Event recognition in marine seismological data using Random Forest machine learning classifier
Automatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OB...
Published in: | Geophysical Journal International |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Oxford University Press
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10037/29811 https://doi.org/10.1093/gji/ggad244 |
id |
ftunivtroemsoe:oai:munin.uit.no:10037/29811 |
---|---|
record_format |
openpolar |
spelling |
ftunivtroemsoe:oai:munin.uit.no:10037/29811 2023-08-27T04:09:30+02:00 Event recognition in marine seismological data using Random Forest machine learning classifier Domel, Przemyslaw Hibert, Clément Schlindwein, Vera Plaza-Faverola, Andreia 2023-06-16 https://hdl.handle.net/10037/29811 https://doi.org/10.1093/gji/ggad244 eng eng Oxford University Press Geophysical Journal International Norges forskningsråd: 223259 Tromsø forskningsstiftelse: SEAMSTRESS Norges forskningsråd: 287865 Domel P, Hibert C, Schlindwein V, Plaza-Faverola A. Event recognition in marine seismological data using Random Forest machine learning classifier. Geophysical Journal International. 2023;235(1):589-609 FRIDAID 2163192 doi:10.1093/gji/ggad244 0956-540X 1365-246X https://hdl.handle.net/10037/29811 Attribution 4.0 International (CC BY 4.0) openAccess Copyright 2023 The Author(s) https://creativecommons.org/licenses/by/4.0 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2023 ftunivtroemsoe https://doi.org/10.1093/gji/ggad244 2023-08-09T23:07:01Z Automatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OBS data requires catalogues containing hundreds of thousands of labelled event examples that currently do not exist, especially for signals different than earthquakes. Therefore, the usual routine involves standard amplitude-based detection methods and manual processing to obtain events of interest. We present here the first attempt to utilize a Random Forest supervised machine learning classifier on marine seismological data to automate catalogue screening and event recognition among different signals [i.e. earthquakes, short duration events (SDE) and marine noise sources]. The detection approach uses the short-term average/long-term average method, enhanced by a kurtosis-based picker for a more precise recognition of the onset of events. The subsequent machine learning method uses a previously published set of signal features (waveform-, frequency- and spectrum-based), applied successfully in recognition of different classes of events in land seismological data. Our workflow uses a small subset of manually selected signals for the initial training procedure and we then iteratively evaluate and refine the model using subsequent OBS stations within one single deployment in the eastern Fram Strait, between Greenland and Svalbard. We find that the used set of features is well suited for the discrimination of different classes of events during the training step. During the manual verification of the automatic detection results, we find that the produced catalogue of earthquakes contains a large number of noise examples, but almost all events of interest are properly captured. By providing increasingly larger sets of noise examples we see an improvement in the quality of the obtained catalogues. Our final model reaches an ... Article in Journal/Newspaper Fram Strait Greenland Svalbard University of Tromsø: Munin Open Research Archive Svalbard Greenland Geophysical Journal International 235 1 589 609 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
description |
Automatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OBS data requires catalogues containing hundreds of thousands of labelled event examples that currently do not exist, especially for signals different than earthquakes. Therefore, the usual routine involves standard amplitude-based detection methods and manual processing to obtain events of interest. We present here the first attempt to utilize a Random Forest supervised machine learning classifier on marine seismological data to automate catalogue screening and event recognition among different signals [i.e. earthquakes, short duration events (SDE) and marine noise sources]. The detection approach uses the short-term average/long-term average method, enhanced by a kurtosis-based picker for a more precise recognition of the onset of events. The subsequent machine learning method uses a previously published set of signal features (waveform-, frequency- and spectrum-based), applied successfully in recognition of different classes of events in land seismological data. Our workflow uses a small subset of manually selected signals for the initial training procedure and we then iteratively evaluate and refine the model using subsequent OBS stations within one single deployment in the eastern Fram Strait, between Greenland and Svalbard. We find that the used set of features is well suited for the discrimination of different classes of events during the training step. During the manual verification of the automatic detection results, we find that the produced catalogue of earthquakes contains a large number of noise examples, but almost all events of interest are properly captured. By providing increasingly larger sets of noise examples we see an improvement in the quality of the obtained catalogues. Our final model reaches an ... |
format |
Article in Journal/Newspaper |
author |
Domel, Przemyslaw Hibert, Clément Schlindwein, Vera Plaza-Faverola, Andreia |
spellingShingle |
Domel, Przemyslaw Hibert, Clément Schlindwein, Vera Plaza-Faverola, Andreia Event recognition in marine seismological data using Random Forest machine learning classifier |
author_facet |
Domel, Przemyslaw Hibert, Clément Schlindwein, Vera Plaza-Faverola, Andreia |
author_sort |
Domel, Przemyslaw |
title |
Event recognition in marine seismological data using Random Forest machine learning classifier |
title_short |
Event recognition in marine seismological data using Random Forest machine learning classifier |
title_full |
Event recognition in marine seismological data using Random Forest machine learning classifier |
title_fullStr |
Event recognition in marine seismological data using Random Forest machine learning classifier |
title_full_unstemmed |
Event recognition in marine seismological data using Random Forest machine learning classifier |
title_sort |
event recognition in marine seismological data using random forest machine learning classifier |
publisher |
Oxford University Press |
publishDate |
2023 |
url |
https://hdl.handle.net/10037/29811 https://doi.org/10.1093/gji/ggad244 |
geographic |
Svalbard Greenland |
geographic_facet |
Svalbard Greenland |
genre |
Fram Strait Greenland Svalbard |
genre_facet |
Fram Strait Greenland Svalbard |
op_relation |
Geophysical Journal International Norges forskningsråd: 223259 Tromsø forskningsstiftelse: SEAMSTRESS Norges forskningsråd: 287865 Domel P, Hibert C, Schlindwein V, Plaza-Faverola A. Event recognition in marine seismological data using Random Forest machine learning classifier. Geophysical Journal International. 2023;235(1):589-609 FRIDAID 2163192 doi:10.1093/gji/ggad244 0956-540X 1365-246X https://hdl.handle.net/10037/29811 |
op_rights |
Attribution 4.0 International (CC BY 4.0) openAccess Copyright 2023 The Author(s) https://creativecommons.org/licenses/by/4.0 |
op_doi |
https://doi.org/10.1093/gji/ggad244 |
container_title |
Geophysical Journal International |
container_volume |
235 |
container_issue |
1 |
container_start_page |
589 |
op_container_end_page |
609 |
_version_ |
1775350938195722240 |