The Effects of the Seafloor on Low Frequency Acoustic Propagation
Abstract
Sonar system performance in shallow water is greatly influenced by the properties of the seafloor. Physical oceanography also plays" an important role and can complicate the acoustic propagation. The ocean properties can modulate the propagation and depending on the season can make the bottom more or less influential. For example, in summer with a strong thermocline, the sound is refracted into the seafloor and thus making the bottom properties more significant. But in some areas, such as shoreward from a shelfbreak front, the warmer bottom water intrusions can protect the seafloor and limit the interaction. Our work has focused on understanding the frequency and depth dependence of the bottom compressional and shear wave speed and attenuation. Often, the elastic properties (shear effects) can become important and control the bottom loss as a function of angle and frequency. We have been developing new inversion schemes for shear wave properties. Our previous investigations have indicated that water-borne acoustic arrival properties such as their Airy Phase are sensitive to sediment shear properties. Recently, we were successful in designing, assembling and testing of a new Interface Wave Sediment Profiler (iWaSP) to estimate shear properties as function of depth and position. This device was funded through the DURIP program. We were successful in deploying this system during the ONR-funded Seabed Characterization Experiment (SBCEX) ~ 2017. We propose to explore different techniques to understand the impact of physical oceanographic variability in combination with bottom properties on low-frequency (<1 kHz) acoustic propagation by analyzing existing relevant data sets such as SBCEX collected in 2017, Shallow Water 06 experiment in 2006, and Shelfbreak Primer Experiment in 1996. All these field experiments were conducted on the East Coast of the US and the PIs participated in all these experiments. In addition, the PIs also participated in the Asian Seas International Acoustic Experiment in 2000 and 2001 in the East China Sea. These data sets provide a wide range of physical oceanographic and geologic settings for addressing Navy priorities Another potential opportunity that will addressed in this project is the application of machine learning (ML) techniques to the data collected in the experiments mentioned above. We are particularly interested in understanding and quantifying the value-added and efficacy of machine learning techniques to seafloor and ocean parameter estimation. We recently applied machine learning techniques to tracking vessels near a hydrophone array. We believe that ML could be used for estimation of environmental parameters, data assimilation, and model verification. An additional problem of interest to the US Navy is the performance of vector sensors deployed in or on the seabed. We have deployed geophones and hydrophones on the seabed with multiple bottom types including sand, silt and clay/mud. The performance of these sensors when in contact with the bottom Or buried causes uncertainly in the magnitude and phase response which makes coherent processing of multiple sensors problematic".
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 11, 2020
- Source ID
- N000142012033
Entities
People
- James M. L. Miller
Organizations
- Office of Naval Research
- United States Navy
- University of Rhode Island