Multidimensional Clustering and Registration of Seismic Waveform Data

GLM-based automatic picking of waveforms

Clustering of waveforms-data based on FPCA direction

The necessity of nding similar features of waveforms data recorded for earthquakes at di erent time instants is here considered, since eventual similarity between these functions could suggest similar behavior of the source process of the corresponding earthquakes. In this paper we develop a clustering algorithm for curves based on directions de ned by an application of PCA to functional data.

Statistical Analysis of Macroseismic Data for a better Evaluation of Earthquakes Attenuation Laws

In this work we propose a statistical approach, based on the joint analysis of macroseismic data of Italian seismic events of the last two centuries, with which we obtain simultaneously maximum likelihood estimates of attenuation laws and coordinates of hypocenters. Our first results encourage us to use in the future more complex models, with a larger number of historical earthquakes.

Functional Principal components direction to cluster earthquake waveforms

Looking for curves similarity could be a complex issue characterized by subjective choices related to continuous transformations of observed discrete data (Chiodi, 1989). In this paper we combine the aim of finding clusters from a set of individual curves to the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. We apply a classical clustering method to rotated data, according to the direction of maximum variance. A k-means clustering algorithm based on PCA rotation of data is proposed, as an alternative to methods that require previous interpolation of data based on splines or linear fitting (Garc´ıa- Escudero and Gordali…

FPCA Algorithm For Waveform Clustering

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.

Clustering of waveforms based on FPCA direction

Looking for curves similarity could be a complex issue characterized by subjective choices related to continuous transformations of observed discrete data (Chiodi, 1989). Waveforms correlation techniques have been introduced to charac- terize the degree of seismic event similarity (Menke, 1999) and in facilitating more accurate relative locations within similar event clusters by providing more precise timing of seismic wave (P and S) arrivals (Phillips, 1997). In this paper functional analysis (Ramsey, and Silverman, 2006) is considered to highlight common characteristics of waveforms-data and to summarize these charac- teristics by few components, by applying a variant of a classical clust…

Multidisciplinary investigations at the Kamarina archaeological site (southern Sicily, Italy)

Multidisciplinary geophysical investigations have been carried out in a small area of the Greek archaeological site of Kamarina, in southern Sicily, in order to support some hypotheses, derived from historical and archaeological bases. After an aerial photographic and thermographic survey, a small area near to the Agora has been considered for magnetometric and GPR investigations. Obtained results show a good correlation and allow to highlight some structures oriented in agreement with the uncovered remains. The use of integrated geophysical techniques allowed a more robust interpretation of the detected anomalies in order to better address the choices for new excavations.

Local LGCP estimation for spatial seismic processes

Using recent results for local composite likelihood for spatial point processes, we show the performance of advanced and flexible statistical models to describe the spatial displacement of earthquake data. Local models described by Baddeley (2017) allow for the possibility of describing both seismic catalogs and sequences. When analysing seismic sequences, the analysis of the small scale variation is the main issue. The interaction among points is taken into account by Log-Gaussian Cox Processes models through the estimation of the parameters of the covariance of the Gaussian Random Field. In their local version these parameters are allowed to vary spatially, and this is a crucial aspect fo…