CN-121995369-A - Deep foundation pit micro-deformation InSAR remote sensing method, system and storage medium

CN121995369ACN 121995369 ACN121995369 ACN 121995369ACN-121995369-A

Abstract

The application relates to the technical field of deep foundation pit monitoring, and discloses a deep foundation pit micro-deformation InSAR remote sensing method, a deep foundation pit micro-deformation InSAR remote sensing system and a storage medium. The method comprises the steps of synchronously collecting data through a ground radar and an InSAR satellite, obtaining a point target set and an original scattering characteristic sequence, obtaining a net deformation phase sequence without influence through registration residual correction, phase subtraction and weighted fusion processing, carrying out phase consistency quality control and unwrapping processing, obtaining an accumulated deformation sequence, constructing a deformation mechanism association characteristic matrix based on association analysis of the accumulated deformation sequence and radar reflection intensity amplitude attenuation characteristics, fusing the deformation mechanism association characteristic matrix with a real-time differential interference image group, extracting deformation rate and phase gradient change, and further generating a deep foundation pit local micro deformation risk early warning index set. The method can effectively detect the micro deformation of the surrounding area of the foundation pit, early warn the potential risk in advance and improve the safety and monitoring efficiency in the construction process.

Inventors

ZHENG JUNJIE
ZHENG YUTAO
CUI YACHANG
LI WEI
HU ZEHUA
HOU XIAOYU
SUN HAILI

Assignees

河南航兴建筑工程有限公司

Dates

Publication Date: 20260508
Application Date: 20260226

Claims (10)

