Bayesian acoustic prediction assimilating oceanographic and acoustically inverted data

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.We thank the partial funding of Funda¸c˜ao para a Ciˆencia e Tecnologia - FCT under POSI, POCTI and POCI programs, and scholarship no. SFRH/BD/9032/2002. Acknowledgements are addressed also to Emanuel Coelho, for conducing the MREA’03 sea trial, and to Peter Gerstoft, for prompt help and improvements of the SAGA inversion package

Blind channel estimation with data from the INTIMATE'96 sea trial

Blind multipath channel estimation is studied by time-frequency (TF) analysis. For a linear frequency modulated source, the technique is based on its instantaneous frequency estimation, followed by an approximate formulation of matched- ltering. Tests concern at-sea recorded data during the INTIMATE '96 experiment.Thanks are due to the INTIMATE '96 team for the real data acquisition, to FCT, for funding, under fellowship PRAXIS XXI (BM/19298/99), and to A. Quinquis and C. Gervaise for the valuable reception at ENSIETA

From oceanographic to acoustic forecasting: acoustic model calibration using in situ acoustic measures

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

Field calibration a tool for acoustic noise prediction. The CALCOM 10 data set

It is widely recognized that anthropogenic noise affects the marine fauna, thus it becomes a major concern in ocean management policies. In the other hand there is an increasing demand for wave energy installations that, presumably, are an important source of noise. A noise prediction tool is of crucial importance to assess the impact of a perspective installation. Contribute for the development of such a tool is one of the objectives of the WEAM project. In this context, the CALCOM’10 sea trial took place off the south coast of Portugal, from 22 to 24 June, 2010 with the purpose of field calibration. Field calibration is a concept used to tune the parameters of an acoustic propagation model for a region of interest. The basic idea is that one can significantly reduce the uncertainty of the predictions of acoustic propagation in a region, even with scarce environmental data (bathymetric, geoacoustic), given that relevant acoustic parameters obtained by acoustic inference (i.e. acoustic inversion) are integrated in the prediction scheme. For example, this concept can be applied to the classical problem of transmission loss predictions or, as in our case, the problem of predicting the distribution of acoustic noise due to a wave energy power plant. In such applications the accuracy of bathymetric and geoacoustic parameters estimated by acoustic means is not a concern, but only the uncertainty of the predicted acoustic field. The objective of this approach is to reduce the need for extensive hydrologic and geoacoustic surveys, and reduce the influence of modelling errors, for example due to the bathymetric discretization used. Next, it is presented the experimental setup and data acquired during the sea trial as well as preliminary results of channel characterization and acoustic forward modelling

Environmental and acoustic assessment: The AOB concept

The requirement for rapid environmental assessment has motivated the development of prediction tools, which allow the observation and prediction in very short notice, of the ocean evolution in an interval up to 3-4 weeks, in given littoral areas. Complex systems exist nowadays, where multidimensional quantities like the oceanographic-biogeochemical-optical-acoustic fields, are tracked in time, melding measures and models of some or all the involved quantities. At some point in the prediction system, the acoustic forecast is computed by acoustic propagation models taking as input the environmental forecast. Inevitably, the error of the acoustic forecast as given by the model output, originates from at least two error sources. The first is the environmental forecast error. The second is due to the model inaccuracies, and to the dependence of propagation on parameters not dealt with by the prediction system, like geometric or geo-acoustic properties. The acoustic community has developed a large number of acoustic inversion systems - based on e.g. matched-field processors or travel-time tomography - from which one can learn that an accurate acoustic simulation requires feeding the acoustic model with an environment which differs from the actual environment by a certain gap. This requires that the environmental forecast as given by ocean prediction systems be gap-compensated, prior to its inclusion in the acoustic environmental input. This paper puts the environmental gap in evidence, considering environmental forecasts, and historical and inverted data, to define heterogeneous environmental inputs to the propagation model. The corresponding acoustic outputs are compared to actual data from the MREA '03 sea trial. It is observed that acoustic inversion can play a significant role in converting the environmental forecast into the acoustic forecast. (c) 2007 Elsevier B.V. All rights reserved

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

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

Water column tomographic inversion with a network of drifting buoys

The estimation of ocean environmental properties by means of the inversion of acoustic signals has in several occasions been performed using a single vertical array of acoustic receivers, with a towed acoustic source as an attempt to ensure a rapid spatial coverage of the area of interest, as only a single ocean transect is "seen" at each time. Ideally, one would like to obtain an instantaneous picture of the complete area (volume) under observation. However, the resulting acoustic observations, hence environmental estimates, are not simultaneous in time. Using multiple acoustic receiving arrays appears to be a natural step towards both increasing the spatial coverage, and obtaining simultaneous environmental estimates of different ocean transects. It also gives a higher chance to capture spatial transient features, as for example solitons. Using multiple receiver arrays represents the addition of a new spatial dimension at the receiving end and opens up the number of possibilities to a Nx2D or full 3D view of the ocean. Taking support on the data set of the RADAR'07 experiment (July 9 - 16, 2007) where data was simultaneously collected on three vertical arrays, this paper explores space coherent processing of the several receiving arrays and Nx2D or 3D environmental constrained water coloumn matched-field inversion.FCT, Portugal under programs POCI, POSI and POCTI