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Adelfio, Giada
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Adelfio, Giada
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- PublicationOpen AccessLocal spatial log-Gaussian Cox processes for seismic dataIn this paper, we propose the use of advanced and flexible statistical models to describe the spatial displacement of earthquake data. The paper aims to account for the external geological information in the description of complex seismic point processes, through the estimation of models with space varying parameters. A local version of the Log-Gaussian Cox processes (LGCP) is introduced and applied for the first time, exploiting the inferential tools in Baddeley (Spat Stat 22:261–295, 2017), estimating the model by the local Palm likelihood. We provide methods and approaches accounting for the interaction among points, typically described by LGCP models through the estimation of the covariance parameters of the Gaussian Random Field, that in this local version are allowed to vary in space, providing a more realistic description of the clustering feature of seismic events. Furthermore, we contribute to the framework of diagnostics, outlining suitable methods for the local context and proposing a new step-wise approach addressing the particular case of multiple covariates. Overall, we show that local models provide good inferential results and could serve as the basis for future spatio-temporal local model developments, peculiar for the description of the complex seismic phenomenon.
140 37 - PublicationOpen AccessSouthern-Tyrrhenian seismicity in space-time-magnitude domain(2006-11-23T14:15:54Z)
; ; ; ; ; ;Adelfio, G.; Dipartimento di Chimica e Fisica della Terra (CFTA), Università di Palermo, Itatly ;Chiodi, M.; Dipartimento di Chimica e Fisica della Terra (CFTA), Università di Palermo, Itatly ;De Luca, L.; Dipartimento di Chimica e Fisica della Terra (CFTA), Università di Palermo, Itatly ;Luzio, D.; Dipartimento di Chimica e Fisica della Terra (CFTA), Università di Palermo, Itatly ;Vitale, M.; Dipartimento di Chimica e Fisica della Terra (CFTA), Università di Palermo, Itatly; ;; ; An analysis is conducted on a catalogue containing more than 2000 seismic events occurred in the southern Tyrrhenian Sea between 1988 and October 2002, as an attempt to characterise the main seismogenetic processes active in the area in space, time and magnitude domain by means of the parameters of phenomenological laws. We chose to adopt simple phenomenological models, since the low number of data did not allow to use more complex laws. The two main seismogenetic volumes present in the area were considered for the purpose of this work. The first includes a nearly homogeneous distribution of hypocentres in a NW steeply dipping layer as far as about 400 km depth. This is probably the seismological expression of the Ionian lithospheric slab subducting beneath the Calabrian Arc. The second contains hypocentres concentrated about a sub-horizontal plane lying at an average depth of about 10 km. It is characterised by a background seismicity spread all over the area and by clusters of events that generally show a direction of maximum elongation. The parameters of the models describing seismogenetically homogeneous subsets of the earthquake catalogue in the three analysis domains, along with their confidence intervals, are estimated and analysed to establish whether they can be regarded as representative of a particular subset.226 854 - PublicationOpen AccessFunctional Principal Components direction to cluster earthquake(2010-05-02)
; ; ; ; ;Adelfio, Giada ;Chiodi, Marcello ;D'Alessandro, Antonino; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Luzio, Dario ; ;; 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 (García-Escudero and Gordaliza (2005), Tarpey (2007), Sangalli et al. (2008)).167 148 - PublicationRestrictedJoint second-order parameter estimation for spatio-temporal log-Gaussian Cox processesWe propose a new fitting method to estimate the set of second-order parameters for the class of homogeneous spatiotemporal log-Gaussian Cox point processes. With simulations, we show that the proposed minimum contrast procedure, based on the spatio-temporal pair correlation function, provides reliable estimates and we compare the results with the current available methods. Moreover, the proposed method can be used in the case of both separable and non-separable parametric specifications of the correlation function of the underlying Gaussian Random Field. We describe earthquake sequences comparing several Cox model specifications.
