David Hannay

Genetic Algorithm Inversion of the 1997 Geoacoustic Inversion Workshop Test Case Data

An analysis of the 1997 Geoacoustic Inversion Workshop test case data was carried out to benchmark the performance of a Genetic Algorithm (GA) inversion code called SAGA_INV.1 The inversion program made use of Westwoods ORCA propagation model,2 FORTRAN subroutines, and Interactive Data Language (Research Systems Inc. IDL). SAGA_INV is capable of performing inversions with either Simulated Annealing (SA) or GA optimization schemes; however, only the GA portion of the code has been benchmarked with the workshop test cases at the present time. Not all of the workshop test cases were processed: this study was concerned only with the CAL, SD, SO, AT, and WA data sets. The CAL data was processed using three different cost functions: (i) standard Bartlett processor, (ii) a broadband coherent processor, and (iii) a transmission loss mismatch function. These processors were applied to three frequency bands: (i) 76 frequencies between 25 Hz and 100 Hz, (ii) nine frequencies between 28 Hz and 36 Hz, and (iii) 13 frequencies between 44 Hz and 56 Hz. The latter two frequency regimes were intended to simulate 1/3-octave bands centered at 32 Hz and 50 Hz, respectively. Four different receiving arrays were simulated: (i) a 1550 m aperture horizontal, bottom mounted array at approximately 1-km range, (ii) a similar array at approximately 4.2-km range, (iii) a 55-m aperture 12-element vertical array located at 1-km range, and (iv) a similar vertical array at 5-km range. In addition to processing the CAL data set, all three subcases of the SD, SO, AT, and WA data sets were also processed; however, only the transmission loss cost function and the two simulated 1/3-octave bands were considered for these test cases.

Assessing vessel slowdown for reducing auditory masking for marine mammals and fish of the western Canadian Arctic

Vessel slowdown may be an alternative mitigation option in regions where re-routing shipping corridors to avoid important marine mammal habitat is not possible. We investigated the potential relief in masking in marine mammals and fish from a 10 knot speed reduction of container and cruise ships. The mitigation effect from slower vessels was not equal between ambient sound conditions, species or vessel-type. Under quiet ambient conditions, a speed reduction from 25 to 15 knots resulted in smaller listening space reductions by 16-23%, 10-18%, 1-2%, 5-8% and 8% respectively for belugas, bowheads, bearded seals, ringed seals, and fish, depending on vessel-type. However, under noisy conditions, those savings were between 9 and 19% more, depending on the species. This was due to the differences in species hearing sensitivities and the low ambient sound levels measured in the study region. Vessel slowdown could be an effective mitigation strategy for reducing masking.

Acoustic characterization of exploration drilling in the Chukchi and Beaufort seas

This paper characterizes underwater sound levels produced by three drilling units during offshore exploration drilling at three sites in the Beaufort and Chukchi seas. Received levels and spectra are reported as functions of distance during drilling and excavation of mudline cellars (MLCs). Sound levels emitted during MLC excavation exceeded those during drilling at all three sites, although this operation was much shorter in duration. Drilling sounds exhibited tones below 2u2009kHz, with harmonics present to 10u2009kHz, while MLC excavation sounds were broadband in character. Drilling sounds varied substantially between the three operations, whereas MLC excavation sounds were more consistent in amplitude and spectral distribution. Estimates of broadband and 1/3-octave band source levels were computed from measurements at 1u2009km range. The broadband drilling source levels were 168.6u2009dB re 1u2009μPa m for the Kulluk drilling unit, 174.9u2009dB re 1u2009μPa m for the drillship Noble Discoverer, and 170.1u2009dB re 1u2009μPa m for the semi-submersible Polar Pioneer. The received levels measured at 1u2009km during MLC excavation yielded source level estimates that were more consistent among sources: 191.8, 193.0, and 193.3u2009dB re 1u2009μPa for the Discoverer, Kulluk, and Polar Pioneer, respectively.

Passive acoustic detection and estimation of the number of sources using compact arrays

The problem of estimating the number of sound-producing sources detected using a compact array of hydrophones is addressed. Closed form expressions representing the techniques of automatic detection and estimation of the number of callers are given. Their performance is evaluated on a year-long dataset (1 October 2015-6 October 2016) containing humpback whale and killer whale calls collected in the Strait of Georgia, near Vancouver, British Columbia. Manual verification of the automatic detections produced by the approach required ∼40 h.

Detection and Classification of Vocalizations for the Study of Marine Mammal Distributions in the Chukchi Sea

Automatic detection and classification of marine mammal vocalizations were performed on a large acoustic dataset collected almost continuously between July 2007 and October 2009 in the Alaskan Chukchi Sea. The purpose of this work was to determine spatial and temporal distributions of marine mammals over a wide area of the Chukchi Sea and to characterize ambient and anthropogenic noise. The acoustic data were obtained from multiple consecutive deployments of between 8 and 44 underwater acoustic recorders sampling at 16 kHz. Median filter and split-window normalizer detection processors were implemented to effectively detect vocalization events. The classification of calls by species was found to be more difficult due to a wide range of vocalization types produced by at least nine species; vocalizations were identified from bowheads, belugas, gray whales, fin whales, killer whales, walruses, bearded and ribbon seals, and arctic cod. Many of these species produced multiple call types and some call types evolved seasonally. Several classification approaches were implemented, and their performances were quantified by comparing classifier outputs with the results from manual classification analyses of a subset of the data. This presentation discusses the classification approaches implemented and the performance evaluations of the classifiers for selected species.