed phase unwrapping tree structure is constructed. Nodes in the tree structure represent observation control points, and edges represent possible unwrapping paths. The optimization of the path connections between tree nodes employs a dynamic programming algorithm, selecting the path with the minimum phase residual as the optimal unwrapping path. The topological relationships of the unwrapping paths are registered using an adjacency matrix. The path reliability score is calculated based on the standard deviation of the phase gradient and the length of the interferometric baseline. The score result is used to generate a phase continuity constraint matrix. This matrix serves as a regularization term in the phase unwrapping operation, forcing the unwrapping result to meet the requirements of spatial continuity and path reliability. For example, during phase unwrapping, the weight coefficients of the constraint matrix are introduced into the least squares adjustment model to suppress phase jumps caused by atmospheric disturbances or topographic undulations, thereby improving the continuity of the deformed phase field and the reliability of the unwrapping results.

[0089] As a preferred embodiment, the specific implementation of this application is as follows: A first elevation-constrained surface is generated from the initial deformation phase sequence. The elevation change gradient distribution of the deformation field is obtained by calculating the curvature characteristics of each node on the surface. A second elevation-constrained surface is generated for the phase group to be corrected. The gradient operator is used to analyze the slope change rate of each region of the surface, and the spatial distribution of the phase abrupt change region is extracted. The first and second elevation-constrained surfaces are imported into the finite element analysis module. The fusion weights of the two surfaces are calculated based on the minimum energy principle to generate the fused three-dimensional deformation displacement field. Atmospheric phase correction is performed on the three-dimensional deformation displacement field. The deformation phase and atmospheric noise phase are separated using an atmospheric delay model. Terrain phase compensation is further performed, and the phase distortion caused by terrain undulations is eliminated by combining a digital elevation model. External GNSS monitoring data is introduced, and the corresponding nodes of the GNSS station coordinates and the three-dimensional deformation displacement field are spatially matched to calculate the directional consistency error of the displacement vector. When the error exceeds a preset threshold, the displacement field inversion optimization mechanism is triggered, and the finite element fusion weights are readjusted until the error meets the accuracy requirements.

[0090] This application further proposes a verification process for the dynamic deformation accuracy benchmark, which includes acquiring deformation displacement residual data fed back by the slope early warning system and performing time series modeling, monitoring the spatial continuity index of the stable deformation phase field and calculating the distribution characteristics of the fault zone, quantifying the phase unwrapping confidence parameter and assessing the degree of atmospheric influence; when the deformation displacement residual exceeds the preset residual threshold, activating the deformation control marker insertion mechanism and optimizing the marker density; when the spatial continuity index is lower than the preset continuity threshold, initiating the elevation change vector reconstruction mechanism and recalculating the displacement field; when the phase unwrapping confidence is lower than the preset confidence threshold, triggering the differential interferometric node strengthening process and increasing the number of interferometric pairs; and generating a slope stability report including risk level assessment after all verification parameters meet the standards.

[0091] Specifically, deformation displacement residual data is fed back in real time through the slope early warning system and imported into the time series analysis model. The spatial continuity index is calculated based on the distribution density of the phase field fault zone. The phase unwrapping confidence parameter is quantified by scoring the redundancy and reliability of the unwrapping path through interferometric pairs. The deformation control marker insertion mechanism optimizes the local accuracy of the phase field by dynamically adjusting the spatial distribution density of control points. The elevation change vector reconstruction mechanism updates the displacement field using the finite element inversion method. The differential interferometric node enhancement process improves the unwrapping confidence by increasing the number of interferometric pairs.

[0092] Specifically, the deformation displacement residual data is modeled over time to generate residual fluctuation curves, which are used to identify abnormal periods exceeding a preset residual threshold. The spatial continuity index is calculated by detecting abrupt changes in phase gradient between adjacent control points in the phase field, determining the spatial distribution density and extension length of the fault zone. The phase unwrapping confidence parameter is calculated based on interferometric pair redundancy and the topological relationship of the unwrapping path, where redundancy is assessed through the correlation between the number of interferometric pairs and the baseline length. When the residual exceeds the threshold, deformation control markers are inserted into the phase field; the marker density is dynamically adjusted according to the residual amplitude, and the marker insertion location is preferentially selected in the edge region of the fault zone. When the spatial continuity index is below the threshold, the elevation change vector is reconstructed using the finite element method, and the three-dimensional displacement field is updated using minimum energy constraints. When the confidence level is below the threshold, the number of differential interferometric nodes is increased; the interferometric pair selection criteria are optimized based on baseline length and temporal interval. After all verification parameters pass the preset threshold, a slope stability report is generated based on the deformation rate, cumulative displacement, and fault zone distribution characteristics. The risk level in the report is classified according to the deformation trend direction and the spatial distribution pattern of the displacement vector.

