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David R. Dall'Osto
University of Washington
OpticsPhysicsAcousticsParticle velocitySound intensity
54Publications
4H-index
52Citations
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Publications 61
Newest
#1Peter H. Dahl (UW: University of Washington)H-Index: 19
#2David R. Dall'Osto (UW: University of Washington)H-Index: 4
The Intensity Vector Autonomous Recorder (IVAR) is a system that records four coherent channels of acoustic data continuously: one channel for acoustic pressure and three channels associated with a triaxial accelerometer from which acoustic particle velocity is obtained. IVAR recorded the vector acoustic field in broadband signals originating from Signal, Underwater Sound (SUS) (Mk-64) charges deployed at 5–13-km range from the fixed IVAR site (mean depth 74.4 m) as part of the 2017 Seabed Chara...
1 CitationsSource
Trend estimates from 25 Hz to more than 50 Hz collected from 1994 to 2018 in the north Pacific Ocean are compared. The majority of trends derived from Acoustic Thermometry of Ocean Climate (ATOC) systems over nearly two decades, starting at roughly 1994, suggest a decrease in ambient noise levels of up to 1 dB/year. This is observed on both coastal and deep ocean systems. (Datasets from the remaining systems show either no change or an increase.) Measurements from the Comprehensive Test Ban Trea...
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#1Stephanie Herron (BYU: Brigham Young University)
#2Traci NeilsenH-Index: 1
Last. David R. Dall'Osto (UW: University of Washington)H-Index: 4
view all 6 authors...
While machine learning has become increasingly popular as a means to learn information from large datasets, the question remains how different machine learning models can best be used to improve source ranging and environmental classification. In the current research, machine learning is used to predict the distance and depth of an impulsive source and the seabed type given a set of acoustic signals in a shallow-water ocean environment. Multiple machine learning models have been developed for th...
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The globally distributed CTBT hydrophones form an existing network to measure temperature structure of the world oceans. Ocean tomography (or thermometry, asserting the primary driver in sound speed variability is temperature) typically operates by deploying active sources in precisely known locations and projecting signals at precisely known times. Ocean temperature is inferred from the travel time to distributed receivers. Here, we investigate the potential for using sound sources of opportuni...
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#1Kira Howarth (BYU: Brigham Young University)
#2David F. Van KomenH-Index: 1
Last. David R. Dall'Osto (UW: University of Washington)H-Index: 4
view all 6 authors...
In ocean acoustics, simultaneous estimation of both source-receiver range and environment are complicated by low signal-to-noise ratio (SNR). Range and environment class can be found with a convolutional neural network (CNN), which is chosen because of its ability to find patterns in grid-structured data. The CNN acts on synthetic pressure time series data from a single receiver generated for four canonical environments: deep mud, mud over sand, sandy silt, and sand. Data were split into trainin...
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#1David F. Van Komen (BYU: Brigham Young University)H-Index: 1
#2Tracianne B. Neilsen (BYU: Brigham Young University)H-Index: 13
Last. David R. Dall'Osto (UW: University of Washington)H-Index: 4
view all 5 authors...
Source localization and environmental inference are common problems in ocean acoustics requiring computationally intensive algorithms and knowledge of the search space. Convolutional neural networks (CNNs) learn useful features for making predictions directly from a gridded input signal circumventing the costly practice of selecting features or comparisons to a forward propagation model. To take advantage of these benefits, a CNN was trained and validated on simulated pressure signals generated ...
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#1Julien Bonnel (WHOI: Woods Hole Oceanographic Institution)H-Index: 10
#2David R. Dall'OstoH-Index: 4
Last. Peter H. Dahl (UW: University of Washington)H-Index: 19
view all 3 authors...
Geoacoustic inversion is traditionally performed using sound pressure data. Here, we present a study of geoacoustic inversion using particle velocity, as recorded on a vector sensor. The scope of the study is restricted to shallow water and low-frequency impulsive sources. In this context, the propagation is described by normal mode theory. The modal description of the vector field is well known: the mode amplitudes are different than from the pressure field, but their phase is the same. As a re...
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#1Peter H. Dahl (UW: University of Washington)H-Index: 19
#2David R. Dall'Osto (UW: University of Washington)H-Index: 4
The Intensity Vector Autonomous Recorder (IVAR) is a bottom deployed system measuring particle velocity and pressure. Results using IVAR in the Sediment Characterization Experiment (SBCEX) conducted off New England (spring 2017), involving active sources have been presented [P. H. Dahl and D. R. Dall’Osto, IEEE J. Ocean. Eng. (2019)]. Here, passive ship noise is studied from a 200-m length cargo vessel that is tracked over a 10 km course a 15 knots for which the closest point of approach (CPA) t...
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On Nov. 15, 2017, an intense acoustic event coincident with the disappearance of the Argentine navy submarine, ARA (Armada Argentina) San Juan, was recorded on the hydroacoustic network established to enforce compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Analysis by Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) scientists, based on two hydroacoustic and one seismic detection, provided a likely origin within an error ellipse of 19 km by 12 km; analysis based solel...
1 CitationsSource
#1Julien Bonnel (WHOI: Woods Hole Oceanographic Institution)H-Index: 10
#2David R. Dall'Osto (UW: University of Washington)H-Index: 4
Last. Peter H. Dahl (UW: University of Washington)H-Index: 19
view all 3 authors...
Geoacoustic inversion is traditionnaly performed using sound pressure data. In this paper, we present a sensitivity study of geoacoustic inversion using particle velocity, as recorded on a single vector sensor. The scope of the study is further restricted to shallow water and low-frequency impulsive sources. If propagating modes can be separated, then four vector acoustic metrics can be formed by coherent combination of pressure and velocity: phase speed, circularity, depth-dependent mode speed ...
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