Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloAutomated detection of sedimentary features using wavelet analysis and neural networks on single beam echosounder data: A case study from the Venice Lagoon, Italy
Anno di pubblicazione2012
Formato
  • Elettronico
  • Cartaceo
Autore/iMadricardo F., Tegowski J., Donnici S.
Affiliazioni autoriIstituto di Scienze Marine, Consiglio Nazionale delle Ricerche Institute of Oceanography, University of Gdansk
Autori CNR e affiliazioni
  • SANDRA DONNICI
  • FANTINA MADRICARDO
Lingua/e
  • inglese
AbstractAcoustic methods are well established and widely used for the exploration of the seafloor and the sub-bottom sediments. However, the mapping and reconstruction of the sedimentary features revealed by acoustics can require a very long time because often large acoustic datasets need to be described and interpreted. To reduce the time of the geophysical visual interpretation, we implemented a new procedure for facies classification based on wavelet analysis and neural networks applied to the acoustic profiles. The optimized algorithm applied to a data set of the very shallow Lagoon of Venice classifies automatically the echo shape parameters to identify and map the main lagoon sedimentary features, such as palaeochannels and palaeosurfaces. The classification algorithm contains a set of wavelet transformation parameters as inputs to a neural network analysis based on the self-organizing map (SOM). The analysis was applied on 580 km of acoustic profiles acquired in a very shallow (less than 1 m) and turbid area of the lagoon with a sub-bottom penetration of about 6-7 m under the bottom. Without any special pre-requirement on the data, the algorithm was successfully tested against the results of the visual interpretation and allowed an automated and more efficient full 2D mapping of the sedimentary features of the area. We could distinguish and map different types of palaeochannels, buried creeks, palaeosurfaces as well as areas characterized by homogeneous mudflat facies. The results were validated by comparison with 5 cores sampled in the survey area corresponding with the main sedimentary features revealed by the acoustics.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da43
Pagine a54
Pagine totali12
RivistaContinental shelf research
Attiva dal 1982
Editore: Pergamon Press. - Oxford;
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 0278-4343
Titolo chiave: Continental shelf research
Titolo proprio: Continental shelf research.
Titolo abbreviato: Cont. shelf res.
Numero volume della rivista43
Fascicolo della rivista-
DOI10.1016/j.csr.2012.04.018
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000306980400005)
Parole chiaveSedimentary feature classification, Wavelet transformation, Neural network, Venice Lagoon
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ISMAR — Istituto di scienze marine
Moduli/Attività/Sottoprogetti CNR
  • TA.P02.028.005 : Evoluzione olocenica dei sistemi lagunari
Progetti Europei-
Allegati
Automated detection of sedimentary features (documento privato )
Tipo documento: application/pdf

Dati associati a vecchie tipologie
I dati associati a vecchie tipologie non sono modificabili, derivano dal cambiamento della tipologia di prodotto e hanno solo valore storico.
Editore
  • Elsevier, Amsterdam (Paesi Bassi)