[0093] As a preferred embodiment, the specific implementation of the scheme in this application is as follows: The verification process of the dynamic deformation accuracy benchmark includes acquiring deformation displacement residual data fed back by the slope early warning system and performing time series modeling, monitoring the spatial continuity index of the stable deformation phase field and calculating the distribution characteristics of the fault zone, quantifying the phase unwrapping confidence parameter and assessing the degree of atmospheric influence. When the deformation displacement residual exceeds the preset residual threshold, the deformation control marker insertion mechanism is activated and the marker density is optimized; when the spatial continuity index is lower than the preset continuity threshold, the elevation change vector reconstruction mechanism is initiated and the displacement field is recalculated; when the phase unwrapping confidence is lower than the preset confidence threshold, the differential interferometric node strengthening process is triggered and the number of interferometric pairs is increased; when all verification parameters meet the standards, a slope stability report including risk level assessment is generated. Specifically, the deformation displacement residual data is acquired through the real-time monitoring interface of the slope early warning system, and time series modeling is performed using an autoregressive integral moving average model to extract residual fluctuation characteristics. The calculation of the spatial continuity index is based on the gradient distribution of the stable deformation phase field, and the spatial location and extension length of the fault zone are identified through morphological algorithms. The phase unwrapping confidence parameter was quantified using Monte Carlo simulation, and the atmospheric impact was assessed through correlation analysis between water vapor radiometer data and phase residuals. When the residual threshold was triggered, the spatial distribution density of deformation control markers was optimized using a gradient descent algorithm; when the continuity threshold was triggered, the elevation change vector was reconstructed using a finite element inversion method; and when the confidence threshold was triggered, the number of interferometric nodes was increased using an interferometric pair combination optimization algorithm. The final slope stability report includes risk level classification, deformation trend prediction, and early warning response recommendations.

[0094] Through the above technical solutions, this application effectively solves the problems of insufficient accuracy and lack of verification mechanisms in traditional InSAR technology for dynamic deformation monitoring. By combining multi-dimensional parameter verification with a dynamic optimization mechanism, comprehensive control over the accuracy, continuity, and unwrapping reliability of the deformation phase field is achieved, ensuring that the monitoring results meet the accuracy requirements for slope stability assessment. Residual modeling and fault zone analysis improve the spatial consistency of the deformation displacement field; confidence assessment and atmospheric influence quantification reduce the propagation risk of phase unwrapping errors; and a trigger-based optimization mechanism enables dynamic allocation of monitoring resources and rapid repair of accuracy defects. The resulting slope stability report provides a highly reliable decision-making basis for disaster early warning, improving the reliability and timeliness of deformation monitoring under complex terrain conditions.

[0095] 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.

Claims

1. A method for processing slope deformation monitoring data based on InSAR technology, characterized in that, Perform the following operations: The deformation calculation engine generates observation control points and initial deformation phase sequence of the target slope area in the preset analysis domain. The initial deformation phase sequence is subjected to spatiotemporal sorting operation to establish a deformation correlation network between observation control points and output the correlation network result. The process verifies whether the correlation network results satisfy the dynamic deformation constraint criterion. If they do, observation control point migration processing is performed to form an optimized phase sequence; otherwise, a differential interferometric channel is constructed using adjacent observation control points. Specifically, this includes: detecting whether the phase difference value of each initial deformation phase sequence in the correlation network results exceeds a preset phase tolerance range; when the phase difference value exceeds the phase tolerance range, observation control point migration processing is performed on the initial deformation phase sequence in the analysis domain to generate a phase migration sequence, and phase unwrapping operation is performed on the phase migration sequence to form a first optimized phase sequence; when the phase difference value does not exceed the phase tolerance range, differential interferometric nodes are deployed between adjacent observation control points to generate an interferometric extension sequence, and phase unwrapping operation is performed on the interferometric extension sequence to form a second optimized phase sequence; the second optimized phase sequence is a continuously differentiable deformation surface. Based on the processing strategy of the phase sequence to be optimized, the deformed phase sequence is reconstructed to generate a stable deformed phase field; the processing strategy includes a phase fusion strategy and a phase reconstruction strategy.

2. The method according to claim 1, characterized in that, The generation of observation control points and initial deformation phase sequences includes: acquiring multi-temporal SAR image datasets and optimizing interferometric pair combinations; performing precise orbit correction and de-flattening phase processing on the optimized interferometric pairs; extracting discrete interferometric measurements of the slope area and calculating the spatial coherence matrix; constructing observation control points and initial deformation phase sequences based on spatial coherence matrix mapping in the analysis domain; establishing the phase binding relationship between observation control points and physical monitoring piles, as well as the deformation mapping relationship between the initial deformation phase sequence and the measured deformation field; Alternatively, based on preset deformation model parameters, the initial deformation phase sequence and associated observation control points can be directly configured in the analysis domain, while a historical deformation database can be introduced for model parameter verification and correction.

3. The method according to claim 1, characterized in that, The reconstructed deformed phase sequence includes: when the processing strategy is a phase fusion strategy, weighted fusion processing is performed on all initial deformed phase sequences in the associated network results, phase quality index of each sequence is calculated and fusion weight coefficients are assigned, and multi-source phase data fusion is performed based on the weight coefficients to generate a stable deformed phase field; When the processing strategy is a phase reconstruction strategy, the starting observation control point of the first initial deformation phase sequence and its three-dimensional displacement vector and elevation change rate are collected, and the ending observation control point of the last initial deformation phase sequence and its three-dimensional displacement vector and elevation change rate are collected. Based on the starting observation control point, the starting elevation change vector, the ending observation control point and the ending elevation change vector, a stable deformation phase field is constructed, and a slope geomechanical model is introduced to constrain the deformation trend.

