Seismic scenario simulation and ANN-based ground motion model development on the North Tabriz Fault in Northwest Iran

Abstract

Earthquakes pose significant seismic hazards in urban regions, often causing extensive damage to the built environment. In regions lacking robust seismic monitoring networks or sufficient data from historical events, ground motion simulations are crucial for assessing potential earthquake impacts. Yet, validating these simulations is challenging, leading to notable predictive uncertainty. This study aims to simulate four scenario earthquakes with moment magnitudes of 6.8, 7.1, 7.4, and 7.7 in Iran, specifically investigating variations in fault plane rupture and earthquake hypocenter. The North Tabriz Fault (NTF), located within the seismic gap in northwest Iran, is selected as the case study due to the lack of well-recorded ground motions from severe earthquakes, despite historical evidence of large-magnitude events. Simulations are conducted using a stochastic finite-fault ground motion simulation methodology with a dynamic corner frequency. Validation of the simulations is performed by comparing estimated peak ground motions and pseudo-spectral ordinates with existing ground motion models (GMMs), supplemented by inter-period correlation analysis. Simulation results reveal high hazard levels, especially in the northeastern area near the fault plane. Intensity maps in terms of the Modified Mercalli Intensity (MMI) scale underscore the urgency for comprehensive preparedness measures. Finally, a region-specific GMM is developed using Artificial Neural Networks (ANN) to predict peak ground motion parameters with an online platform accessible to end-users.