A Novel Empirical Orthogonal Function (EOF)-Based Methodology to Study the Internal Wave Effects on Acoustic Propagation

Casagrande, Gaëlle; Stéphan, Y.; Varnas, A. C. W.; Folégot, Thomas

doi:10.1109/joe.2011.2161158

Cited by 18 publications

(16 citation statements)

References 17 publications

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“…To minimize the cost of uptake and maximize the clarity of the presentation we have built this diagnostic on Empirical Orthogonal Functions (EOFs), which are used in an enormous variety of contexts (e.g. [1], [2], [3], [4], [5], etc.) and have implementations in every commonly used software toolbox (e.g.…”

Section: Introductionmentioning

confidence: 99%

Feature identification in time-indexed model output

Shaw

Stastna

2019

PLoS ONE

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We present a method for identifying features (time periods of interest) in data sets consisting of time-indexed model output. The method is used as a diagnostic to quickly focus the attention on a subset of the data before further analysis methods are applied. Mathematically, the infinity norm errors of empirical orthogonal function (EOF) reconstructions are calculated for each time output. The result is an EOF reconstruction error map which clearly identifies features as changes in the error structure over time. The ubiquity of EOF-type methods in a wide range of disciplines reduces barriers to comprehension and implementation of the method. We apply the error map method to three different Computational Fluid Dynamics (CFD) data sets as examples: the development of a spontaneous instability in a large amplitude internal solitary wave, an internal wave interacting with a density profile change, and the collision of two waves of different vertical mode. In all cases the EOF error map method identifies relevant features which are worthy of further study.

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Section: Introductionmentioning

confidence: 99%

Feature identification in time-indexed model output

Shaw

Stastna

2019

PLoS ONE

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“…One cannot overestimate the importance of high quality data and monitoring systems when considering the environmental volatility and the intersecting social, political, and economic repercussions. Current data streams for polar observations largely rely on satellites 15 which, while incredibly advantageous for high temporal and spatial resolution, provide little information about the water column. Autonomous vehicles are one tool-deployed individually, in a fleet, or in heterogeneous networks-that will be essential for the next generation of smart data collection in polar environments.…”

Section: A New Blue Arcticmentioning

confidence: 99%

“…Seger et al use passive acoustic listeners to suggest that over the last decade, various species of dolphins may be shifting into Arctic waters during years of significant warm water invasion, and that these acoustic detections may anticipate future visual surveys of habitat shifts. 15 Decreasing sea ice cover also affects the geophony, where periods of ice formation generate higher sound levels. Chen et al finds that, in comparing ambient noise data between 1994 and 2016, the surface noise is now better modeled by sources at discrete ranges rather than the historical assumption of a continuous and uniform distribution of sources.…”

Section: Passive Acousticsmentioning

confidence: 99%

“…This study looked at acoustic transmissions on the Chukchi shelf 14 Duarte et al, "The soundscape of the Anthropocene ocean". 15 Seger et al, "Acoustic documentation of temperate odontocetes in the Bering and Chukchi Seas". 16 Chen et al, "Spectral, spatial, and temporal characteristics of underwater ambient noise in the Beaufort Sea in 1994 and 2016".…”

Section: Active Acousticsmentioning

confidence: 99%

“…14 Notably, Bianco et al discusses dictionary learning as a new method of generating these sound speed "modes" in a shallow water dataset. 15 The learned dictionaries have no orthogonal constraint and describe much of the observed variability with one non-zero coefficient. While the framework implemented in these TDAs uses EOFs to compress sound speed information, it is agnostic to the EOFs themselves, and would work on any set of weights and shape functions.…”

Section: Summary Of Eof Analysis In Ocean Acousticsmentioning

confidence: 99%

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A Virtual Ocean framework for environmentally adaptive, embedded acoustic navigation on autonomous underwater vehicles

Bhatt¹

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Autonomous underwater vehicles (AUVs) are an increasingly capable robotic platform, with embedded acoustic sensing to facilitate navigation, communication, and collaboration. The global positioning system (GPS), ubiquitous for air- and terrestrial-based drones, cannot position a submerged AUV. Current methods for acoustic underwater navigation employ a deterministic sound speed to convert recorded travel time into range. In acoustically complex propagation environments, however, accurate navigation is predicated on how the sound speed structure affects propagation. The Arctic’s Beaufort Gyre provides an excellent case study for this relationship via the Beaufort Lens, a recently observed influx of warm Pacific water that forms a widespread yet variable sound speed lens throughout the gyre. At short ranges, the lens intensifies multipath propagation and creates a dramatic shadow zone, deteriorating acoustic communication and navigation performance. The Arctic also poses the additional operational challenge of an ice-covered, GPSdenied environment. This dissertation demonstrates a framework for a physics-based, model-aided, real-time conversion of recorded travel time into range—the first of its kind—which was essential to the successful AUV deployment and recovery in the Beaufort Sea, in March 2020. There are three nominal steps. First, we investigate the spatio-temporal variability of the Beaufort Lens. Second, we design a human-in-the-loop graphical decision-making framework to encode desired sound speed profile information into a lightweight, digital acoustic message for onboard navigation and communication. Lastly, we embed a stochastic, ray-based prediction of the group velocity as a function of extrapolated source and receiver locations. This framework is further validated by transmissions among GPS-aided modem buoys and improved upon to rival GPS accuracy and surpass GPS precision. The Arctic is one of the most sensitive regions to climate change, and as warmer surface temperatures and shrinking sea ice extent continue to deviate from historical conditions, the region will become more accessible and navigable. Underwater robotic platforms to monitor these environmental changes, along with the inevitable rise in human traffic related to trade, fishing, tourism, and military activity, are paramount to coupling national security with international climate security.

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