1. The deep foundation pit micro-deformation InSAR remote sensing method is characterized by comprising the following steps of: S1, synchronously acquiring a continuous reflection signal and a differential interference image group of a deep foundation pit region through a ground radar and an InSAR satellite to acquire a point target set and an original scattering characteristic sequence with high stability; s2, performing registration residual correction and phase subtraction processing according to the point target set and the original scattering characteristic sequence to obtain a combined time sequence characteristic sequence fusing radar scattering and InSAR phases; s3, carrying out phase noise evaluation and weighted fusion on the combined time sequence characteristic sequence, separating an atmospheric delay phase and a real deformation phase, and obtaining a net deformation phase sequence from which the atmospheric influence is removed; s4, performing phase consistency quality control and phase unwrapping treatment on the net deformation phase sequence to obtain a continuous and reliable accumulated deformation sequence; s5, performing association analysis on the accumulated deformation sequence and radar reflected signal amplitude attenuation characteristics to construct a deformation mechanism association characteristic matrix; s6, performing deviation calculation and secondary quality control on the deformation mechanism association characteristic matrix to obtain an updated deformation mechanism association characteristic matrix; And S7, performing space-time fusion on the updated deformation mechanism association characteristic matrix and the real-time differential interference image group, extracting deformation rate and phase gradient change at the current moment, and generating a deep foundation pit local micro-deformation risk early warning index set.
2. The method of claim 1, wherein S1 comprises: calculating coherence coefficients of each pixel point at intervals of a plurality of times on the basis of the differential interference image group for the enclosure structure and the soil surface of the deep foundation pit area; Comparing the coherence coefficient with a preset coherence threshold value, and screening out pixel points higher than the preset coherence threshold value to serve as candidate permanent scatterers; Performing time sequence verification on the candidate permanent scatterers, and identifying the surface permanent scatterers based on verification results to form the point target set; Extracting a continuous time series of radar reflection intensity values and radar phase values from the continuous reflection signal for each target point in the set of point targets; The radar reflection intensity values and radar phase values at each time point are aligned and integrated in time order to form the original scattering property sequence.
3. The method of claim 1, wherein S2 comprises: performing spatial registration residual error correction on the differential interference image group by adopting a main-auxiliary image registration method to obtain a registered differential interference image group; performing track error and terrain phase subtraction processing on the registered differential interference image group based on a reference ellipsoid model and a digital elevation model to obtain a corrected InSAR phase sequence; Calculating a time sequence phase stability index based on the corrected InSAR phase sequence, and extracting a radar original amplitude sequence corresponding to each point target from the original scattering characteristic sequence; and based on the time sequence phase stability index, fusing the radar original amplitude sequence and the corrected InSAR phase sequence to generate the joint time sequence characteristic sequence.
4. The method of claim 1, wherein S3 comprises: calculating a time sequence coherence index of each point target based on the InSAR phase sequence aiming at each point target in the combined time sequence feature sequence; Carrying out fusion processing on the InSAR phase sequence in multiple phases by adopting a coherence weighting method to obtain a fusion phase sequence after noise suppression; For the fusion phase sequence, separating out an atmospheric delay phase component through spatial low-pass filtering and time sequence analysis; and subtracting the atmospheric delay phase component from the InSAR phase in the combined time sequence characteristic sequence to obtain the net deformation phase sequence.
5. The method of claim 1, wherein S4 comprises: Aiming at a point target group in the building envelope area, based on the net deformation phase sequence, respectively calculating the standard deviation of the phase sequence corresponding to each point target as a phase consistency quality control index of the point targets; When the phase consistency quality control index of any point target is higher than a preset index threshold, detecting a jump point of a phase sequence corresponding to any point target so as to identify mutation or abnormal data possibly occurring in the deformation process; And carrying out differential interference phase unwrapping processing on the detected phase jump points by adopting a minimum cost flow method or a statistical inspection method, eliminating the influence of phase jump and obtaining the accumulated deformation sequence.
6. The method of claim 1, wherein S5 comprises: based on the accumulated deformation sequence and satellite geometric parameters, performing deformation decomposition projection to obtain a projection result of deformation components; respectively extracting a horizontal displacement component sequence of the enclosure structure and a vertical uplift component sequence of the soil body from the projection result; Extracting radar reflection intensity value sequences corresponding to each point target from the original scattering characteristic sequences, and calculating an amplitude attenuation ratio sequence based on the radar reflection intensity value sequences; Performing time sequence alignment and association analysis on the horizontal displacement component sequence, the vertical bulge component sequence and the amplitude attenuation ratio sequence, and establishing a corresponding relation between deformation components and amplitude attenuation characteristics; And constructing a deformation mechanism association characteristic matrix containing various deformation component association relations based on the corresponding relations and the projection results.
7. The method of claim 1, wherein S6 comprises: Comparing the deformation characteristic values of each point target in the deformation mechanism association characteristic matrix with corresponding phase stability reference values in the historical time sequence phase stability sequence, and calculating the characteristic deviation value of each point target; When the characteristic deviation value exceeds a preset deviation threshold value, marking the corresponding point target as an abnormal point target; based on the differential interference image group, performing track error polynomial fitting removal processing on each abnormal point target, eliminating the influence of track errors on phase data, and acquiring a corrected phase data sequence; Based on the corrected phase data sequence, obtaining a phase consistency index of each point target by calculating a phase change gradient of each point target between adjacent time nodes; screening out an effective point target with the phase consistency index smaller than a preset quality threshold value, and generating an updated deformation mechanism association characteristic matrix based on the effective point target.
8. The method of claim 1, wherein S7 comprises: Performing space-time alignment and fusion processing on the updated deformation mechanism association characteristic matrix and the newly acquired real-time differential interference image group to acquire a characteristic data field; Calculating the line-of-sight deformation rate of each point target at the current moment based on the characteristic data field, and calculating the phase gradient change rate in the corresponding local area in the characteristic data field by taking the local area formed by each point target and the space neighborhood thereof as an analysis unit; comparing the line-of-sight deformation rate and the phase gradient change rate with a preset rate threshold and a preset change rate threshold respectively, and carrying out risk classification on a plurality of local areas in the deep foundation pit based on a comparison result; And generating a deep foundation pit local micro-deformation risk early warning index set containing a plurality of local area risk level information based on the risk level division and the spatial position information of the local area.
9. A deep foundation pit micro-deformation InSAR remote sensing system for implementing the method of any one of claims 1 to 8, the system comprising: The data acquisition module is used for synchronously acquiring continuous reflection signals and differential interference image groups for the deep foundation pit region through ground radars and InSAR satellites to acquire a point target set and an original scattering characteristic sequence with high stability; The correction fusion module is used for carrying out registration residual correction and phase subtraction processing according to the point target set and the original scattering characteristic sequence to obtain a combined time sequence characteristic sequence of fusion radar scattering and InSAR phase; the denoising separation module is used for carrying out phase noise evaluation and weighted fusion on the combined time sequence characteristic sequence, separating an atmospheric delay phase from a real deformation phase and obtaining a net deformation phase sequence after the atmospheric influence is removed; the unwrapping integration module is used for carrying out phase consistency quality control and phase unwrapping treatment on the net deformation phase sequence to obtain a continuous and reliable accumulated deformation sequence; The matrix construction module is used for carrying out association analysis on the accumulated deformation sequence and the radar reflection signal amplitude attenuation characteristic to construct a deformation mechanism association characteristic matrix; the deviation updating module is used for carrying out deviation calculation and secondary quality control on the deformation mechanism association characteristic matrix to obtain an updated deformation mechanism association characteristic matrix; And the early warning output module is used for carrying out space-time fusion on the updated deformation mechanism association characteristic matrix and the real-time differential interference image group, extracting the deformation rate and the phase gradient change at the current moment and generating a deep foundation pit local micro deformation risk early warning index set.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement a deep foundation pit micro-deformation InSAR remote sensing method according to any one of claims 1 to 8.

