High-precision monitoring and prediction of mining area surface subsidence using SBAS-InSAR and CNN-...
High-precision monitoring and prediction of mining area surface subsidence using SBAS-InSAR and CNN-BiGRU-attention model
About this item
Full title
Author / Creator
Zhu, Mingfei , Yu, Xuexiang , Tan, Hao , Yuan, Jiajia , Chen, Kai , Xie, Shicheng , Han, Yuchen and Long, Wenjiang
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Coal mining-induced surface subsidence can significantly impact resident safety and hinder regional sustainable development, making precise subsidence monitoring and prediction critical. Existing mining subsidence monitoring technologies often exhibit low spatiotemporal resolution, while subsidence prediction models suffer from heavy dependence on...
Alternative Titles
Full title
High-precision monitoring and prediction of mining area surface subsidence using SBAS-InSAR and CNN-BiGRU-attention model
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_d23fd18b4e2744a5a71c11a04d5a78fd
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d23fd18b4e2744a5a71c11a04d5a78fd
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-80446-7
