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<title lang=en>Statistical classification of log response as an indicator of facies variation during changes in sea level: Integrated Ocean Drilling Program Expedition 313</title>
<creator>Inwood, Jennifer</creator>
<creator>Lofi, Johanna</creator>
<creator>Davies, Sarah</creator>
<creator>Basile, Christophe</creator>
<creator>Bjerum, Christian</creator>
<creator>Mountain, Gregory, </creator>
<creator>Proust, Jean-Noël</creator>
<creator>Al, Et</creator>
<contributor>Borehole Research Group (BRG) ; Department of Geology [Leicester] ; University of Leicester - University of Leicester</contributor>
<contributor>Transferts en milieux poreux ; Géosciences Montpellier ; Université des Antilles et de la Guyane (UAG) - Institut national des sciences de l'Univers (INSU - CNRS) - Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS) - Université des Antilles et de la Guyane (UAG) - Institut national des sciences de l'Univers (INSU - CNRS) - Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Tectonique reliefs et bassins ; Institut des Sciences de la Terre (ISTerre) ; Université Joseph Fourier - Grenoble 1 (UJF) - Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR) - Institut national des sciences de l'Univers (INSU - CNRS) - Institut de recherche pour le développement [IRD] : UR219 - PRES Université de Grenoble - Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]) - Centre National de la Recherche Scientifique (CNRS) - Université Joseph Fourier - Grenoble 1 (UJF) - Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR) - Institut national des sciences de l'Univers (INSU - CNRS) - Institut de recherche pour le développement [IRD] : UR219 - PRES Université de Grenoble - Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Géosciences Rennes (GR) ; Université de Rennes 1 (UR1) - Institut national des sciences de l'Univers (INSU - CNRS) - Observatoire des Sciences de l'Univers de Rennes (OSUR) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>EISSN: 1553-040X</source>
<source>Geosphere</source>
<publisher>Geological Society of America</publisher>
<identifier>insu-00858238</identifier>
<identifier>https://hal-insu.archives-ouvertes.fr/insu-00858238</identifier>
<source>https://hal-insu.archives-ouvertes.fr/insu-00858238</source>
<source>Geosphere, Geological Society of America, 2013, 9 (4), pp.1025-1043. 〈10.1130/GES00913.1〉</source>
<identifier>DOI : 10.1130/GES00913.1</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1130/GES00913.1</relation>
<language>en</language>
<subject>[SDU.STU] Sciences of the Universe [physics]/Earth Sciences</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>In this study, a novel application of a statistical approach is utilized for analysis of downhole logging data from Miocene-aged siliciclastic shelf sediments on the New Jersey Margin (eastern USA). A multivariate iterative nonhierarchical cluster analysis (INCA) of spectral gamma-ray logs from Integrated Ocean Drilling Program (IODP) Expedition 313 enables lithology within this siliciclastic succession to be inferred and, through comparison with the 1311 m of recovered core, a continuous assessment of depositional sequences is constructed. Signifi cant changes in INCA clusters corroborate most key stratigraphic surfaces interpreted from the core, and this result has particular value for surface recognition in intervals of poor core recovery. This analysis contributes to the evaluation of sequence stratigraphic models of large-scale clinoform complexes that predict depositional environments, sediment composition, and stratal geometries in response to sea-level changes. The novel approach of combining statistical analysis with detailed lithostratigraphic and seismic refl ection data sets will be of interest to any scientists working with downhole logs, especially spectral gamma-ray data, and also provides a reference for the strengths and weaknesses of multi component analysis applied to continental margin lithofacies. The method presented here is appropriate for evaluating successions elsewhere and also has value for hydrocarbon exploration where sequence stratigraphy is a fundamental tool.</description>
<date>2013</date>
</dc>
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