Description

Deep foundation pit micro-deformation InSAR remote sensing method, system and storage medium Technical Field The application relates to the technical field of deep foundation pit monitoring, in particular to a deep foundation pit micro-deformation InSAR remote sensing method, a deep foundation pit micro-deformation InSAR remote sensing system and a storage medium. Background The deep foundation pit construction is taken as an indispensable engineering link in modern city construction, and along with the continuous increase of building depth and scale, the requirement for foundation pit deformation monitoring is growing. The traditional foundation pit deformation monitoring method, such as ground subsidence observation, laser scanning and the like, can provide certain deformation information, but has the limitations of uneven setting of monitoring points, long data acquisition period, large influence by environmental interference and the like, and is difficult to meet the accurate monitoring requirement of large-scale deep foundation pit engineering. With the development of remote sensing technology, especially the continuous maturation of InSAR (synthetic aperture radar interferometry) technology, the monitoring method of foundation pit deformation is significantly improved. The InSAR technology can realize high-precision and high-spatial-resolution ground deformation monitoring by analyzing the phase change of radar wave reflected signals. Especially in deep foundation pit engineering, inSAR can realize synchronous monitoring in a wide area, and point location limitation in the traditional method is overcome. However, in the existing InSAR monitoring method in deep foundation pit micro-deformation monitoring, the technical problems of atmospheric interference, radar signal noise, data processing complexity and the like still face, and multi-dimensional monitoring and early warning with strong real-time performance and high precision cannot be realized. Therefore, how to combine the ground radar and InSAR remote sensing technology, accurately monitor the micro deformation of the deep foundation pit, and effectively evaluate and early warn the risk on the basis of real-time monitoring becomes a key technical problem to be solved in the field. Disclosure of Invention The application provides a deep foundation pit micro-deformation InSAR remote sensing method, a deep foundation pit micro-deformation InSAR remote sensing system and a deep foundation pit micro-deformation InSAR remote sensing storage medium, and aims to solve the problems of poor real-time performance, precision, operability and the like of deep foundation pit deformation monitoring in the prior art. In a first aspect, the application provides a deep foundation pit micro-deformation InSAR remote sensing method, which comprises the following steps: S1, synchronously acquiring a continuous reflection signal and a differential interference image group of a deep foundation pit region through a ground radar and an InSAR satellite to acquire a point target set and an original scattering characteristic sequence with high stability; s2, performing registration residual correction and phase subtraction processing according to the point target set and the original scattering characteristic sequence to obtain a combined time sequence characteristic sequence fusing radar scattering and InSAR phases; S3, carrying out phase noise evaluation and weighted fusion on the combined time sequence characteristic sequence, separating an atmospheric delay phase and a real deformation phase, and obtaining a net deformation phase sequence from which the atmospheric influence is removed; s4, performing phase consistency quality control and phase unwrapping treatment on the net deformation phase sequence to obtain a continuous and reliable accumulated deformation sequence; s5, performing association analysis on the accumulated deformation sequence and radar reflected signal amplitude attenuation characteristics to construct a deformation mechanism association characteristic matrix; s6, performing deviation calculation and secondary quality control on the deformation mechanism association characteristic matrix to obtain an updated deformation mechanism association characteristic matrix; and S7, performing space-time fusion on the updated deformation mechanism association characteristic matrix and the real-time differential interference image group, extracting deformation rate and phase gradient change at the current moment, and generating a deep foundation pit local micro-deformation risk early warning index set. In a second aspect, the application provides a deep foundation pit micro-deformation InSAR remote sensing system, which comprises: The data acquisition module is used for synchronously acquiring continuous reflection signals and differential interference image groups for the deep foundation pit region through ground radars and InSAR satellites to acquire a point ta