Can Deep Learning Predict Complete Ruptures in Numerical Megathrust Faults?

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dc.contributor.orgEarth, Environmental, and Planetary Sciences
dc.creatorBlank, David
dc.creatorMorgan, Julia
dc.date.accessioned2021-08-18T12:00:26Z
dc.date.available2021-08-18T12:00:26Z
dc.date.issued2021-08-18
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.
dc.format.extent269 MB total size
dc.format.mimetypetext/csv, text/x-python-code
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-4132.
dc.identifier.doihttps://doi.org/10.25611/8TJE-4132
dc.identifier.urihttps://hdl.handle.net/1911/111262
dc.language.isoeng
dc.publisherRice University
dc.rightsCC0 1.0 Universal
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.titleCan Deep Learning Predict Complete Ruptures in Numerical Megathrust Faults?
dc.title.subtitleDataset
dc.type.dcmiDataset
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