A slowdown in global trade activity due to COVID-19 has led to a reduction in commercial shipping traffic into the Port of Vancouver. The Ocean Networks Canada observatory system provides researchers real-time access to oceanographic data from a wide range of instruments including hydrophones located along the offshore and inshore approaches to Vancouver. Measurements of power spectral density at 100 Hz from four of these bottom mounted hydrophones are presented, along with AIS data and shipping and trade statistics to assess to what extent the economic impact of COVID-19 can be observed acoustically and in near real-time. The quarterly trend in median weekly noise power in the shipping band of frequencies shows that a reduction in noise commensurate with the economic slowdown has been observed at three of the four hydrophone stations.

This paper develops a linearized Bayesian inversion algorithm for high-precision localization of an autonomous underwater vehicle (AUV) on a test range using time difference of arrival acoustic data. The Bayesian approach allows the uncertainties of several factors, including precise receiver locations and bias and lateral variability of the water-column sound-speed profile, to be included in the inversion as unknown parameters with varying levels of prior information. This provides better estimates of these parameters (and hence AUV locations), and includes the effect of their uncertainties in the uncertainty estimates for AUV locations. A modeling study comparing AUV localization uncertainties from the linearized inversion that results from nonlinear Monte Carlo analysis shows that linearization errors are small, and hence linearized analysis is used to efficiently map AUV localization uncertainty as a function of position over the test range. These approaches are applied to model localization uncertainty for an AUV test range associated with the Victoria Experimental Network Under the Sea cabled observatory system, examining the factors mentioned above as well as the effects of water-column refraction and receiver geometry.

The autonomous passive-acoustic lander Deep Sound was deployed at five locations during the Office of Naval Research (ONR)-supported Seabed Characterization Experiment, a multi-institutional field effort held at the New England Mud Patch, where the seabed is known to consist of a thick layer of silt and clay overlying a medium and coarse sand. The five deployments of Deep Sound were up to 9 h long, during which time ambient noise data, taken over an acoustic bandwidth of 5 Hz–30 kHz, were collected on four hydrophones arranged in an inverted “T” shape. Local temperature and conductivity were also recorded continuously at each location. A wave number integral model of wind-driven noise in a fluid waveguide over a two-layered elastic seabed was used to calculate the dependence of the vertical noise coherence on the geoacoustic properties in the overlying silt and clay layer, as well as the subbottom sand half-space. The modeled noise coherence was fitted to the data over the band 100 Hz–12 kHz, returning the compressional- and shear-wave speeds and densities of both layers and the thickness of the top layer.

This article analyzes the effects of key physical oceanographic conditions on underwater propagation in a shallow water environment. Signals at 2 kHz were transmitted and received over ranges of 1-10 km, and the variability in the sound-speed profile, bathymetry, position of the instruments, and sea surface roughness was measured and the uncertainty on each parameter was estimated. The acoustic channel characteristics, including the transmission loss, delay spread, and coherence time, were calculated between a moored five-element vertical line array and a vessel deployed source. The measurement results were modeled using a Bellhop's ray tracing algorithm. The sensitivity of the simulator output was evaluated as a function of the environmental conditions, and the observed variability in the transmission loss was reproduced by varying the input parameters to the model within the observed and estimated bounds. The relative importance of the physical properties of the environment in terms of their impact on the acoustic channel is determined through a comparison of modeled and measured transmission loss variability.

A two-month-long glider deployment in the Gulf of St. Lawrence, Canada, measured the ambient sound level variability with depth and lateral position across a narrow channel that serves as an active commercial shipping corridor. The Honguedo Strait between the Gaspé Peninsula and Anticosti Island has a characteristic sound channel during the Summer and Fall due to temperature variation with depth. The experiment comprised continuous acoustic measurements in the band 1–1000 Hz and oceanographic (temperature and salinity) measurements from a profiling electric glider down to 210 m water depth. The mean observed ambient sound depth-profile was modeled by placing a uniform distribution of sources near the surface to represent a homogeneous wind-generated ocean wave field and computing the acoustic field using normal modes. The measurements and predictions match within the observed error bars and indicate a minimum in the sound channel at 70 m depth and a relative increase by ∼1 dB down to 180 m depth for frequencies >100 Hz. The impact of detector depth, the distance to a busy shipping corridor, wind noise, flow noise, and self-noise are discussed in the context of passive acoustic monitoring and marine mammal detection.

An ocean-ice-acoustic coupled model is configured for the Beaufort Sea. The model uses outputs from a data assimilating global scale ice-ocean-atmosphere forecast to drive a bimodal roughness algorithm for generating a realistic ice canopy. The resulting range-dependent ice cover obeys observed roughness, keel number density, depth, and slope, and floe size statistics. The ice is inserted into a parabolic equation acoustic propagation model as a near-zero impedance fluid layer along with a model defined range-dependent sound speed profile. Year-long observations of transmissions at 35 Hz from the Coordinated Arctic Acoustic Thermometry Experiment and 925 Hz from the Arctic Mobile Observing System source were recorded over the winter of 2019–2020 on a free-drifting, eight-element vertical line array designed to vertically span the Beaufort duct. The ocean-ice-acoustic coupled model predicts receive levels that reasonably agree with the measurements over propagation ranges of 30–800 km. At 925 Hz, seasonal and sub-seasonal ocean and ice driven variations of propagation loss are captured in the data and reproduced in the model.