Single-hydrophone automated passive acoustic ranging of fin whales at Station ALOHA

Abstract

This paper presents a technique for performing passive underwater acoustic ranging with data from a single hydrophone and builds upon earlier localization approaches which estimate the sound source position using times of arrival of acoustic energy traveling along direct and/or interface-reflecting paths between source and receiver. In this work, measured time differences between interface-reflecting and direct path arrival times are compared with a set of model-predicted time differences calculated over a set of candidate source ranges in a way that does not require measured arrival paths to be labeled (e.g., direct, surface bounce, bottom bounce, etc.). The modeled set with the best match to the measured data indicates the best estimate of source range. To enable the processing of multi-year data sets, the detection and localization steps are automated and, where possible, multi-threaded to improve computational efficiency on multi-core computer processors. This approach is demonstrated using 20-Hz fin whale (Balaenoptera physalus) calls recorded by the ALOHA Cabled Observatory (ACO), 100 km N of Oahu (Hawaii) in 4782 m of water.This paper presents a technique for performing passive underwater acoustic ranging with data from a single hydrophone and builds upon earlier localization approaches which estimate the sound source position using times of arrival of acoustic energy traveling along direct and/or interface-reflecting paths between source and receiver. In this work, measured time differences between interface-reflecting and direct path arrival times are compared with a set of model-predicted time differences calculated over a set of candidate source ranges in a way that does not require measured arrival paths to be labeled (e.g., direct, surface bounce, bottom bounce, etc.). The modeled set with the best match to the measured data indicates the best estimate of source range. To enable the processing of multi-year data sets, the detection and localization steps are automated and, where possible, multi-threaded to improve computational efficiency on multi-core computer processors. This approach is demonstrated using 20-Hz fin ...