Search

Due to the complexity of the generator process of seismic events, we study under several aspects the interaction structure between earthquake events using recently developed spatio-temporal statistical techniques and models. Using these advanced statistical tools, we aim to characterise the global and local scale cluster behaviour of the Easter Sicily seismicity considering the catalogue data since 2006, when the Italian National Seismic Network was upgraded and earthquake location was sensibly improved. Firstly, we characterise the global complex spatiotemporal interaction structure with the space-time ETAS model where background seismicity is estimated non-parametrically, while triggered seismicity is estimated by MLE. After identifying seismic sequences by a clustering technique, we characterise their spatial and spatio-temporal interaction structures using other advanced point process models. For the characterisation of the spatial interactions, a version of hybrid of Gibbs point process models is proposed as method to describe the multiscale interaction structure of several seismic sequences accounting for both the attractive and repulsive nature of data. Furthermore, we consider log-Gaussian Cox processes (LGCP), that are relatively tractable class of empirical models for describing spatio-temporal correlated phenomena. Several parametric formulation of spatio-temporal LGCP are estimated, by the minimum contrast procedure, assuming both separable and non-separable parametric specification of the correlation function of the underlying Gaussian Random Field.

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.

Objective: Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions. Methods: We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, nonstationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process information transfer (ipInfTr), which is directly derived from point-process-based definitions of the Kolmogorov–Smirnov distance. Results and Conclusion: Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipTE, as well as ipInfTr measures are able to dynamically track changes in physiological systems coupling. Significance: This novel approach opens new avenues in the study of hidden, transient, nonstationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multisystem physiology (e.g., brain–heart or, more in general, brain–body interactions).

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.

Nowadays in the seismic and geological fields, large and complex data sets are available. This information is a valuable source that can be used for improving the seismic hazard assessment of a given region. In particular, the integration of geologic variables into point process models to study seismic pattern is an open research field that has not been fully explored. In this work, we present several open-access datasets (the catalogue of the earthquakes, geological information such as faults, plate boundary and the presence of volcanoes) that are properly treated to describe the seismicity of events occurred in Greece between 2005 and 2014. We use these datasets to fit an advanced spatial point process model for the description of interaction among the points in thepresence of larger-scale inhomogeneity

etasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquake catalog; non-parametric background seismicity can be estimated through a forward predictive likelihood approach, while parametric components of triggered seismicity are estimated through maximum likelihood; estimation steps are alternated until convergence is obtained and for each event the probability of being a background event is estimated. The package includes options which allow its wide use. Methods for plot, summary and profile are defined for the main output class object. The paper provides examples of the package's use with description of the underlying R and Fortran routines.

Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and a wide spectrum of autonomic dysfunctions including cardiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population. Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains.

An estimation approach for the semi-parametric intensity function of a particular space-time point process is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or one belonging to a seismic sequence is therefore estimated.