,,,- Institute of Marine Sciences CSIC, Marine geosciences, Barcelona, Spain (hlatorre@icm.csic.es)
While sensing marine environments, seismic and DAS instruments routinely
record hydroacoustic signals together with transient seismic phases. Although some
of these signals correspond to converted phases at the seafloor boundary, other
signals that originate within the water column are also recorded. Whale vocalisations
are a particular class of these hydroacoustic signals, commonly referred to as whale
songs in marine science.
In the case of fin whales, distinct vocalisation types include 20-Hz and
backbeat reproductive calls produced by males, and 40-Hz feeding-associated calls
attributed to both sexes. All of them fall within the bandwidth and sampling
characteristics commonly available in seismic and DAS experiments and are
therefore relevant for bioacoustics monitoring. Unlike seismic arrivals corresponding
to P and S phases, which are typically short and impulsive, individual notes sung by
whales are composed of many cycles. Although existing picking algorithms can
already detect some of these notes, often from amplitude increases, detection
performance can be improved by developing strategies that account for the narrow-
band nature and longer duration of these signals.
Here we adapt the Kurtosis-Value-Picker (KVP) algorithm, originally
developed by the authors to pick P and S phases with accurate arrival times, to better
detect individual notes within whale songs. Since accurate picking times are not as
critical as detection itself for these particular types of signals and their later study, we
can instead focus on their specific frequency content and waveform. Replacing the
Ricker wavelet used by KVP with the Morlet wavelet, we find that detection
improves significantly in tested data and that non-target signals are more effectively
rejected. The time-frequency resolution trade-off introduced by the Morlet wavelet is
not limiting when the focus is on detection rather than accurate picking times. This
allows for better narrow-band selection, which in turn facilitates improved
classification of notes within whale songs.
How to cite: Latorre, H., Ventosa, S., Diego-Tortosa, D., and Ugalde, A.: Adapting a multiscale phase picking algorithm to detect whale songs in marine environments, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-63, https://doi.org/10.5194/egusphere-gc14-fibreoptic-63, 2026.
