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Research Project

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Acoustic field calibration for noise prediction: the CALCOM'10 data setExpand Expand

Publication . Martins, N.; Felisberto, P.; Jesus, S. M.

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.

Bayesian acoustic prediction assimilating oceanographic and acoustically inverted dataExpand Expand

Publication . Martins, N.; Jesus, S. M.

The prediction of the transmission loss evolution on a day to week frame, in a given
oceanic area, is an important issue in modeling the sonar performance. It relies
primarily on acoustic propagation models, which convert water column and geometric/
geoacoustic parameters to ‘instantaneous’ acoustic field estimates. In practice, to model the acoustic field, even the most accurate acoustic models have to be fed with simplified environmental descriptions, due to computational issues and to a limited knowledge of the environment. This is a limitation, for example, in acoustic inversion methods, in which, by maximizing the proximity between measured and modeled acoustic signals, the estimated environmental parameters are deviated from reality, forming what is normally called an ‘acoustically equivalent environment’. This problem arises also in standard acoustic prediction, in which, the oceanographic forecasts and bottom data (typically from archives) are fed directly
to an acoustic model. The claim in the present work is that, by converting the oceanographic prediction and the bottom properties to ‘acoustically equivalent’
counterparts, the acoustic prediction can be obtained in an optimal way, adapted to the environmental model at hand. Here, acoustic prediction is formulated as a Bayesian estimation problem, in which, the observables are oceanographic forecasts,
a set of known bottom parameters, a set of acoustic data, and a set of water column
data. The predictive posterior PDF of the future acoustic signal is written as a function of elementary PDF functions relating these observables and ‘acoustically
equivalent’ environmental parameters. The latter are obtained by inversion of acoustic data. The concept is tested on simulated data based on water column measurements and forecasts for the MREA’03 sea trial.

Classification of three-dimensional ocean features using three-dimensional empirical orthogonal functionsExpand Expand

Publication . Martins, N.; Calado, L.; Paula, A. C. de; Jesus, S. M.

Acoustic tomography is now a well known method for remote estimation of water column properties. The problem is ill-conditioned and computationally intensive, if each spatial point varies freely in the inversion.
Empirical orhogonal functions (EOFs) efficiently regularize the inversion, leading to a few (2, 3) coefficients to be estimated, giving a coherent estimate of the field. At small scales, EOFs are typically depth-dependent basis functions. The extension of the concept to larger-scale anisotropic fields requires horizontal discretization into cells, with corresponding coefficients. This becomes unstable and computationally intensive, having been overcome by two-dimensional depth-range EOFs, in the past. The present work extends the empirical orthogonal
function concept to three dimensions, assessing the performance of the inversion for an instantaneous sound speed field constructed from dynamical predictions for Cabo Frio, Brazil. The results show that the large-scale features of the field are correctly estimated, though with strong ambiguity, using an acoustic source tens of km from an acoustic hydrophone array. Work is under progress, to remove the ambiguity and estimate finer details of the three-dimensional field, via the addition of multiple acoustic arrays.

From oceanographic to acoustic forecasting: acoustic model calibration using in situ acoustic measuresExpand Expand

Publication . Martins, N.; Jesus, S. M.

Sonar performance prediction relies heavily on acoustic propagation models and environmental representations of the oceanic area in which the sonar is to operate. The
performance estimate is derived from a predicted acoustic eld, which is the output of a propagation model. Though well developed nowadays, acoustic propagation modeling is limited in practice by simpli cations in the numerical methods, in the environmental structure to consider (for computational reasons), and even in the knowledge of some environmental properties. This is complicated by the fact that, in sonar performance prediction, the environmental properties need to be predicted for a far future, in the
order of hours or days. These limitations imply that the acoustic eld at the output of
the acoustic predictor is biased, in current methods. In mathematical terms, the prediction of the acoustic eld can be seen as a model parametrization problem, in which
the model is a numerical propagation model, and the parameters are environmental
descriptors which, when fed to the propagation model, best model the future acoustic field. Since the 1980's, signi cant research has been done in the development of propagation model parametrization, using techniques of the so-called \acoustic inversion" family. These techniques, having as objective the estimation of environmental properties of an oceanic area, use observed acoustic elds at the area, to be matched with candidate elds corresponding to candidate environmental pictures. At the end, the best acoustic match gives the estimated environment, in other words, the best model parameters to closely reproduce the measured acoustic eld. In the current work, the technique of acoustic inversion is used in the design of an acoustic predictor, together with oceanographic forecasts and measures. Synthetic acoustic data generated with oceanographic measures taken in the MREA'03 sea trial, is used to illustrate the proposed method. The results show that a collection of environments estimated by past
acoustic inversions, can ameliorate the acoustic estimates for future time, as compared to a conventional method.

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## Funders

## Funding agency

Fundação para a Ciência e a Tecnologia

## Funding programme

SFRH

## Funding Award Number

SFRH/BD/9032/2002