A continuous car ferry line crossing the Saguenay Fjord mouth and traffic from the local whale-watching fleet introduce high levels of shipping noise in the heart of the Saguenay-St. Lawrence Marine Park. To characterize this noise and examine its potential impact on belugas, a 4-hydrophone array was deployed in the area and continuously recorded for five weeks in May-June 2009. The source levels of the different vessel types showed little dependence on vessel size or speed increase. Their spectral range covered 33 dB. Lowest noise levels occurred at night, when ferry crossing pace was reduced, and daytime noise peaked during whale-watching tour departures and arrivals. Natural ambient noise prevailed 9.4% of the time. Ferry traffic added 30-35 dB to ambient levels above 1 kHz during crossings, which contributed 8 to 14 dB to hourly averages. The whale-watching fleet added up to 5.6 dB during peak hours. Assuming no behavioral or auditory compensation, half of the time, beluga potential communication range was reduced to less than ~30% of its expected value under natural noise conditions, and to less than ~15% for one quarter of the time, with little dependence on call frequency. The echolocation band for this population of belugas was also affected by the shipping noise.

An inversion scheme is proposed, relying upon the inversion of the noise of a moving ship measured on a single distant hydrophone. The spectrogram of the measurements exhibits striations which depend on waveguide parameters. The periodic behavior of striations versus range are used to estimate the differences of radial wavenumber between couples of propagative modes at a given frequency. These wavenumber differences are stacked for several frequencies to form the relative dispersion curves. Such relative dispersion curves can be synthesized using a propagation model feeded with a bottom geoacoustic model. Inversion is performed by looking for the bottom properties that optimize the fit between measured and predicted relative dispersion curves. The inversion scheme is tested on simulated data. The conclusions are twofold: (1) a minimum 6 dB signal to noise ratio is required to obtained an unbiased estimate of compressional sound speed in the bottom with a 3 m s(-1) standard deviation; however, even with low signal to noise ratio, the estimation error remains bounded and (2) in the case of a multi-layer bottom, the scheme produces a single depth-average compressional sound speed. The inversion scheme is applied on experimental data. The results are fully consistent with a core sample measured around the receiving hydrophone.

<p>Maps of underwater noise generated by shipping activity became a useful tool to support international regulations on marine environments. They are used to infer the risk of impact on biodiversity. Maps are performed by 1) computing the emitted noise levels from ships, 2) propagating the acoustic signal in the environment and 3) using localized measurements to validate the results. Because of mismatches in environmental data and a limited number of measurements, noise maps remain highly uncertain.</p><p>In this work, the uncertainty of the noise maps is investigated through the potential complexity of soundscape. The acoustic signal at each receiving cell is computed from the convolution of the source of the ships by the transmission losses of the environment. Complexity is mapped by computing Shannon's entropy of the transmission losses for each receiver. High entropy areas only reflect high shipping densities and favorable acoustic propagation properties of the local environment. Low entropy areas reflect: low shipping density and/or poor acoustic propagation properties. An area with high shipping densities and poor acoustic propagation properties will still have low entropy values.</p><p>Entropy maps allow classifying areas depending on their environmental features. Thus, scenarios of uncertainty are defined. Results highlight the necessity to consider the diversity of the environmental properties in support of the production of noise maps. The methodology could help in optimizing spatial and temporal resolution of map computations, as well as optimizing acoustic monitoring strategies.</p>