A geological disaster early warning method based on InSAR and deep learning

By combining InSAR with deep learning, the problems of spatial bias and insufficient timeliness in geological disaster early warning have been solved, achieving more accurate segmentation of deformation anomaly zones and dynamic early warning, thus improving the spatial matching and timeliness of geological disaster early warning.

CN122176872BActive Publication Date: 2026-07-17安徽省第一测绘院
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
安徽省第一测绘院
Filing Date
2026-05-13
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

In existing technologies, the identification of geological hazard risks based on radar interferometry suffers from spatial bias and delayed early warning judgment, making it difficult to fully reflect slope deformation changes and lacking timeliness.

Method used

By combining InSAR and deep learning, SBAS-InSAR calculation is performed by collecting multi-source data to generate line-of-sight deformation variables. The MaskRCNN model is then used to segment deformation anomalies and generate dynamic early warning results, enhancing spatial matching and timeliness.

Benefits of technology

It improves the spatial matching and timeliness of geological disaster early warning, reduces early warning deviation, and enhances the ability to identify slope deformation direction and link multi-source induced information.

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Abstract

This invention discloses a geological disaster early warning method based on InSAR and deep learning, belonging to the field of disaster early warning technology. The method includes: collecting geological disaster monitoring data, including L-SAR ascending and descending trajectory data, DEM data, optical image data, and disaster-induced data; performing SBAS-InSAR calculation on the L-SAR ascending and descending trajectory data to generate ascending and descending trajectory line-of-sight deformation variables; performing temporal interpolation registration and spatial homologous pixel registration on the ascending and descending trajectory line-of-sight deformation variables to generate registered line-of-sight deformation variables; calculating a two-dimensional deformation field and slope aspect deformation variables based on the registered line-of-sight deformation variables and DEM data; rasterizing the slope aspect deformation variables to obtain a slope aspect deformation map, which is then input into a MaskRCNN model improved based on the Involution operator and deformable pooling module to generate deformation anomaly segmentation results. This invention improves the identification of the true deformation direction of the slope and the linkage capability of multi-source induced information, enhancing the spatial matching and timeliness of dynamic geological disaster early warning.
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