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David Dall'Osto

Senior Research Scientist/Engineer

Email

dallosto@uw.edu

Phone

206-221-5085

Department Affiliation

Acoustics

Education

B.S. Mechanical Engineering, Vanderbilt University, 2006

M.S. Mechanical Engineering, University of Washington, 2009

Ph.D. Mechanical Engineering, University of Washington, 2013

Publications

2000-present and while at APL-UW

Neural network for geoacoustic inversion of sub-bottom profiler data

Diamond, J., D. Dall'Osto, and J. Mower, "Neural network for geoacoustic inversion of sub-bottom profiler data," Proc. Mtgs. Acoust., 55, doi:10.1121/2.0002019
, 2025.

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28 Mar 2025

Sub-bottom profilers are utilized to extract features pertaining to the sub seafloor environment sediment stratification. Acquisition and analysis of sub-bottom profiles can provide insight into the sediment composition and acoustical properties. Typical analysis of profiles involves computationally expensive inversions such as model-based or Bayesian techniques which require large computational costs. Here, a neural network is developed to perform a geoacoustic inversion on simulated sub-bottom profiler data. The network is used to derive attenuation and acoustical impedance measurements corresponding to the layered media. Geoacoustic properties of the layered sediments are compared to values determined through a direct inversion of reflection coefficient, testing how well these techniques recover the ground truth values. The network, trained on simulated data, is applied to real sub bottom profiler data acquired over a well-studied area called the New England Mud Patch, roughly 80 km south of Nantucket. The simulated data-trained network is compared to a network trained on experimental data acquired by the R/V Tioga over the same region.

Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning

Vardi, A., P.H. Dahl, D. Dall'Osto, D. Knobles, P. Wilson, J. Leonard, and J. Bonnel, "Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning," J. Acoust. Soc., Am., 156, 4229-4241, doi:10.1121/10.0034707, 2024.

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24 Dec 2024

This article presents a spatial environmental inversion scheme using broadband impulse signals with deep learning (DL) to model a single spatially-varying sediment layer over a fixed basement. The method is applied to data from the Seabed Characterization Experiment 2022 (SBCEX22) in the New England Mud-Patch (NEMP). Signal Underwater Sound (SUS) explosive charges generated impulsive signals recorded by a distributed array of bottom-moored hydrophones. The inversion scheme is first validated on a range-dependent synthetic test set simulating SBCEX22 conditions, then applied to experimental data to predict the lateral spatial structure of sediment sound speed and its ratio with the interfacial water sound speed. Traditional geoacoustic inversion requires significant computational resources. Here, a neural network enables rapid single-signal inversion, allowing the processing of 1836 signals along 722 tracks. The method is applied to both synthetic and experimental data. Results from experimental data suggest an increase in both absolute compressional sound speed and sound speed ratio from southwest to northeast in the NEMP, consistent with published coring surveys and geoacoustic inversion results. This approach demonstrates the potential of DL for efficient spatial geoacoustic inversion in shallow water environments.

Estimation of peak dosage of kinetic acoustic measures from pressure measurements as derived from time-limited signal waveforms

Drinnan, R.W., P.H. Dahl, and D.R. Dall'Osto, "Estimation of peak dosage of kinetic acoustic measures from pressure measurements as derived from time-limited signal waveforms," JASA Express Lett., 4, doi:10.1121/10.0034611, 2024.

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16 Dec 2024

This study builds on Dahl, Bonnel, and Dall'Osto [J. Acoust. Soc. Am. 155(5), 3291–3301 (2024)] by empirically demonstrating the equivalence between peak kinematic values (acoustic displacement, velocity, acceleration) and peak dynamic values (pressure). Methods for estimating peak levels from pressure are developed and tested on signals from impulsive sources used in the Seabed Characterization Experiment (2022) and a towed narrow band sonar source from the Target and Reverberation Experiment (2013). The comparison between peak kinematic levels and peak pressure falls within the calibration uncertainty of the vector sensor. The analysis shows that, for typical monitoring scenarios, peak pressure measurements are sufficient to monitor peak kinematic dosages.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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