Ricerc@Sapienza

In the present project, we consider problem of estimating and forecasting earthquakes in Italy. We refer to real data data from the Catalogo Parametrico dei Terremoti Italiani 2015 (CPTI15) and a homogeneous Italian catalog in terms of moment magnitude (Gasperini et al., 2013).
Standard methods for dealing with this kind of data include nonparametric procedures such as kernel smoothing, which usually rely on too simplistic or unrealistic choices.
As an alternative, we propose a Bayesian nonparametric approach to study Italian seismicity from 1981 to 2009, based on finding Gaussian Markov random field solutions to a class of stochastic partial differential equations
(SPDEs) for adaptive smoothing splines. The model fits naturally within the Integrated Nested Laplace Approximation (INLA) framework, that allows to efficiently estimate the unknown parameters and speed up calculations.
In preliminary studies, we obtained promising results which, however, lack of optimal configurations. So, one first goal of this project is a more in-depth analysis of parameters tuning, for instance priors for the hyperparameters and support triangularization.
We also aim to model temporal features of data. Up to now, in fact, we have considered a "declustered" version of the catalog, in which foreshocks and aftershocks effects are removed, as well as temporal dependence. In real application would be useful to include both spatial and temporal nature of the phenomenon, and this could be done with spatio-temporal modeling.

References
-Gasperini, P., Lolli, B., & Vannucci, G. (2013) Empirical calibration of local magnitude data sets versus moment magnitude in Italy. Bulletin of the Seismological Society of America, 103(4), 2227-2246.
-Rovida, A., Locati, M., Camassi, R., Lolli, B., & Gasperini, P. (2016) CPTI15, the 2015 version of the Parametric Catalogue of Italian Earthquakes. Istituto Nazionale di Geofisica e Vulcanologia.