Unsupervised machine learning and k-Means clustering as a way of discovering anomalous events In continuous seismic time series
Name
1046449728-MIT.pdf
Description
Full printable version
Size
5 MB
Format
Adobe PDF
Checksum (MD5)
48ac0deeaf94770556795cb4b8d5533d
Author(s)
Zhakiya, Elezhan
Advisor(s)
Bradford Hager.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Unsupervised k-Means clustering was implemented as a method for identifying anomalies in seismic time series. Sliding window approach was used for generating specific subsequences from the overall waveform. Dynamic Time Warping (DTW) was used as the method for comparing seismic subsequences. DTW barycenter averaging (DBA) was used as the method for averaging multiple subsequences within a group of similiar shapes. Clustering is able to discover anomalously shaped parts of a seismic time series in a completely unsupervised fashion, without requiring anyone to input actual times of the events, any predetermiend examples of events, or any other parameters about the signal.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 51-52).
Subjects
Earth, Atmospheric, and Planetary Sciences.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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