A fine subsidence information extraction model based on multi-source inversion by integrating InSAR and leveling data

Hui Liu (), Mei Li, Mingze Yuan, Ben Li and Xiao Jiang
Additional contact information
Hui Liu: Nanjing University of Information Science & Technology
Mei Li: Peking University
Mingze Yuan: Peking University
Ben Li: Peking University
Xiao Jiang: Peking University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 114, issue 3, No 18, 2839-2854

Abstract: Abstract Subsidence information is of paramount significance for the restoration and treatment of geological destruction in mining areas. However, the traditional interferometric synthetic aperture radar (InSAR) technique is incapable of detecting mining subsidence with large gradient deformation. To overcome this limitation, this study proposes a fine subsidence information extraction model to improve the subsidence detection ability. The model consists of the data integration, multi-source parameter inversion, and subsidence extraction method. The integration of small baseline subset InSAR (SBAS-InSAR) and leveling data allows the parameters to be accurately inversed and predict the subsidence funnel based on the probability integration method (PIM). By fusing the subsidence funnel and SBAS-InSAR data based on geographically weighted regression, the fine subsidence information is extracted. Model validation with a certain working face of coal mine in China shows that the proposed model is significantly efficient in detecting large deformation in the goaf center, as well as acquiring more detailed information for goaf boundary areas, indicating its superiority compared with SBAS-InSAR and PIM. The proposed model is not only a low-cost and practical way of parameter acquisition, but also a promising method for obtaining more accurate and reliable subsidence information, and is expected to provide decision support for geological disasters in mining areas.

Keywords: Large gradient deformation; SBAS-InSAR; Data integration; Multi-source parameter inversion; Fine subsidence information (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-022-05494-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:114:y:2022:i:3:d:10.1007_s11069-022-05494-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-022-05494-6

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().