Detecting Fin Whale Calls from Ocean-Bottom Seismometer Data with Deep Learning

Fin whales (Balaenoptera physalus) produce low-frequency vocalisations that propagate efficiently through the ocean and seafloor, making them detectable on broadband ocean bottom seismometers (OBS). While primarily deployed for seismic studies, OBSs offer a unique and cost-effective opportunity for passive acoustic monitoring (PAM) of marine mammals in remote regions over extended periods. Traditional detection and classification of whale calls have relied on energy thresholding, cross-correlation, or matched filtering techniques. These approaches, however, may falter in performance in high-noise environments typical of OBS datasets and often require extensive manual post-processing, making them a labour-intensive process. These limitations motivate automated, noise-robust approaches capable of exploiting the growing volume of seismic data now available.

We present a deep learning framework for detecting fin whale calls from broadband OBSs surrounding the São Jorge Island in the Azores, as well as up to twenty stations of the wider UPFLOW array spanning the Azores–Madeira–Canaries region. Our method uses a semantic segmentation model that operates on spectrogram representations between 12–35 Hz, a frequency band encompassing the classic ‘20-Hz’ fin whale note and the lower frequency ‘backbeat’. The model architecture includes a ResNet-18 encoder pretrained on ImageNet with a U-Net decoder to identify calls in both time and frequency. Training was conducted on a dataset comprising of ~6 days of manually annotated spectrograms and an additional ~6 days of background-only spectrograms. Performance was evaluated using mean Intersection-over-Union and F1-score, achieving 0.65 and 0.80 respectively.

Once validated, the model was applied to months- to year-long OBS records across the region. Fin whale calls were detected at all stations, with clear seasonal patterns showing peak calling activity between October and February, consistent with known migratory patterns in the North Atlantic. Spatial differences in call characteristics and temporal patterns further revealed potential regional variations in vocal behaviour, offering insights into song plasticity and complexity.

By applying a deep learning-based detector on OBS data, we show that machine learning provides a powerful and efficient approach to automating fin whale call detection at scale. Our method processed hundreds of thousands of hours of OBS recordings and identified nearly a million calls across all stations. This large-scale detection unlocks detailed analyses of vocal behaviour, spatial distribution, and seasonal trends, deepening our understanding of their behaviour in the north-east Atlantic. Our findings not only highlight the interdisciplinary value of OBS datasets, but also the potential of machine learning in supporting PAM efforts for the conservation and management of wide-ranging marine species.