Examinando por Autor "Chiodi, Marcello"

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  • Miniatura
    Ítem
    Etas space–time modeling of chile triggered seismicity using covariates: Some preliminary results
    (MDPI, 2021-10) Chiodi, Marcello; Nicolis, Orietta; Adelfio, Giada; D’angelo, Nicoletta; Gonzàlez, Alex
    Chilean seismic activity is one of the strongest in the world. As already shown in previous papers, seismic activity can be usefully described by a space–time branching process, such as the ETAS (Epidemic Type Aftershock Sequences) model, which is a semiparametric model with a large time-scale component for the background seismicity and a small time-scale component for the triggered seismicity. The use of covariates can improve the description of triggered seismicity in the ETAS model, so in this paper, we study the Chilean seismicity separately for the North and South area, using some GPS-related data observed together with ordinary catalog data. Our results show evidence that the use of some covariates can improve the fitting of the ETAS model.
  • Miniatura
    Ítem
    Space‑time clustering of seismic events in Chile using ST‑DBSCAN‑EV algorithm
    (Springer, 2024-06) Nicolis, Orietta; Delgado, Luis; Peralta, Billy; Díaz, Mailiu; Chiodi, Marcello
    Chile is one of the most seismic countries in the world especially due to the subduction of the Nazca plate under the South America plate along the Chilean cost. Normally, the spatial distribution of seismic events tends to form spatial and temporal clusters around the main event including both precursor and aftershock events. However, it is very difficult to identify whether an event is a precursor, a main event or an aftershock. In the literature, only some large earthquakes are well described but it does not exist an automatic method to classify them. In this work, we propose a new density based clustering method, called ST-DBSCAN-EV (Space-time DBSCAN with Epsilon Variable), which allows the Epsilon parameter (the radius) to vary depending on the density of the points. The results of the ST-DBSCAN-EV are validated on three important earthquakes with magnitude greater than 8.0 Mw occurred in Chile in the last 20 years, by carrying out a series of experiments considering different combinations of parameters. A comparison with some traditional clustering techniques such as the DBSCAN, ST-DBSCAN, and the K-means has been implemented for assessing the performance of the proposed method. Almost in all cases ST-DBSCAN-EV outperformed traditional ones by providing an F1-Score metric higher than 0.8. Finally, the results of classification are compared with a declustering method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.