Sensitivity of acoustic propagation to uncertainties in the marine environment as characterized by various rapid environmental assessment methods

Abstract

Accurate sonar performance prediction modelling depends on a good knowledge of the local environment, including bathymetry, oceanography and seabed properties. The function of rapid environmental assessment (REA) is to obtain relevant environmental data in a tactically relevant time frame, with REA methods categorized by the nature and immediacy of their application, from historical databases through remotely sensed data to in situ acquisition. However, each REA approach is subject to its own set of uncertainties, which are in turn transferred to uncertainty in sonar performance prediction. An approach to quantify and manage this uncertainty has been developed through the definition of sensitivity metrics and Monte Carlo simulations of acoustic propagation using multiple realizations of the marine environment. This approach can be simplified by using a linearized two-point sensitivity measure based on the statistics of the environmental parameters used by acoustic propagation models. The statistical properties of the environmental parameters may be obtained from compilations of historical data, forecast conditions or in situ measurements.During a field trial off the coast of Nova Scotia, a set of environmental data, including oceanographic and geoacoustic parameters, were collected together with acoustic transmission loss data. At the same time, several numerical models to forecast the oceanographic conditions were run for the area, including 5-and 1-day forecasts as well as nowcasts. Data from the model runs are compared to each other and to in situ environmental sampling, and estimates of the environmental uncertainties are calculated. The forecast and in situ data are used with historical geoacoustic databases and geoacoustic parameters collected using REA techniques, respectively, to perform acoustic transmission loss predictions, which are then compared to measured transmission loss. The progression of uncertainties in the marine environment, within and between different REA categories, and the consequences on acoustic propagation are examined.

Predicting acoustic dose associated with marine mammal behavioural responses to sound as detected with fixed acoustic recorders and satellite tags

“…Characterizing uncertainties associated with sound propagation modelling is challenging and is a continuing point of study on which a large body of literature exists (Colosi et al, 1999;Lynch et al, 2003;Finette, 2006;Lermusiaux et al, 2010;Pecknold and Osler, 2012).…”

“…The exposure area near Jan Mayen was situated in an oceanographic frontal zone with warmer, more saline, waters coming in from the south, and colder, less saline waters coming in from the Greenland Sea into the Norwegian Sea (Bourke et al, 1992;Rudels et al, 2005;Mork et al, 2014). Acoustic propagation in such frontal environments is notoriously difficult to model accurately (Heathershaw et al, 1991;Lynch et al, 2003;Finette, 2006;Katsnel'son et al, 2007;Pecknold and Osler, 2012;Shapiro et al, 2014), especially when detailed measurements of the oceanographic conditions are lacking.…”

Assessing the Impact of Different Ocean Analysis Schemes on Oceanic and Underwater Acoustic Predictions

“…Acoustic simulations were one-way coupled to each member of the oceanic ensemble system. As limited-size Monte Carlo simulations, ensemble prediction systems provide indications on the sensitivity of uncertainties (Pecknold & Osler, 2012). The main diagnostics we rely on is the "explained ensemble variance," which is built by clustering the different ensemble members depending on the perturbation used.…”

“…Ensemble approaches, as a limited-size application of Monte Carlo simulations, may also provide a useful assessment of sources of uncertainty for integrated ocean-acoustic systems (e.g., Dosso et al, 2007). This provides further motivation for developing such systems, as ranking uncertainty sources is of obvious importance to detect the reliability of the analysis system within rapid environmental assessments (Pecknold & Osler, 2012).…”

Quantifying, predicting, and exploiting uncertainties in marine environments

“…The authors suggest that sufficient resolution of the wind forcing, especially in areas close to the shoreline, is critical for accurate nearshore forecasts. Pecknold and Osler (2011) present an approach to quantify and manage environmental uncertainty using sensitivity metrics and Monte Carlo simulations of acoustic propagation with multiple realizations of the marine environment. This approach can be simplified by using a linearized twopoint sensitivity measure based on the statistics of the environmental parameters used by acoustic propagation models.…”