Automated Classification of Ground Motion Record Quality Using Machine Learing

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dc.contributor.authorBellagamba, Xavier
dc.contributor.authorLee, Robin
dc.contributor.authorBradley, Brendon
dc.date.accessioned2018-09-18T23:13:18Z
dc.date.available2018-09-18T23:13:18Z
dc.date.issued2018en
dc.description.abstractMotivation Densification of strong-motion station networks, their increased sensitivity, and the desire to use smaller magnitude data, is leading to exponentially-increasing ground motion datasets. Despite the improving reliability of seismic instrumentation, recorded ground motions are not of uniform quality, and the exponentially-increasing dataset sizes require automated quality assessment in order to be scalable. Here we propose a two-layer neural network that takes key ground motion metrics as inputs to automatically determine the quality of the records.en
dc.identifier.urihttp://hdl.handle.net/10092/16008
dc.language.isoen
dc.rightsCC-BY 4.0 Internationalen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcode
dc.titleAutomated Classification of Ground Motion Record Quality Using Machine Learingen
dc.typeConference Contributions - Otheren
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