“Seismic-mass” density-based algorithm for spatio-temporal clustering

► A novel spatio-temporal seismic clustering algorithm is presented. ► The first stage employs a new density based algorithm. ► The notion of seismic mass is introduced. ► The second stage applies hierarchical clustering. ► The algorithm is applied to the Hellenic arc with results comparative to exp...

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Bibliographic Details
Published in:Expert systems with applications 2013-08, Vol.40 (10), p.4183-4189
Main Authors: Georgoulas, G., Konstantaras, A., Katsifarakis, E., Stylios, C.D., Maravelakis, E., Vachtsevanos, G.J.
Format: Article
Language:English
Subjects:
Agglomerative algorithms
Applied sciences
Computer science
control theory
systems
Data processing. List processing. Character string processing
Density based algorithms
Distinct seismic regions classification
Earth sciences
Earth, ocean, space
Earthquakes, seismology
Exact sciences and technology
Internal geophysics
Memory organisation. Data processing
Regional spatio-temporal clustering
Seismic-mass
Software
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Summary:► A novel spatio-temporal seismic clustering algorithm is presented. ► The first stage employs a new density based algorithm. ► The notion of seismic mass is introduced. ► The second stage applies hierarchical clustering. ► The algorithm is applied to the Hellenic arc with results comparative to expert knowledge. In this research work a new hybrid approach to spatio-temporal seismic clustering is proposed. The method builds upon a novel density based clustering scheme that explicitly takes into account earthquake’s magnitude during the density estimation. The new density based clustering algorithm considers both time and spatial information during cluster formation. Therefore clusters lie in a spatio-temporal space. A hierarchical agglomerative clustering algorithm acts upon the identified clusters after dropping the time information in order to come up only with the spatial description of seismic events. The approach is demonstrated using data from the vicinity of the Hellenic seismic arc in order to enable its comparison with some of the state-of-the-art distinct seismic region identification methodologies. The presented results indicate that the combination of the two clustering stages could be potentially used for an automatic definition of major seismic sources.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.01.028