T-phase detection within spectrograms of hydroacoustic data from the Arctic Ocean
DOI:
https://doi.org/10.26443/seismica.v5i2.2506Keywords:
seismoacoustic, Ocean Soundscape, automatic processing, T-phase, Arctic Ocean, underwater acousticsAbstract
Microseismicity in the Central Arctic remains understudied due to its remoteness from land stations. Hydrophone arrays can be used to detect earthquakes from their T-phases, components that propagate in water. This approach can provide a valuable alternative to traditional recording systems and improve the monitoring coverage. However, manual annotations of T-phases are a time-consuming process, and more automatic approaches are needed. Recent studies in T-phase detection consider machine learning techniques, but these methods are computationally intensive and sensitive to training data. In this study, an automated detector is presented utilising a Regions Of Interest (ROI) algorithm on spectrograms, requiring minimal parameter tuning and computational resources. The detector was applied to acoustic data recorded along the Nansen Basin from September 2019 to August 2020 during the CAATEX experiment. The ROI detector correctly identified 82 out of 96 manually labelled events across all moorings, with the majority of false detections linked to the presence of flow noise within hydrophones. The algorithm also characterised spectro-temporal properties of T-phase signals, with energy focused between 3 and 50 Hz, peaking at around 20 Hz. This method is robust against the varying underwater background noise conditions encountered and aids in studying the complexities of the T-phase signal structure.
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Funding data
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HORIZON EUROPE European Research Council
Grant numbers 101094621 -
UK Research and Innovation
Grant numbers 10071903

