0000000000859116

AUTHOR

Antonino D’alessandro

showing 3 related works from this author

A new picking algorithm based on the variance piecewise constant models

2022

AbstractIn this paper, we propose a novel picking algorithm for the automatic P- and S-waves onset time determination. Our algorithm is based on the variance piecewise constant models of the earthquake waveforms. The effectiveness and robustness of our picking algorithm are tested both on synthetic seismograms and real data. We simulate seismic events with different magnitudes (between 2 and 5) recorded at different epicentral distances (between 10 and 250 km). For the application to real data, we analyse waveforms from the seismic sequence of L’Aquila (Italy), in 2009. The obtained results are compared with those obtained by the application of the classic STA/LTA picking algorithm. Althoug…

variance piecewise constant modelEnvironmental EngineeringEarthquake Early WarningArrival timesChange-pointEnvironmental ChemistrySettore SECS-S/01 - StatisticaSafety Risk Reliability and QualityPickingGeneral Environmental ScienceWater Science and TechnologyStochastic Environmental Research and Risk Assessment
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Local Spatial Log-Gaussian Cox Processes for seismic data

2022

AbstractIn 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 th…

Statistics and ProbabilityEconomics and Econometricsspatial point processeApplied MathematicsModeling and SimulationLog-Gaussian Cox procelocal composite likelihoodPalm likelihoodseismologySettore SECS-S/01 - StatisticaSocial Sciences (miscellaneous)Analysis
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Statistical Picking of Multivariate Waveforms

2022

In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typica…

changes in variationseismogram; seismic phase picking; change points; changes in variation; cumulative segmentationChange pointSeismogram seismic phase pickingcumulative segmentationElectrical and Electronic EngineeringSettore SECS-S/01 - StatisticaBiochemistryInstrumentationAtomic and Molecular Physics and OpticsAnalytical ChemistrySensors
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