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Seismic source modeling by clustering earthquakes and predicting earthquake magnitudes

Hashemi, M and Karimi, HA (2016) Seismic source modeling by clustering earthquakes and predicting earthquake magnitudes. In: UNSPECIFIED.

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Abstract

Seismic sources are currently generated manually by experts, a process which is not efficient as the size of historical earthquake databases is growing. However, large historical earthquake databases provide an opportunity to generate seismic sources through data mining techniques. In this paper, we propose hierarchical clustering of historical earthquakes for generating seismic sources automatically. To evaluate the effectiveness of clustering in producing homogenous seismic sources, we compare the accuracy of earthquake magnitude prediction models before and after clustering. Three prediction models are experimented: decision tree, SVM, and kNN. The results show that: (1) the clustering approach leads to improved accuracy of prediction models; (2) the most accurate prediction model and the most homogenous seismic sources are achieved when earthquakes are clustered based on their non-spatial attributes; and (3) among the three prediction models experimented in this work, decision tree is the most accurate one.

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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Hashemi, M
Karimi, HAhkarimi@pitt.eduHKARIMI0000-0001-5331-5004
Date: 1 January 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume: 166
Page Range: 468 - 478
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-319-33681-7_39
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9783319336800
ISSN: 1867-8211
Date Deposited: 15 Jun 2016 16:52
Last Modified: 11 Aug 2024 18:55
URI: http://d-scholarship.pitt.edu/id/eprint/28211

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