Neural net aided detection of astronomical periodicities in geologic records (Articolo in rivista)

Type
Label
  • Neural net aided detection of astronomical periodicities in geologic records (Articolo in rivista) (literal)
Anno
  • 1996-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/0012-821X(96)84608-5 (literal)
Alternative label
  • Massimo Brescia, Bruno D'Argenio, Vittoria Ferreri, Giuseppe Longo, Nicola Pelosi, Salvatore Rampone, Roberto Tagliaferri (1996)
    Neural net aided detection of astronomical periodicities in geologic records
    in Earth and planetary science letters; Elsevier, Paris (Paesi Bassi)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Massimo Brescia, Bruno D'Argenio, Vittoria Ferreri, Giuseppe Longo, Nicola Pelosi, Salvatore Rampone, Roberto Tagliaferri (literal)
Pagina inizio
  • 33 (literal)
Pagina fine
  • 45 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 139 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1-2 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Dipartimento di Informatica ed Applicazioni, Università di Salerno, Salerno, Italy Osservatorio Astronomico di Capodimonte, Napoli, Italy Istituto di Ricerca GEOMARE sud, CNR, Napoli, Italy Dipartimento di Scienze della Terra, Università di Napoli \"Federico II\", Napoli, Italy Dipartimento di Fisica Teorica, Università di Salerno, Salerno, Italy INFM, Unit of Salerno, Salerno, Italy (literal)
Titolo
  • Neural net aided detection of astronomical periodicities in geologic records (literal)
Abstract
  • Astronomically controlled variations in the Earth's climate induce cyclic trends in the sedimentary process and record (Milankovitch periodicity). One of the main difficulties to be solved in order to choose among the registered periodicities is the conversion from the spatial (i.e. recurrent variations along the stratal sequences) to the temporal domains of the astronomically induced frequencies present in the rock record. We discuss here how this problem can be circumvented by teaching a neural net how to recognize periodicities in the signal. The application to two sequences of shallow water carbonate deposits from the Cretaceous of Southern Italy has shown this approach to be particularly effective, confirming the existence of Milankovitch-type periodicities in the records examined, where climate, sediments and biota concomitantly react to the variation in the solar constant induced by secular perturbations of the Earth's orbital elements. (literal)
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