Abstract
This paper introduces a new technique for clustering seismic events based on processing, in time-frequency domain, the waveforms recorded by seismographs. The detection of clusters of waveforms is performed by a k-means like algorithm which analyzes, at each iteration, the time-frequency content of the signals in order to optimally remove the non discriminant components which should compromise the grouping of waveforms. This step is followed by the allocation and by the computation of the cluster centroids on the basis of the filtered signals. The effectiveness of the method is shown on a real dataset of seismic waveforms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adelfio, G., Chiodi, M., D’Alessandro, A., & Luzio, D. (2011). FPCA algorithm For waveform clustering. Journal of Communication and Computer, 8(6), 494–502. ISSN:1548–7709.
Adelfio, G., Chiodi, M., D’Alessandro, A., Luzio, D., D’Anna, G., & Mangano G. (2012). Simultaneous seismic wave clustering and registration. Computers & Geosciences, 44, 60–69.
Barani, S., Ferretti, G., Massa, M., & Spallarossa, D. (2007). The waveform similarity approach to identify dependent events in instrumental seismic catalogues. Geophysical Journal International, 168(1), 100–108.
D’Alessandro, A., Mangano, G., D’Anna, G., & Luzio, D. (2013). Waveforms clustering and single-station location of microearthquake multiplets recorded in the northern Sicilian offshore region. Geophysical Journal International. doi:10.1093/gji/ggt192.
Ferretti, G., Massa, M., & Solarino, S. (2005) An improved method for the recognition of seismic families: application to the Garfagnana-Lunigiana area, Italy. Bulletin of the Seismological Society of America, 95(5), 1903–1015.
Gan, G., Ma, C., & Wu, J. (2007). Data clustering: theory, algorithms, and applications. Philadelphia: SIAM.
Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336), 846–850.
Shumway, R. H. (2003). Time-frequency clustering and discriminant analysis. Statistics & Probability Letters, 63, 307–314
Zhang, H. (2003). Double-difference seismic tomography method and its applications (Ph.D. thesis, Department of Geology and Geophysics, University of Wisconsin-Madison).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Balzanella, A., Adelfio, G., Chiodi, M., D’Alessandro, A., Luzio, D. (2014). Time-Frequency Filtering for Seismic Waves Clustering. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-06692-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06691-2
Online ISBN: 978-3-319-06692-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)