GNGTS 2022 - Atti del 40° Convegno Nazionale

194 GNGTS 2022 Sessione 2.1 APPLICATION OF THE EEPAS SEISMIC FORECASTING METHOD TO ITALY E. Biondini 1 , D. Rhoades 2 , P. Gasperini 1,3 1 Dipartimento di Fisica e Astronomia, Università di Bologna, Italy 2 GNS Science, New Zealand 3 Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Italy Abstract. The EEPAS (Every Earthquake a Precursor According to Scale) forecasting model is a space–time point-process model based on the precursory scale increase ( Ψ ) phenomenon and associated predictive scaling relations (Rhoades and Evison, 2004). It has been previously applied to New Zealand, California and Japan earthquakes with target magnitude thresholds varying fromabout 5 to 7. In all previous application, computations weremade by the computer code implemented in Fortran language by the model authors. In this work we applied it to Italy using a suite of computing codes completely rewritten in Matlab and Python. We first compared the two software codes to ensure the convergence and adequate coincidence between the estimated model parameters for a simple region capable of being analyzed by both software codes, then using the rewritten codes we optimized the parameters for a different and more complex polygon of analysis for a retrospective forecasting experiment of Italian earthquakes from 1990 to 2019 with Mw³5.0 and compare its forecasting skill with other forecasting models. Brief introduction on the EEPAS model. EEPAS (Every Earthquake as a Seismic Precursor According to Scale) is a seismic forecastingmethod based on the statistical analysis of seismicity (Rhoades and Evison 2004). Its basic assumption is that magnitudes and rates of minor seismicity increase before a strong shock. The hypothesis on the base of the EEPAS model is that each earthquake is considered as an individual precursor according to their appropriate magnitude scale, rather than as a possible member of a Ψ . The model defines the rate density of earthquakes occurrence λ ( t, m, x, y ) for any time ( t ), magnitude ( m ) and location ( x,y ), where m exceeds a threshold magnitude M T and ( x,y ) is a point in a region of surveillance, R . Each earthquake ( t i  , m i  , x i  , y i ) with t i greater than a starting time, t 0 , and m i greater than a minimum magnitude M C , contributes to a transient increment λ i  ( t, m, x, y ) of the future rate density in its vicinity, given by the multiplication of the probability density function of time (1), magnitude (2) and location(3). (1) (2) (3) Where a M , b M , σ M , a T , b T , σ T , b A , σ A are free parameters of the model. The details of the EEPAS method are described in a number of papers (e. g. Evison and Rhoades 2005; Rhoades, 2011; Rhoades and Evison 2004; Rhoades, 2007). Application to Italy. We applied the EEPAS model to Italy using data from the HORUS - HOmogenized instRUmental Seismic catalog (http://horus.bo.ingv.it ) from 1960 to 2021