52 1 - PublicationRestrictedSimultaneous seismic wave clustering and registration(2012)
; ; ; ; ; ; ;Adelfio, G.; Università di Palermo ;Chiodi, M.; Università di Palermo ;D'Alessandro, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Luzio, D.; Università di Palermo ;D'Anna, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Mangano, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; ;; ; ; In this paper we introduce a simple procedure to identify clusters of multivariate waveforms based on a simultaneous assignation and alignment procedure. This approach is aimed at the identification of clusters of earthquakes,assuming that similarities between seismic events with respect to hypocentral parameters and focal mechanism correspond to similarities between waveforms of events. Therefore we define a distance measure between seismic curve, in order to interpret and better understand the main features of the generating seismic process.221 16 - PublicationRestrictedSpatial pattern analysis using hybrid models: an application to the Hellenic seismicityEarthquakes are one of the most destructive natural disasters and the spatial distribution of their epicentres generally shows diverse interaction structures at different spatial scales. In this paper, we use a multi-scale point pattern model to describe the main seismicity in the Hellenic area over the last 10 years. We analyze the interaction between events and the relationship with geological information of the study area, using hybrid models as proposed by Baddeley et al. (2013). In our analysis, we find two competing suitable hybrid models, one with a full parametric structure and the other one based on nonparametric kernel estimators for the spatial inhomogeneity.
137 2 - PublicationOpen AccessClusters of effects in quantile regression modelsIn this paper we propose a new method for nding similarity of effects in a multivariate regression context. Using quantile regression, the effect of each covariate on a response variable is represented as a function of percentiles. Col- lecting all these curves, describing the effects of each covariate on the response, we could assess if there are covariates with similar effects. Moreover, we provide a exible algorithm which could be used not only for clustering the coefcient effects of a quantile regression framework, but also for nding clusters of generic curves. We provide also some simulated results and applications on real data, highlighting the exibility of the proposed approach in several research elds.
46 69 - PublicationOpen AccessSpatial analysis of the Italian seismic network and seismicity(2018-06)
; ; ; ; ; ; ; ; ; ; ; Seismic networks are powerful tools for understanding active tectonic processes in a monitored region. Their numerous applications, from monitoring seismicity to characterizing seismogenic volumes and generated seismicity, make seismic networks essential tools for assessing seismic hazard in active regions. The ability to locate earthquakes hypocenters requires a seismic network with a sufficient number of optimally distributed, stations. It is important to assess existing network geometry, to identify seismogenic volumes that are not adequately monitored, and to quantify measures that will allow network improvement. In this work we have studied the spatial arrangement of the stations of the Italian National Seismic Network by means of several Point Pattern techniques The results of the point patter analysis were compare with the spatial distribution of the historical and instrument seismicity and with the distribution of the well know seismogenetic sources of the Italian peninsula. Some considerations have also been made on some models of seismic hazard of the Italian territory. Our analysis allowed us to identify some critical areas that could require an optimization of the monitoring network.140 358 - PublicationRestrictedSpatio‐temporal classification in point patterns under the presence of clutterWe consider the problem of detection of features in the presence of clutter for spatio‐temporal point patterns. In previous studies, related to the spatial context, Kth nearest‐neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation‐maximization algorithm. This paper extends this methodology to the spatio‐temporal context by considering the properties of the spatio‐temporal Kth nearest‐neighbor distances. For this purpose, we make use of a couple of spatio‐temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions of such Kth nearest‐neighbor distances and present an intensive simulation study together with an application to earthquakes.
50 3 - PublicationOpen AccessMultiscale processes to describe the Eastern Sicily Seismic Sequences(2018)
; ; ; ; ; ; ; ; ; In this paper, a version of hybrid of Gibbs point process models is proposed as method to characterise the multiscale interaction structure of several seismic sequences occurred in the Eastern Sicily in the last decade. Seismic sequences were identified by a clustering technique based on space-time distance criterion and hierarchical clustering. We focus our analysis on five small seismic sequences, showing that two of these are described by an inhomogeneous Poisson process (not significant interaction among events) while the other three clusters are described by a Hybrid-Geyer process (mutiscale interaction between events). The proposed method, although it still needs extensive testing on a larger catalogue, seems to be a promising tool for the characterization of seismogenic sources through the analysis of induced seismicity.715 397