Semantic segmentation for feature detection in ocean bottom seismometer data

We curate a small, manually annotated dataset of 500 spectrograms containing a range of feature classes in the 1 to 50 Hz frequency band from a single ocean bottom seismometer (OBS) from the UPFLOW array in the mid-Atlantic region. We explore several machine learning (ML) training techniques that a...

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Hlavní autoři:	Alex Saoulis, Afonso Loureiro, Maria Tsekhmistrenko, Ana Ferreira
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	McGill University 2026-02-01
Edice:	Seismica
Témata:	ocean bottom seismometer machine learning data processing whales
On-line přístup:	https://seismica.library.mcgill.ca/article/view/1821
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