Data-Driven Analysis and Prediction of Ocean Ambient Noise in the Northeast Pacific Ocean Continental Slope
Abstract
Ocean ambient noise reveals important information about marine life, natural phenomena, andthe human footprint in the ocean. Under"standing the ocean ambient noise in the Northeast Pacificcontinental shelf is a challenging task due to the steep continental slope, range-dependent oceanbottom properties from sand in shallow water to silt and clays in the deeper parts, frequentseismic activities in the subduction zone, and volcanic eruptions in the Axial Seamount.Developing a mathematical ambient noise model is not practical in this region due to thecomplexity of the environment. Here, a data-driven approach will be taken that leverages anexisting, public, long-term ocean acoustic dataset recorded by hydrophone arrays at the OceanObservatories Initiative (OOI) to (1) characterize the ambient noise in the Northeast Pacificcontinental shelf, (2) identify its directionality and its seasonal, spectral, and spatial patterns, and(3) predict future trends of the ambient noise in this challenging environment. The OOI providesmulti-year continuous observations of the ocean noise along the continental shelf, spanning awide area with different water depths, to continuously monitor the pulse of the ocean in realtime.This project enhances our understanding of the complex phenomena in the ocean andexpands our ability to predict the ocean ambient noise. Machine learning tools and cloudcomputing resources will be used to advance this field and establish a new state of the art. Uponsuccessful completion of this project, the outcome will form the basis for managing the globalocean in a sustainable way. Findings will be disseminated through journal publications,conference presentations, and ONR reports and presentations. Research will be integrated withoutreach to broaden the participation of underrepresented groups, engage undergraduate studentsin research, and train a graduate student and a postdoc as the next generation of researchers inocean" sciences with data science expertise.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 30, 2019
- Source ID
- N000141912644
Entities
People
- Shima Abadi
Organizations
- Office of Naval Research
- United States Navy
- University of Washington