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Ruben Juanes. Zhang, Yuchen, M. Eng. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering 2017-09-15T15:38:20Z 2017-09-15T15:38:20Z 2017 2017 http://hdl.handle.net/1721.1/111532 1003324519 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35-37). Appropriate feature-based representations are significant for time series analysis and subsequent machine learning applications. A low-dimensional set of comprehensive features is instrumental to improving the efficiency and accuracy of classification. The main contribution of this work is to develop a new methodology to characterize seismic time series signals by extracting and selection statistical features from them. This methodology allows one to study earthquakes with much lower-dimensional, yet informative, datasets. In this work, a large number of unbiased features were generated from raw time series using the highly-comparative times series analysis (HCTSA) operation library. The similarity between each pair of features was represented by the measure of maximal information coefficient (MIC). MATLAB functions were implemented to compute the similarity matrix of the feature dataset generated by HCTSA. Affinity propagation (AP) was used for clustering similar features and selecting exemplary features from different clusters. These independent exemplary features were determined to characterize the original data. The process was applied to data from real earthquakes and a representation of reduced features was generated to characterize the original times series signals. Results showed that MIC reflected a reliable measure of general associations (similarities) between features, and features which were associated tended to be placed into the same cluster. The clustering results also showed that the average distance within a cluster was generally less than the distances between different clusters, which demonstrated that the selected exemplary features were relatively independent. This work opens a door to the use of feature-based datasets from seismic signals for model inversion and improved subsurface characterization. by Yuchen Zhang. M.Eng. 37 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Civil and Environmental Engineering. Application of maximal information coefficient and affinity propagation to characterizing seismic time series associated with earthquakes Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6d0962c1-19f5-4f62-be72-c3abf27e12c1"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Application of maximal information coefficient and affinity propagation to characterizing seismic time series associated with earthquakes</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Zhang, Yuchen, M. Eng. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Civil and Environmental Engineering.</Keyword> <Abstract>Appropriate feature-based representations are significant for time series analysis and subsequent machine learning applications. A low-dimensional set of comprehensive features is instrumental to improving the efficiency and accuracy of classification. The main contribution of this work is to develop a new methodology to characterize seismic time series signals by extracting and selection statistical features from them. This methodology allows one to study earthquakes with much lower-dimensional, yet informative, datasets. In this work, a large number of unbiased features were generated from raw time series using the highly-comparative times series analysis (HCTSA) operation library. The similarity between each pair of features was represented by the measure of maximal information coefficient (MIC). MATLAB functions were implemented to compute the similarity matrix of the feature dataset generated by HCTSA. Affinity propagation (AP) was used for clustering similar features and selecting exemplary features from different clusters. These independent exemplary features were determined to characterize the original data. The process was applied to data from real earthquakes and a representation of reduced features was generated to characterize the original times series signals. Results showed that MIC reflected a reliable measure of general associations (similarities) between features, and features which were associated tended to be placed into the same cluster. The clustering results also showed that the average distance within a cluster was generally less than the distances between different clusters, which demonstrated that the selected exemplary features were relatively independent. This work opens a door to the use of feature-based datasets from seismic signals for model inversion and improved subsurface characterization.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>