Rui Chen, Ph.D., 2021
Henrik Schmidt, Advisor
The Arctic Ocean is a vital component of Earth’s climate system experiencing dramatic environmental changes. These changes are reflected in its underwater ambient soundscape as its origin and propagation are primarily dependent on properties of the ice cover and water column.
The first component of this work examines the effect on ambient noise characteristics due to changes to the Beaufort Sea sound speed profile (SSP) and ice cover. Hypothesized shifts to the ambient soundscape and surface noise generation are verified by comparing measured noise data during two experiments to modeled results.
Motivated by our data analyses, the second component presents several tools to facilitate ambient noise characterization and generation monitoring. One is a convolutional neural network approach to noise range estimation. Its robustness to SSP and bottom depth mismatch is compared with conventional matched field processing. Another tool is a frequency domain, transient event detection algorithm to identify and categorize noise transients in data spectrograms. The spectral content retained by this method enables insight into the generation mechanism of the detected events by the ice cover. Lastly, we present the deployment of a seismo-acoustic system to localize transient events. The examination of this system’s performance prompts recommendations for future deployments.