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dc.contributor.authorAvalos, Sebastian
dc.contributor.authorOrtiz, Julian M.
dc.date.accessioned2020-07-06T19:00:22Z
dc.date.available2020-07-06T19:00:22Z
dc.date.issued2019
dc.identifier.citationAvalos S, Ortiz JM (2019) Exploring the RCNN technique in a multiple-point statistics framework, Predictive Geometallurgy and Geostatistics Lab, Queen’s University, Annual Report 2019, paper 2019-03, 30-40.en
dc.identifier.urihttp://hdl.handle.net/1974/27940
dc.descriptionThis is a preprint version of a paper that was subsequently published in S. Avalos and J. M. Ortiz, “Recursive convolutional neural networks in a multiple-point statistics framework,” Computers & geosciences, vol. 141, p. 104522–, Aug. 2020, doi: 10.1016/j.cageo.2020.104522.. It does not contain changes and edits that were made during peer review, or updates, edits and alterations by the authors and the publisher that occurred prior to acceptance and final publication
dc.description.abstractThis work explores the Recursive Convolutional Neural Network (RCNN) technique in terms of describing (1) the inner architecture, (2) the training process and (3) the simulation algorithm in a multiple-point statistics simulation framework. To acquire a more intuitive understanding of the previous description, visualizations of hidden layers activations using three different training images is presented. Two interesting applications (1) using RCNN E-types as secondary information for MPS algorithms and (2) training RCNN with scarce and limited information, are also explored.en
dc.language.isoenen
dc.publisherQueen's Universityen
dc.relationQueen’s University Research Initiation Granten
dc.relationMitacs Accelerateen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleExploring the RCNN Technique in a Multiple-Point Statistics Frameworken
dc.typejournal articleen
project.funder.identifierhttp://dx.doi.org/10.13039/501100003321en
project.funder.identifierhttp://dx.doi.org//10.13039/501100000038en
project.funder.identifierhttp://dx.doi.org/10.13039/501100004489en
project.funder.nameQueen's Universityen
project.funder.nameNSERCen
project.funder.nameMitacsen
project.funder.nameSRK Consulting Canadaen
oaire.awardNumberRGPIN-2017-04200en
oaire.awardNumberRGPAS-2017-507956en
oaire.awardNumberFR37072-IT14666en


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