Ambient Sound Modeling in Complex Ocean Environments

Using state-of-the-art acoustics propagation simulations and complex oceanographic models, the Underwater Acoustics and Signal Processing branch at NRL seeks to better understand how local environmental processes are imprinted on ambient sound data. Temporal and spatial variability of the acoustic propagation environment result in significant variability to the received acoustic signal time series. This effort will create a more comprehensive understanding of the changes in received acoustic time series due to the presence and location of oceanographic features such as fronts, eddies, and mixing processes. Large variability in ocean data presents challenges for traditional data analysis methods, but better understandings of how specific features in the ocean appear on acoustic sensors will reveal new modes for interpreting data. Successful candidates should have a strong background in numerical modeling and data analysis. Familiarity with Python or Matlab encouraged. 

[1] D. J. Brooker, Kay L. Gemba, Laurie T. Fialkowski; Overcoming snapshot-deficient measurements with knowledge-aided approaches. JASA Express Lett. 1 May 2022; 2 (5): 054804

[2] John A. Colosi, William Zinicola-Lapin; Sensitivity of mixed layer duct propagation to deterministic ocean features. J. Acoust. Soc. Am. 1 March 2021; 149 (3): 1969–1978.

key words

Underwater Acoustics; Wave Propagation; Signal Processing; Physics; Oceanography; Ambient Sound; Numerical Modeling; Data Analysis

Citizenship:  Open to U.S. citizens and permanent residents

Level:  Open to Postdoctoral applicants