Focalization: Environmental focusing and source localization

Collins, Michael D.; Kuperman, W. A.

doi:10.1121/1.401933

Cited by 218 publications

(106 citation statements)

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“…This is the concept of acoustic inversion, which has been largely studied since the 1970's. Many works like the ones of Baggeroer [6], Collins [7], Gerstoft [8], Richardson [9], Jesus [10] and Elisseeff [11], showed successful results of environmental estimation using acoustic signals. the environment, are favorable to the objective at hand, since they optimize for the modeled acoustic field.…”

Section: Problem Statement and Backgroundmentioning

confidence: 99%

Acoustic field calibration for noise prediction: The CALCOM' 10 data set

Martins

Felisberto

Jesus

2011

OCEANS 2011 IEEE - Spain

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Abstract-Wave energy is one of the marine renewable energies that are becoming increasingly explored. One of the concerns about the respective ocean plants is the noise generated by the mechanical energy converters. This noise may affect the fauna surrounding the energy plant, what induces the idea of planning the location of a prospective plant, optimum in terms of noise minimization. Naturally, in such an approach, the plant noise can be predicted, using information concerning the ocean geometric, water column and bottom properties, if available. This information can be fed to an acoustic propagation code, to solve an acoustic forward problem. Inevitably, this knowledge is often incomplete, and the use of guesses or inferences from nautical charts can lead to erroneous noise predictions. This paper presents a noise prediction tool which can be divided into two steps. The first step consists of characterizing the candidate ocean area, in terms of the environmental properties relevant to acoustic propagation. In the second step, the environmental characteristics are fed to a computational acoustic propagation model, which provides estimates of the plant-noise generated in the candidate area. The first step uses at-sea measured acoustic data, during the CALCOM'10 sea trial (in Portugal), to solve an acoustic inverse problem, which gives environmental estimates. This procedure can be seen as a "field model calibration", in that the estimated environmental properties are tailored to model the acoustic data. The second step uses the estimates in a forward modeling problem, with the same propagation code. In numerical terms, differences greater than 4.4 dB in the median of the modeled transmission loss difference have been observed, upto ≈ 1.6 km from the acoustic source. The results show that the field calibration is important to better model the data at hand, and thus act as a noise prediction tool, as compared to a procedure in which only a partial a priori knowledge of the candidate oceanic area is available. The results are promising, in terms of the application of the present method in the project of ocean power plants.

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Section: Problem Statement and Backgroundmentioning

confidence: 99%

Acoustic field calibration for noise prediction: The CALCOM' 10 data set

Martins

Felisberto

Jesus

2011

OCEANS 2011 IEEE - Spain

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“…A goal of this paper is to validate that the platform laydown solutions determined by the GA are indeed optimal. Unlike the Mixed Linear Programming method (Yilmaz 2005;Yilmaz, et al 2008), and simulated annealing (Collins and Kuperman 1991) mathematical proof of optimality for the GA is not possible. We settle for demonstration that the GA solutions provide value added and are better than other sampling approaches.…”

Section: Validationmentioning

confidence: 99%

Validation of genetic algorithm-based optimal sampling for ocean data assimilation

et al. 2016

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Abstract-Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the Genetic Algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root-mean-square-error (RMSE) between the "true" data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A 5-glider optimal sampling study is set up for a 400 km x 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions. Index Terms-Genetic algorithms, ocean technology, optimization methods sampling methods, adaptive sampling, computational ocean modeling, data assimilation, error subspace statistical estimation, OSSE

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“…Several parameters are therefore to be optimized simultaneously in order to fit the model to the data via environmental focalization [9]. The water column temperature, parameterized by the EOF coefficients, is time variant as well as source position (both range and depth), array tilt and receiver depth.…”

Section: Parameter Focalization Applied To Oatmentioning

confidence: 99%

Shallow water tomography in a highly variable scenario

Soares

Jesus

Coelho

2006

Acoustic Sensing Techniques for the Shallow Water Environment

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In October 2000, SiPLAB and the Instituto Hidrografico (IH -PN) conducted the IN-TIFANTE'00 sea trial in a shallow area off the Peninsula of Troia, approximately 50 kḿ south from Lisbon, in Portugal. The experiment itself and results obtained in most of the data set have been reported at various occasions in the last two years. This paper focuses on the data acquired during Event 2, where the acoustic propagation path was approximately range independent and the source ship was held on station at a constant range of 5.8 km from the vertical line array. Although these conditions were, in general, relatively benign for matched-field tomography, retrieval of water column and bottom parameters over a 14-hour-long recording revealed to be extremely difficult. This paper analysis in detail the characteristics of this data set and determines the causes for the observed inversion difficulties. Is is shown that the causes for the poor performance of the conventional methods are mainly the tide induced spatially correlated noise and the relative source-receiver motion during time averaging. An eigenvalue-based criterion is proposed for detecting optimal averaging time. It is shown that this data selection procedure together with hydrophone normalization and an appropriate objective function provide a better model fit and consistent inversion results and thus a better understanding of the environmental variability.

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Focalization: Environmental focusing and source localization

Cited by 218 publications

References 0 publications

Acoustic field calibration for noise prediction: The CALCOM' 10 data set

Acoustic field calibration for noise prediction: The CALCOM' 10 data set

Validation of genetic algorithm-based optimal sampling for ocean data assimilation

Shallow water tomography in a highly variable scenario

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