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Comparative Study of FDA and Time Series Approaches for Seabed Classification from Acoustic Curves

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Abstract

Seabed classification in coastal environments is usually accomplished using multivariate methods applied to acoustic features from corrected or uncorrected echoes. This paper presents a comparative study of alternative statistical tools based on time series clustering and non-hierarchical clustering methods for functional data. This allows us to consider the entire acoustic signal without information reduction and assess performance using data acquired in a controlled environment with three different seabed types. The methods considered are used to both analyse the classification power of the recorded echoes and identify the most significant portions of signal.

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Acknowledgements

This work has been supported by the Xunta de Galicia through the Centro Singular de Investigación de Galicia ED431G/01, Grupos de Referencia Competitiva ED431C-2016-015, and EM2013/052 projects (Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia), in addition to MINECO Grants MTM2014-52876-R and MTM2017-82724-R, all of them through the ERDF. Also, the authors wish to acknowledge J. A. Rodríguez “Rodri”, skipper of the boat “Betsaida”, from the Ecology and Marine Conservation Research Group, University of Murcia, and Gaston Trobbiani from CESIMAR, for their help with the field work.

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Authors and Affiliations

  1. Departamento de Matemáticas, Universidade da Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain

    Javier Tarrío-Saavedra & Andrés Prieto

  2. Centro para el Estudio de Sistemas Marinos (CESIMAR), Centro Nacional Patagónico (CENPAT-CONICET), Puerto Madryn, Argentina

    Noela Sánchez-Carnero

Authors
  1. Javier Tarrío-Saavedra
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  2. Noela Sánchez-Carnero
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  3. Andrés Prieto
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Correspondence to Andrés Prieto.

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Tarrío-Saavedra, J., Sánchez-Carnero, N. & Prieto, A. Comparative Study of FDA and Time Series Approaches for Seabed Classification from Acoustic Curves. Math Geosci 52, 669–692 (2020). https://doi.org/10.1007/s11004-019-09807-7

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  • DOI: https://doi.org/10.1007/s11004-019-09807-7

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