Similar features between waveform data recorded for earthquakes at different time instants could suggest similar behavior of the source process of the corresponding source seismic process. In this paper we combine the aim of finding clusters from a set of individual waveform curves with the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. This approach overcome the limitation of the cross-correlation, and represents an alternative to methods based on the interpolation of data by splines or linear fitting.
Adelfio, G., Chiodi, M., D'Alessandro, A., Luzio, D. (2011). FPCA Algorithm For Waveform Clustering. JOURNAL OF COMMUNICATION AND COMPUTER, 8(8), 494-502.
FPCA Algorithm For Waveform Clustering
ADELFIO, Giada;CHIODI, Marcello;D'Alessandro, Antonino;LUZIO, Dario
2011-01-01
Abstract
Similar features between waveform data recorded for earthquakes at different time instants could suggest similar behavior of the source process of the corresponding source seismic process. In this paper we combine the aim of finding clusters from a set of individual waveform curves with the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. This approach overcome the limitation of the cross-correlation, and represents an alternative to methods based on the interpolation of data by splines or linear fitting.
| File | Dimensione | Formato | |
|---|---|---|---|
|
JCC2010_finale.pdf
Solo gestori archvio
Dimensione
626.92 kB
Formato
Adobe PDF
|
626.92 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
