Mesoscale habitat use by St. Lawrence estuary beluga over the annual cycle from an acoustic recording network.

The spatial-temporal distribution pattern of St. Lawrence Estuary (SLE) beluga is examined with a passive acoustic monitoring network of 13 stations from June 2018 to October 2021. A beluga calling index, correlated with beluga density, is used as a proxy for habitat use by the population at weekly, monthly, and yearly scales.

Effects of shipping on marine acoustic habitats in Canadian Arctic estimated via probabilistic modeling and mapping.

Canadian Arctic and Subarctic regions experience a rapid decrease of sea ice accompanied with increasing shipping traffic. The resulting time-space changes in shipping noise are studied for four key regions of this pristine environment, for 2013 traffic conditions and a hypothetical tenfold traffic increase. A probabilistic modeling and mapping framework, called Ramdam, which integrates the intrinsic variability and uncertainties of shipping noise and its effects on marine habitats, is developed and applied.

Mapping probability of shipping sound exposure level.

Mapping vessel noise is emerging as one method of identifying areas where sound exposure due to shipping noise could have negative impacts on aquatic ecosystems. The probability distribution function (pdf) of sound exposure levels (SEL) is an important metric for identifying areas of concern. In this paper a probabilistic shipping SEL modeling method is described to obtain the pdf of SEL using the sonar equation and statistical relations linking the pdfs of ship traffic density, source levels, and transmission losses to their products and sums.

Shallow-water acoustic tomography from angle measurements instead of travel-time measurements.

For shallow-water waveguides and mid-frequency broadband acoustic signals, ocean acoustic tomography (OAT) is based on the multi-path aspect of wave propagation. Using arrays in emission and reception and advanced array processing, every acoustic arrival can be isolated and matched to an eigenray that is defined not only by its travel time but also by its launch and reception angles. Classically, OAT uses travel-time variations to retrieve sound-speed perturbations; this assumes very accurate source-to-receiver clock synchronization.

Time-angle sensitivity kernels for sound-speed perturbations in a shallow ocean.

Acoustic waves traveling in a shallow-water waveguide produce a set of multiple paths that can be characterized as a geometric approximation by their travel time (TT), direction of arrival (DOA), and direction of departure (DOD). This study introduces the use of the DOA and DOD as additional observables that can be combined to the classical TT to track sound-speed perturbations in an oceanic waveguide. To model the TT, DOA, and DOD variations induced by sound-speed perturbations, the three following steps are used: (1) In the first-order Born approximation, the Fréchet kernel provides a linear link between the signal fluctuations and the sound-speed perturbations; (2) a double-beamforming algorithm is used to transform the signal fluctuations received on two source-receiver arrays in the time, receiver-depth, and source-depth domain into the eigenray equivalent measured in the time, reception-angle and launch angle domain; and finally (3) the TT, DOA, and DOD variations are extracted from the double-beamformed signal variations through a first-order Taylor development.

Analyzing sound speed fluctuations in shallow water from group-velocity versus phase-velocity data representation.

Data collected over more than eight consecutive hours between two source-receiver arrays in a shallow water environment are analyzed through the physics of the waveguide invariant. In particular, the use of vertical arrays on both the source and receiver sides provides source and receiver angles in addition to travel-times associated with a set of eigenray paths in the waveguide. From the travel-times and the source-receiver angles, the eigenrays are projected into a group-velocity versus phase-velocity (Vg-Vp) plot for each acquisition.