4. The method according to claim 3, characterized in that, The construction of a stable deformable phase field includes: generating an initial phase field surface in the analysis domain based on the spatial coordinates and elevation change rate of the initial observation control point; generating a termination phase field surface based on the spatial coordinates and elevation change rate of the termination observation control point; generating a phase field to be fused based on the initial and termination phase field surfaces; performing spatiotemporal superposition processing on the phase field to be fused and the initial deformable phase sequence to form a phase group to be corrected; and performing elevation constraint processing based on least squares adjustment on the initial deformable phase sequence and the phase group to be corrected to generate a stable deformable phase field that meets the requirements of continuity and smoothness.

5. The method according to claim 4, characterized in that, After the elevation constraint processing is performed, the following steps are continued: extracting the first ordered deformation observation set of the initial deformation phase sequence and the second ordered deformation observation set of the phase group to be corrected; performing time series analysis on the first ordered deformation observation set to extract the deformation trend direction; constructing a normal projection operator based on the deformation trend direction; performing orthogonal projection operation of the deformation field on the second ordered deformation observation set using the normal projection operator to generate the third ordered deformation observation set; and performing atmospheric delay correction and noise filtering processing on the third ordered deformation observation set. The processed third ordered deformation observation set is used as a reliable deformation expression of the stable deformation phase field and transmitted to the slope early warning system for real-time monitoring.

6. The method according to claim 5, characterized in that, After the data is transmitted to the slope early warning system, the following steps are performed: the first deformation accuracy index of the expected deformation expression is compared with the second deformation accuracy index of the initial deformation phase sequence; the deformation rate error and displacement cumulative error are calculated; when the deformation rate error exceeds a preset rate threshold or the displacement cumulative error exceeds a preset displacement threshold, it is determined that the first deformation accuracy index has not reached the dynamic deformation accuracy benchmark; at this time, deformation control markers are inserted into the stable deformation phase field and their spatial distribution density is optimized, or the elevation change vector is reconstructed and the three-dimensional displacement field is recalculated, or the phase unwrapping parameters are adjusted until the slope monitoring accuracy standard is met and the Kolmogorov-Smirnov test is passed.

7. The method according to claim 1, characterized in that, The spatiotemporal sorting operation includes generating a time distribution sequence of observation control points based on satellite orbit parameters and revisit period, extracting topographic slope maps and rock mass structural characteristic parameters of the slope area, classifying the deformation sensitivity of the time distribution sequence according to rock mass stability and topographic complexity, assigning phase unwrapping priority weight coefficients to the initial deformation phase sequence according to the classification results, and finally performing interferometric baseline optimization sorting on the initial deformation phase sequence based on topographic relief and atmospheric change characteristics to generate the optimal interferometric pair combination sequence.

8. The method according to claim 1, characterized in that, The establishment of the deformation correlation network includes configuring coherent domain identifiers for observation control points of adjacent initial deformation phase sequences, calculating the spatial distance and phase correlation index between control points, constructing a distributed phase unwrapping tree structure based on the coherent domain identifiers and optimizing the tree node connection paths, registering the unwrapping path topology in the correlation network results and calculating the path reliability score, generating a phase continuity constraint matrix based on the unwrapping path topology and reliability score, and applying the constraint matrix to the phase unwrapping operation process.

9. The method according to claim 4, characterized in that, The elevation constraint processing includes generating a first elevation constraint surface for the initial deformation phase sequence and calculating its curvature characteristics. A second elevation constraint surface is generated for the phase group to be corrected and its gradient distribution is calculated. The first and second elevation constraint surfaces are then fused using the minimum energy method based on the finite element method. Based on the fusion result, the three-dimensional deformation displacement field of the slope is inverted and the displacement vector direction is calculated. Finally, atmospheric phase correction and terrain phase compensation are performed on the three-dimensional deformation displacement field, and external GNSS monitoring data is introduced to verify the results.

10. The method according to claim 6, characterized in that, The verification process of the dynamic deformation accuracy benchmark includes acquiring deformation displacement residual data fed back by the slope early warning system and performing time series modeling, monitoring the spatial continuity index of the stable deformation phase field and calculating the distribution characteristics of the fault zone, quantifying the phase unwrapping confidence parameter and assessing the degree of atmospheric influence; when the deformation displacement residual exceeds the preset residual threshold, the deformation control marker insertion mechanism is activated and the marker density is optimized. When the spatial continuity index is lower than the preset continuity threshold, the elevation change vector reconstruction mechanism is activated and the displacement field is recalculated; when the phase unwrapping confidence level is lower than the preset confidence threshold, the differential interferometric node strengthening process is triggered and the number of interferometric pairs is increased; when all verification parameters meet the standards, a slope stability report containing risk level assessment is generated.

Images