Publication Date:
2024-05-09
Description:
This article has been accepted for publication in Geophysical Journal International ©:The Author(s) 2023. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Uploaded in accordance with the publisher's self-archiving policy.
All rights reserved.
Description:
The Every Earthquake a Precursor According to Scale (EEPAS) forecasting model is a space– time point-process model based on the precursory scale increase (ψ ) phenomenon and associated predictive scaling relations. It has been previously applied to New Zealand, Cal- ifornia and Japan earthquakes with target magnitude thresholds varying from about 5–7. In all previous application, computations were done using 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. 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 analysed by both software codes. Then, using the rewritten codes, we optimized the parameters for a different and more complex polygon of analysis using the Homogenized Instrumental Seismic Catalogue data from 1990 to 2011. We then perform a pseudo-prospective forecasting experiment of Italian earthquakes from 2012 to 2021 with Mw ≥ 5.0 and compare the forecasting skill of EEPAS with those obtained by other time in- dependent (Spatially Uniform Poisson, Spatially Variable Poisson and PPE: Proximity to Past Earthquakes) and time dependent [Epidemic Type Aftershock Sequence (ETAS)] forecasting models using the information gain per active cell. The preference goes to the ETAS model for short time intervals (3 months) and to the EEPAS model for longer time intervals (6 months to 10 yr).
Description:
Published
Description:
1681–1700
Description:
OST4 Descrizione in tempo reale del terremoto, del maremoto, loro predicibilità e impatto
Description:
JCR Journal
Keywords:
Computational seismology
;
Earthquake interaction
;
forecasting and prediction
;
Statistical seismology
;
Earthquake forecasting
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
