Skip to main content

News about Scitation

In December 2016 Scitation will launch with a new design, enhanced navigation and a much improved user experience.

To ensure a smooth transition, from today, we are temporarily stopping new account registration and single article purchases. If you already have an account you can continue to use the site as normal.

For help or more information please visit our FAQs.

banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. D. J. Tang, J. Moum, J. Lynch, P. Abbot, R. Chapman, P. Dahl, T. Duda, G. Gawarkiewicz, S. Glenn, J. Goff, H. Graber, J. Kemp, A. Maffei, J. Nash, and A. Newhall, “ Shallow Water' 06—A joint acoustic propagation/nonlinear internal wave physics experiment,” Oceanography (Wash. D.C.) 20, 156167 (2007).
2. J. A. Goff, B. J. Kraft, L. A. Mayer, S. G. Schock, C. K. Sommerfield, H. C. Olson, S. P. S. Gulick, and S. Nordfjord, “ Seabed characterization on the New Jersey middle and outer shelf: Correlatability and spatial variability of seafloor sediment properties,” Mar. Geol. 209,147172 (2004).
3. J. Bonnel and N. R. Chapman, “ Geoacoustic inversion in a dispersive waveguide using warping operators,” J. Acoust. Soc. Am. 130(2), EL101EL107 (2011).
4. J. Bonnel, S. E. Dosso, and N. R. Chapman, “ Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis,” J. Acoust. Soc. Am. 134(1), 120130 (2013).
5. M. Taroudakis, G. Tzagkarakis, and P. Tsakalides, “ Classification of shallow water acoustic signals via alpha stable modeling of the one dimensional wavelet coefficients,” J. Acoust. Soc. Am. 119, 13961405 (2006).
6. G. Tzagkarakis, M. I. Taroudakis, and P. Tsakalides, “ A statistical geoacoustic inversion scheme based on a modified radial basis functions neural network,” J. Acoust. Soc. Am. 122, 19591968 (2007).
7. M. I. Taroudakis and C. Smaragdakis, “ On the use of Genetic Algorithms and a statistical characterization of the acoustic signal for tomographic and bottom geoacoustic inversions,” Acta Acust. Acust. 95, 814822 (2009).
8. M. Taroudakis and C. Smaragdakis, “ Inversions of statistical parameters of an acoustic signal in range-dependent environments with applications in ocean acoustic tomography,” J. Acoust. Soc. Am. 134, 28142823 (2013).
9. S. Mallat, “ A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674692 (1989).
10. J. P. Nolan, “ Parameterizations and modes of stable distributions,” Stat. Probab. Lett. 38, 187195 (1998).
11. S. Kullback, Information Theory and Statistics ( Dover, New York, 1998).

Data & Media loading...


Article metrics loading...



This paper presents an application to validate an acoustic signal characterization scheme for ocean acoustic tomography and geoacoustic inversions proposed by Taroudakis, Tzagkarakis, and Tsakalides [J. Acoust. Soc. Am. , 1396–1405 (2006)] using data from an experiment at sea. The data were collected during the Shallow water '06 (SW06) experiment off the New Jersey Continental Shelf and the inversion results (sea-bed geoacoustic parameters and source range) are compared with those reported from the same data by Bonnel and Chapman [J. Acoust. Soc. Am. (2), EL101–EL107 (2011)]. The comparison and the signal reconstruction using estimated values of the model parameters are satisfactory indicating that the new signal characterization method is useful for practical applications of acoustical oceanography.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd