Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis
International audience Polar regions and Greenland in particular are highly sensitive to global warming. Impacts on Greenland's glaciers may be observed through the increasing number of calving events. However, a direct assessment of the calving activity is limited due to the remoteness of pola...
Published in: | Geophysical Journal International |
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Online Access: | https://insu.hal.science/insu-04462200 https://insu.hal.science/insu-04462200/document https://insu.hal.science/insu-04462200/file/ggad402.pdf https://doi.org/10.1093/gji/ggad402 |
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ftinsu:oai:HAL:insu-04462200v1 2024-04-14T08:08:34+00:00 Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis Pirot, Emilie Hibert, Clément Mangeney, Anne Institut de Physique du Globe de Paris (IPGP (UMR_7154)) Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) Institut Terre Environnement Strasbourg (ITES) École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS) 2024 https://insu.hal.science/insu-04462200 https://insu.hal.science/insu-04462200/document https://insu.hal.science/insu-04462200/file/ggad402.pdf https://doi.org/10.1093/gji/ggad402 en eng HAL CCSD Oxford University Press (OUP) info:eu-repo/semantics/altIdentifier/doi/10.1093/gji/ggad402 insu-04462200 https://insu.hal.science/insu-04462200 https://insu.hal.science/insu-04462200/document https://insu.hal.science/insu-04462200/file/ggad402.pdf BIBCODE: 2024GeoJI.236.849P doi:10.1093/gji/ggad402 info:eu-repo/semantics/OpenAccess ISSN: 0956-540X EISSN: 1365-246X Geophysical Journal International https://insu.hal.science/insu-04462200 Geophysical Journal International, 2024, 236, pp.849-871. ⟨10.1093/gji/ggad402⟩ Arctic region Machine learning Glaciology Seismology [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2024 ftinsu https://doi.org/10.1093/gji/ggad402 2024-03-21T17:00:57Z International audience Polar regions and Greenland in particular are highly sensitive to global warming. Impacts on Greenland's glaciers may be observed through the increasing number of calving events. However, a direct assessment of the calving activity is limited due to the remoteness of polar regions and the cloudy weather which makes impossible a recurrent observation through satellite imagery. To tackle this issue, we exploit the seismological network deployed in Greenland which actively records seismic signals associated with calving events, hereinafter referred to as glacial earthquakes. These seismic signals present a broad frequency range and a wide diversity of waveform which make them difficult to discriminate from tectonic events as well as anthropogenic and natural noises. In this study, we start from two catalogues of known events, one for glacial earthquake events which occurred between 1993 and 2013 and one for earthquakes which occurred in the same time period, and we implement a detection algorithm based on the STA/LTA method to extract signals' events from continuous data. Then, we train and test a machine learning processing chain based on the Random Forest algorithm which allows us to automatically associate the events respectively with calving and tectonic activity, with a certain probability. Finally, we investigate 844 selected days spanning time of continuous data from the Greenland regional seismic network which results in a new, more exhaustive, catalogue of glacial earthquakes expanded of 1633 newly detected glacial events. Moreover, we extensively discuss the choice of the features used to describe glacial earthquakes, in particular the 39 new features created in this study which have drastically improved our results with 7 of the 10 best features being in the added set. The perspective of further expansion of the glacial earthquake catalogue applying the processing chain discussed in this paper on different time spans highlights how combining seismology and machine learning can ... Article in Journal/Newspaper Arctic Global warming Greenland Institut national des sciences de l'Univers: HAL-INSU Arctic Greenland Geophysical Journal International 236 2 849 871 |
institution |
Open Polar |
collection |
Institut national des sciences de l'Univers: HAL-INSU |
op_collection_id |
ftinsu |
language |
English |
topic |
Arctic region Machine learning Glaciology Seismology [SDU]Sciences of the Universe [physics] |
spellingShingle |
Arctic region Machine learning Glaciology Seismology [SDU]Sciences of the Universe [physics] Pirot, Emilie Hibert, Clément Mangeney, Anne Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis |
topic_facet |
Arctic region Machine learning Glaciology Seismology [SDU]Sciences of the Universe [physics] |
description |
International audience Polar regions and Greenland in particular are highly sensitive to global warming. Impacts on Greenland's glaciers may be observed through the increasing number of calving events. However, a direct assessment of the calving activity is limited due to the remoteness of polar regions and the cloudy weather which makes impossible a recurrent observation through satellite imagery. To tackle this issue, we exploit the seismological network deployed in Greenland which actively records seismic signals associated with calving events, hereinafter referred to as glacial earthquakes. These seismic signals present a broad frequency range and a wide diversity of waveform which make them difficult to discriminate from tectonic events as well as anthropogenic and natural noises. In this study, we start from two catalogues of known events, one for glacial earthquake events which occurred between 1993 and 2013 and one for earthquakes which occurred in the same time period, and we implement a detection algorithm based on the STA/LTA method to extract signals' events from continuous data. Then, we train and test a machine learning processing chain based on the Random Forest algorithm which allows us to automatically associate the events respectively with calving and tectonic activity, with a certain probability. Finally, we investigate 844 selected days spanning time of continuous data from the Greenland regional seismic network which results in a new, more exhaustive, catalogue of glacial earthquakes expanded of 1633 newly detected glacial events. Moreover, we extensively discuss the choice of the features used to describe glacial earthquakes, in particular the 39 new features created in this study which have drastically improved our results with 7 of the 10 best features being in the added set. The perspective of further expansion of the glacial earthquake catalogue applying the processing chain discussed in this paper on different time spans highlights how combining seismology and machine learning can ... |
author2 |
Institut de Physique du Globe de Paris (IPGP (UMR_7154)) Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) Institut Terre Environnement Strasbourg (ITES) École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Pirot, Emilie Hibert, Clément Mangeney, Anne |
author_facet |
Pirot, Emilie Hibert, Clément Mangeney, Anne |
author_sort |
Pirot, Emilie |
title |
Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis |
title_short |
Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis |
title_full |
Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis |
title_fullStr |
Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis |
title_full_unstemmed |
Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis |
title_sort |
enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis |
publisher |
HAL CCSD |
publishDate |
2024 |
url |
https://insu.hal.science/insu-04462200 https://insu.hal.science/insu-04462200/document https://insu.hal.science/insu-04462200/file/ggad402.pdf https://doi.org/10.1093/gji/ggad402 |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
Arctic Global warming Greenland |
genre_facet |
Arctic Global warming Greenland |
op_source |
ISSN: 0956-540X EISSN: 1365-246X Geophysical Journal International https://insu.hal.science/insu-04462200 Geophysical Journal International, 2024, 236, pp.849-871. ⟨10.1093/gji/ggad402⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1093/gji/ggad402 insu-04462200 https://insu.hal.science/insu-04462200 https://insu.hal.science/insu-04462200/document https://insu.hal.science/insu-04462200/file/ggad402.pdf BIBCODE: 2024GeoJI.236.849P doi:10.1093/gji/ggad402 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1093/gji/ggad402 |
container_title |
Geophysical Journal International |
container_volume |
236 |
container_issue |
2 |
container_start_page |
849 |
op_container_end_page |
871 |
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1796305989160402944 |