Can Deep Learning predict complete ruptures in numerical megathrust faults?

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dc.contributor.orgEarth, Environmental, and Planetary Sciencesen_US
dc.creatorBlank, Daviden_US
dc.creatorMorgan, Juliaen_US
dc.date.accessioned2021-08-18T12:00:26Zen_US
dc.date.available2021-08-18T12:00:26Zen_US
dc.date.issued8/18/2021en_US
dc.description.abstractThis dataset accompanies the paper, "Can Deep Learning Predict Complete Ruptures in Numerical Megathrust Faults?". The directory contains full python codes used to create Convolutional Neural Networks and Long Short Term Memory Recurrent Neural Networks, along with the raw data used for training, testing, and validation. Since stochastic initialization of weights in the training process will result in slightly different parameterization of models trained with identical hyperparameters, we have also included the trained models we presented in the paper with this dataset as well.en_US
dc.format.extent269 MBen_US
dc.format.mimetypetext/csv, text/x-python-codeen_US
dc.identifier.citationBlank, David and Morgan, Julia (2021): Can Deep Learning predict complete ruptures in numerical megathrust faults?. [Dataset]. Rice University. https://doi.org/10.25611/8TJE-4132en_US
dc.identifier.doihttps://doi.org/10.25611/8TJE-4132en_US
dc.identifier.urihttps://hdl.handle.net/1911/111262en_US
dc.language.isoengen_US
dc.publisherRice Universityen_US
dc.rightsCC0 1.0 Universalen_US
dc.rights.urihttps://creativecommons.org/publicdomain/zero/1.0/en_US
dc.titleCan Deep Learning predict complete ruptures in numerical megathrust faults?en_US
dc.title.subtitleDataseten_US
dc.type.dcmiDataseten_US
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