Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic...
Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring
About this item
Full title
Author / Creator
Publisher
Switzerland: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Switzerland: MDPI AG
Subjects
More information
Scope and Contents
Contents
A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical process...
Alternative Titles
Full title
Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_808f92faa5674acf986ade204d72c72b
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_808f92faa5674acf986ade204d72c72b
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s24154892
