Articles | Volume 21, issue 1
https://doi.org/10.5194/nhess-21-339-2021
https://doi.org/10.5194/nhess-21-339-2021
Research article
 | 
27 Jan 2021
Research article |  | 27 Jan 2021

Near-real-time automated classification of seismic signals of slope failures with continuous random forests

Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter

Viewed

Total article views: 2,664 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,771 851 42 2,664 57 48
  • HTML: 1,771
  • PDF: 851
  • XML: 42
  • Total: 2,664
  • BibTeX: 57
  • EndNote: 48
Views and downloads (calculated since 08 Jul 2020)
Cumulative views and downloads (calculated since 08 Jul 2020)

Viewed (geographical distribution)

Total article views: 2,664 (including HTML, PDF, and XML) Thereof 2,553 with geography defined and 111 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 04 Oct 2023
Download
Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Altmetrics
Final-revised paper
Preprint