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dc.contributor.authorDomel, Przemyslaw
dc.contributor.authorHibert, Clément
dc.contributor.authorSchlindwein, Vera
dc.contributor.authorPlaza-Faverola, Andreia
dc.date.accessioned2023-08-09T09:27:32Z
dc.date.available2023-08-09T09:27:32Z
dc.date.issued2023-06-16
dc.description.abstractAutomatic 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 average accuracy of 87 per cent in recognition between the classes, comparable to classification results for data from land. We find that, from the used set of features, the most important in separating the different classes of events are related to the kurtosis of the envelope of the signal in different frequencies, the frequency with the highest energy and overall signal duration. We illustrate the implementation of the approach by using the temporal and spatial distribution of SDEs as a case study. We used recordings from six OBSs deployed between 2019 and 2020 off the west-Svalbard coast to investigate the potential link of SDEs to fluid dynamics and discuss the robustness of the approach by analysing SDE intensity, periodicity and distance to seepage sites in relation to other published studies on SDEs.en_US
dc.identifier.citationDomel 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):589-609en_US
dc.identifier.cristinIDFRIDAID 2163192
dc.identifier.doi10.1093/gji/ggad244
dc.identifier.issn0956-540X
dc.identifier.issn1365-246X
dc.identifier.urihttps://hdl.handle.net/10037/29811
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.relation.ispartofDomel, P. (2023). Seismicity of the western-Svalbard margin and its relationship with near surface fluid flow and seepage systems - A study using ocean bottom seismometers. (Doctoral thesis). <a href=https://hdl.handle.net/10037/31067>https://hdl.handle.net/10037/31067</a>.
dc.relation.journalGeophysical Journal International
dc.relation.projectIDNorges forskningsråd: 223259en_US
dc.relation.projectIDTromsø forskningsstiftelse: SEAMSTRESSen_US
dc.relation.projectIDNorges forskningsråd: 287865en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleEvent recognition in marine seismological data using Random Forest machine learning classifieren_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)