Event recognition in marine seismological data using Random Forest machine learning classifier

• Published in: Geophysical Journal International
• Main Authors: Domel, Przemyslaw, Hibert, Clément, Schlindwein, Vera, Plaza-Faverola, Andreia
• Other Authors: Tromsø Research Foundation, Research Council of Norway
• Format: Article in Journal/Newspaper
• Language: English
• Published: Oxford University Press (OUP) 2023
• Subjects: Fram Strait; Greenland; Svalbard
• Online Access: http://dx.doi.org/10.1093/gji/ggad244; https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggad244/50629157/ggad244.pdf; https://academic.oup.com/gji/article-pdf/235/1/589/50722598/ggad244.pdf

SUMMARY 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 ...