Machine learning in passive ocean acoustics for localizing and characterizing events

• Main Author: Ozanich, Emma
• Other Authors: Gerstoft, Peter
• Format: Thesis
• Language: English
• Published: eScholarship, University of California 2020
• Subjects: Acoustics; Electrical engineering; Geophysics; ambient noise; beamforming; localization; machine learning; passive acoustics; unsupervised clustering; bowhead whale; Fram Strait; North Pole; Spitsbergen
• Online Access: https://escholarship.org/uc/item/0p12m53p; https://escholarship.org/content/qt0p12m53p/qt0p12m53p.pdf

Passive acoustics, or the recording of pressure signals from uncontrolled sound sources, is a powerful tool for monitoring man-made and natural sounds in the ocean. Passive acoustics can be used to detect changes in physical processes within the environment, study behavior and movement of marine animals, or observe presence and motion of ocean vessels and vehicles. Advances in ocean instrumentation and data storage have improved the availability and quality of ambient noise recordings, but there is an ongoing effort to improve signal processing algorithms for extracting useful information from the ambient noise. This dissertation uses machine learning as a framework to address problems in underwater passive acoustic signal processing. Statistical learning has been used for decades, but machine learning has recently gained popularity due to the exponential growth of data and its ability to capitalize on these data with efficient GPU computation. The chapters within this dissertation cover two types of problems: characterization and classification of ambient noise, and localization of passive acoustic sources. First, ambient noise in the eastern Arctic was studied from April to September 2013 using a vertical hydrophone array as it drifted from near the North Pole to north of Fram Strait. Median power spectral estimates and empirical probability density functions (PDFs) along the array transit show a change in the ambient noise levels corresponding to seismic survey airgun occurrence and received level at low frequencies and transient ice noises at high frequencies. Noise contributors were manually identified and included broadband and tonal ice noises, bowhead whale calling, seismic airgun surveys, and earthquake $T$ phases. The bowhead whale or whales detected were believed to belong to the endangered Spitsbergen population and were recorded when the array was as far north as 86$\degree$24'N. Then, ambient noise recorded in a Hawaiian coral reef was analyzed for classification of whale song and fish calls. Using ...