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dc.contributor.authorBaasch, Ben­jamin  Concept link
dc.contributor.authorMül­ler, Hen­drik  Concept link
dc.contributor.authorvon Dobeneck, Tilo  Concept link
dc.date.accessioned2018-11-08T20:02:41Z
dc.date.available2018-11-08T20:02:41Z
dc.date.issued2018-04-17
dc.identifier.citationGeophysical Journal International 215 (2018): 460–473en_US
dc.identifier.urihttps://hdl.handle.net/1912/10701
dc.descriptionAuthor Posting. © The Authors, 2018. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 215 (2018): 460–473, doi:10.1093/gji/ggy152.en_US
dc.description.abstractIn this work, we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine-learning techniques. Non-negative matrix factorization is used to determine grain-size end-members from sediment surface samples. Four end-members were found, which well represent the variety of sediments in the study area. A radial basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.en_US
dc.description.sponsorshipThis work was funded through DFG Research Center/Cluster of Excellence ‘The Ocean in the Earth System’ and was part of MARUM Research Area SDen_US
dc.language.isoen_USen_US
dc.publisherOxford University Pressen_US
dc.relation.urihttps://doi.org/10.1093/gji/ggy152
dc.subjectNeural networksen_US
dc.subjectFuzzy logicen_US
dc.subjectStatistical methodsen_US
dc.subjectElectrical propertiesen_US
dc.subjectMagnetic propertiesen_US
dc.subjectMarine electromagneticsen_US
dc.subjectControlled source electromagnetics (CSEM)en_US
dc.titlePredictive modelling of grain-size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function networken_US
dc.typeArticleen_US
dc.identifier.doi10.1093/gji/ggy152


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