Effects of different analysis techniques and recording duty cycles on passive acoustic monitoring of killer whales.

Killer whales in British Columbia are at risk, and little is known about their winter distribution. Passive acoustic monitoring of their year-round habitat is a valuable supplemental method to traditional visual and photographic surveys. However, long-term acoustic studies of odontocetes have some limitations, including the generation of large amounts of data that require highly time-consuming processing. There is a need to develop tools and protocols to maximize the efficiency of such studies. Here, two types of analysis, real-time and long term spectral averages, were compared to assess their performance at detecting killer whale calls in long-term acoustic recordings. In addition, two different duty cycles, 1/3 and 2/3, were tested. Both the use of long term spectral averages and a lower duty cycle resulted in a decrease in call detection and positive pod identification, leading to underestimations of the amount of time the whales were present. The impact of these limitations should be considered in future killer whale acoustic surveys. A compromise between a lower resolution data processing method and a higher duty cycle is suggested for maximum methodological efficiency.

Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis.

This paper presents geoacoustic inversion of a light bulb implosion recorded during the Shallow Water 2006 experiment. The source is low frequency and impulsive, the environment is shallow water, and the acoustic signal is recorded using a single receiver. In this context, propagation is described by modal theory, and inversion is carried out by matching modal dispersion curves in the time-frequency domain. Experimental dispersion curves are estimated using an advanced signal processing method called warping, allowing inversion to be carried out at a relatively short range (~/=7 km). Moreover, the inversion itself is performed using Bayesian methodology. This allows inference of the seabed structure from the data, including the number of seabed layers resolved, optimal estimates of the seabed parameters, and quantitative uncertainty estimates. Inversion results of the experimental data are in good agreement with both ground truth and estimates from other experimental data in the same region.