Ground surface settlement monitoring method based on synthetic aperture radar image

By combining the ridge regression model and the autoregressive integrated moving average model, the problem of missing down-orbit data in D-InSAR is solved, and surface deformation monitoring with single-track data is realized, especially high-precision monitoring in areas where down-orbit data is missing.

CN120702416APending Publication Date: 2025-09-26YUNNAN DIANDONG YUWANG ENERGY CO LTD +2
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Patent Information

Application Number
CN202510677340.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-26
Publication Date
2025-09-26

AI Technical Summary

Technical Problem

Existing D-InSAR technology relies on the complementarity of ascending and descending orbit data, but Sentinel satellites can only obtain ascending orbit data in most areas and lack descending orbit data, resulting in deviations or omissions in surface deformation monitoring results, affecting accuracy.

Method used

A ridge regression model is combined with an autoregressive integrated moving average model, and only the ascending orbit value is processed. The trained model is used to perform real-time ascending and descending orbit fusion to obtain surface settlement and deformation information.

Benefits of technology

It breaks through the dependence of traditional InSAR dual-track fusion, is suitable for areas where down-track data is missing, and realizes surface deformation monitoring with single-track data, especially high-precision monitoring in basin boundaries, mining areas, polar regions and mountainous areas.

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Abstract

The invention discloses a ground surface settlement monitoring method based on synthetic aperture radar images, and relates to the technical field of ground surface deformation monitoring. Comprising the following steps: acquiring a real-time rail lifting value of a to-be-processed synthetic aperture radar image obtained by rail lifting imaging; inputting the real-time rail lifting value into the trained ridge regression model to obtain a real-time rail lifting fusion preliminary prediction value; carrying out modeling on a residual error generated by the trained ridge regression model in a prediction process through the trained autoregression integrated moving average model to obtain a real-time prediction residual error; and superposing the real-time lifting rail fusion preliminary prediction value and the real-time prediction residual error to obtain a real-time lifting rail fusion final prediction value, and using the real-time lifting rail fusion final prediction value to extract ground surface settlement deformation information. According to the method, the rail lifting fusion value can be obtained only by processing the rail lifting value, the dependency of traditional InSAR double-rail fusion is broken through, and a new normal form is provided for single-rail data deformation monitoring.
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