Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic
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https://hdl.handle.net/10037/34944Dato
2024-08-23Type
Journal articleTidsskriftartikkel
Peer reviewed
Sammendrag
Seismic phase detection and classification using deep learning is so far poorly investigated
for regional events since most studies focus on local events and short time windows as
the input to the detection models. To evaluate deep learning on regional seismic records,
we create a data set of events in Northern Europe and the European Arctic. This data set
consists of about 151 000 three component event waveforms and corresponding phase arrival
picks at stations in mainland Norway, Finland and Svalbard. We train several state-of-theart and one newly developed deep learning model on this data set to pick P- and S-wave
arrivals. The new method modifies the popular PhaseNet model with new convolutional blocks
including transformers. This yields more accurate predictions on the long input time windows
associated with regional events. Evaluated on event records not used for training, our new
method improves the performance of the current state-of-the-art methods when it comes to
recall, precision and pick time residuals. Finally, we test our new model for continuous mode
processing on 4 d of single-station data from the ARCES array. Results show that our new
method outperforms the existing array detector at ARCES. This opens up new opportunities
to improve automatic array processing with deep learning detectors.
Forlag
Oxford University PressSitering
Myklebust, Köhler A. Deep learning models for regional phase detection on seismic stations in Northern Europe and the European Arctic. Geophysical Journal International. 2024;239(2):862-881Metadata
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