Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska
SUMMARY Quantifying landslide activity in remote regions is difficult because of the numerous complications that prevent direct landslide observations. However, building exhaustive landslide catalogues is critical to document and assess the impacts of climate change on landslide activity such as inc...
Published in: | Geophysical Journal International |
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Oxford University Press (OUP)
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Online Access: | http://dx.doi.org/10.1093/gji/ggz354 http://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggz354/29024913/ggz354.pdf http://academic.oup.com/gji/article-pdf/219/2/1138/29309917/ggz354.pdf |
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croxfordunivpr:10.1093/gji/ggz354 2024-10-13T14:10:15+00:00 Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska Hibert, C Michéa, D Provost, F Malet, J-P Geertsema, M French National Research Agency Hydrogeophysical Monitoring of Clayey Landslides French National Institute of Sciences of the Universe 2019 http://dx.doi.org/10.1093/gji/ggz354 http://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggz354/29024913/ggz354.pdf http://academic.oup.com/gji/article-pdf/219/2/1138/29309917/ggz354.pdf en eng Oxford University Press (OUP) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model Geophysical Journal International volume 219, issue 2, page 1138-1147 ISSN 0956-540X 1365-246X journal-article 2019 croxfordunivpr https://doi.org/10.1093/gji/ggz354 2024-09-17T04:28:31Z SUMMARY Quantifying landslide activity in remote regions is difficult because of the numerous complications that prevent direct landslide observations. However, building exhaustive landslide catalogues is critical to document and assess the impacts of climate change on landslide activity such as increasing precipitation, glacial retreat and permafrost thawing, which are thought to be strong drivers of the destabilization of large parts of the high-latitude/altitude regions of the Earth. In this study, we take advantage of the capability offered by seismological observations to continuously and remotely record landslide occurrences at regional scales. We developed a new automated machine learning processing chain, based on the Random Forest classifier, able to automatically detect and identify landslide seismic signals in continuous seismic records. We processed two decades of continuous seismological observations acquired by the Alaskan seismic networks. This allowed detection of 5087 potential landslides over a period of 22 yr (1995–2017). We observe an increase in the number of landslides for the period and discuss the possible causes. Article in Journal/Newspaper permafrost Alaska Oxford University Press Geophysical Journal International 219 2 1138 1147 |
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Open Polar |
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Oxford University Press |
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croxfordunivpr |
language |
English |
description |
SUMMARY Quantifying landslide activity in remote regions is difficult because of the numerous complications that prevent direct landslide observations. However, building exhaustive landslide catalogues is critical to document and assess the impacts of climate change on landslide activity such as increasing precipitation, glacial retreat and permafrost thawing, which are thought to be strong drivers of the destabilization of large parts of the high-latitude/altitude regions of the Earth. In this study, we take advantage of the capability offered by seismological observations to continuously and remotely record landslide occurrences at regional scales. We developed a new automated machine learning processing chain, based on the Random Forest classifier, able to automatically detect and identify landslide seismic signals in continuous seismic records. We processed two decades of continuous seismological observations acquired by the Alaskan seismic networks. This allowed detection of 5087 potential landslides over a period of 22 yr (1995–2017). We observe an increase in the number of landslides for the period and discuss the possible causes. |
author2 |
French National Research Agency Hydrogeophysical Monitoring of Clayey Landslides French National Institute of Sciences of the Universe |
format |
Article in Journal/Newspaper |
author |
Hibert, C Michéa, D Provost, F Malet, J-P Geertsema, M |
spellingShingle |
Hibert, C Michéa, D Provost, F Malet, J-P Geertsema, M Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska |
author_facet |
Hibert, C Michéa, D Provost, F Malet, J-P Geertsema, M |
author_sort |
Hibert, C |
title |
Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska |
title_short |
Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska |
title_full |
Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska |
title_fullStr |
Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska |
title_full_unstemmed |
Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska |
title_sort |
exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in alaska |
publisher |
Oxford University Press (OUP) |
publishDate |
2019 |
url |
http://dx.doi.org/10.1093/gji/ggz354 http://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggz354/29024913/ggz354.pdf http://academic.oup.com/gji/article-pdf/219/2/1138/29309917/ggz354.pdf |
genre |
permafrost Alaska |
genre_facet |
permafrost Alaska |
op_source |
Geophysical Journal International volume 219, issue 2, page 1138-1147 ISSN 0956-540X 1365-246X |
op_rights |
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
op_doi |
https://doi.org/10.1093/gji/ggz354 |
container_title |
Geophysical Journal International |
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219 |
container_issue |
2 |
container_start_page |
1138 |
op_container_end_page |
1147 |
_version_ |
1812817430941007872 |