Use of Automated passive acoustic monitoring methods for monitoring for marine mammals in conjunction with US Navy Mid-frequency Active Sonar training events

Abstract

Automated passive acoustic detection, classification, and localization (DCL) methods are employed to deal with large volumes of acoustic data to support estimating the sound pressure levels (SPLs) that marine mammals are exposed to from mid-frequency active sonar (MFAS) during US Naval training events. These methods are applied to a training event involving MFAS conducted February 2012 in Hawaiian waters with thirty one hydrophones of data collected continuously over an 11 day period. The automated methods detect and determine locations of marine mammals, specifically minke and beaked whales, and the times of the MFAS transmissions utilizing custom C + + algorithms. Streamlined manual validation methods are employed which utilize custom Matlab display routines. Animal locations uncertainties are addressed for the two different species. Once the transmitting ship and animal locations are determined acoustic propagation modeling is utilized to estimate the sound pressure levels (in dB re 1 micro Pascal) that an animal, or group of animals, were exposed to. Surface ducted propagation conditions can result in species such as beaked whales being exposed to over 30 dB higher SPL’s when they return to the surface to breathe compared to when at depth foraging.

Studies on the Computer Control Methods for Marine Environment Monitoring Data

“…To meet the actual demand of a complex marine environment, China has made a comprehensive use of many effective monitoring means. Marine monitoring data quality index, with the combination of the actual characteristics of many quality control means, has fully embodied the request for accuracy and tightness in marine environment quality monitoring index [5][6][7][8]. So the main content of quality assessment can be defined as: data integrity, rationality and representativeness.…”