Using recent results for local composite likelihood for spatial point processes, we show the performance of advanced and flexible statistical models to describe the spatial displacement of earthquake data. Local models described by Baddeley (2017) allow for the possibility of describing both seismic catalogs and sequences. When analysing seismic sequences, the analysis of the small scale variation is the main issue. The interaction among points is taken into account by Log-Gaussian Cox Processes models through the estimation of the parameters of the covariance of the Gaussian Random Field. In their local version these parameters are allowed to vary spatially, and this is a crucial aspect for describing and characterizing the study area through a multiple underlying process.
Nicoletta D'Angelo, Marianna Siino, Antonino D'Alessandro, & Giada Adelfio (2020). Local LGCP estimation for spatial seismic processes. In Book of short papers - SIS 2020.
|Titolo:||Local LGCP estimation for spatial seismic processes|
D'ANGELO, Nicoletta (Corresponding)
|Data di pubblicazione:||2020|
|Citazione:||Nicoletta D'Angelo, Marianna Siino, Antonino D'Alessandro, & Giada Adelfio (2020). Local LGCP estimation for spatial seismic processes. In Book of short papers - SIS 2020.|
|Appare nelle tipologie:||2.08 Abstract in atti di convegno pubblicato in volume|