一种基于数据融合的地表沉降监测方法、装置和设备
By using a coherence-based partitioning and weighted fusion method, and utilizing UAV-LiDAR and SBAS-InSAR data, the problem of insufficient accuracy in surface subsidence monitoring was solved, and a high-precision subsidence field was constructed in the goaf, thereby improving the accuracy and consistency of monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF MINING & TECH
- Filing Date
- 2025-04-16
- Publication Date
- 2026-07-17
AI Technical Summary
In existing technologies, a single error assessment method is insufficient to fully reflect the differences in accuracy requirements for data fusion-based surface subsidence monitoring, resulting in insufficient monitoring accuracy in both high and low gradient regions, especially prone to misjudgment in the boundary areas of mining subsidence areas.
By acquiring settlement values from UAV-LiDAR and SBAS-InSAR, high coherence, low coherence, and transition regions are delineated using coherence coefficients. Weighted fusion and regression models are used to optimize settlement information in the transition region. The regression coefficients are determined by combining the least squares method, and linear superposition is performed to construct a complete settlement field.
It improves the accuracy and reliability of surface subsidence monitoring, especially in transition areas, reduces local errors, constructs smoother and more reasonable subsidence information, and enhances monitoring accuracy and completeness.
Smart Images
Figure CN120385313B_ABST