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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorYener, Bülent, 1959-
dc.contributorRoecker, Steven W.
dc.contributorYener, Bülent, 1959-
dc.contributorRoecker, Steven W.
dc.contributorZaki, Mohammed J., 1971-
dc.contributorMagdon-Ismail, Malik
dc.contributor.authorFerritto, Anthony
dc.date.accessioned2021-11-03T09:02:02Z
dc.date.available2021-11-03T09:02:02Z
dc.date.created2018-07-27T15:39:25Z
dc.date.issued2018-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2240
dc.descriptionMay 2018
dc.descriptionSchool of Science
dc.description.abstractWe begin by examining the data to determine the applicability of a traditional time series model. This serves to discover the inherent complexity of the data before exploring more complex models. From there we examine both geophysics and machine learning approaches. We proceed to compare the performance of these approaches on standard metrics, and conclude by considering possible extensions and improvements.
dc.description.abstractThe objective of our research is to develop algorithms to detect earthquakes from seismograph data in an automated manner. With the high prevalence of earthquakes in places like California and Chile, it is extremely important to be able to detect them automatically since the monitoring of seismograph data by human experts is both time consuming and costly. Furthermore, accurate earthquake detection could potentially save lives. While there has been much work done in this field, their success is limited and calls for new approaches. Our aim is to introduce new algorithms that improve upon the results of existing methods for near earthquake detection.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectComputer science
dc.titleP-wave classification : an investigation of geophysical and machine learning approaches
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid179125
dc.digitool.pid179126
dc.digitool.pid179127
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Computer Science


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