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

Area of Science:

• Earthquake seismology and seismic hazard assessment.
• Computational geophysics and ground motion simulation.

Background:

• Urban regions face significant seismic hazards, with damage often exacerbated by a lack of historical seismic data and robust monitoring networks.
• Ground motion simulations are vital for assessing earthquake impacts but face challenges in validation and predictive uncertainty.

Purpose of the Study:

• To simulate seismic ground motions for four scenario earthquakes (Mw 6.8–7.7) in Iran, focusing on the North Tabriz Fault (NTF).
• To investigate the influence of fault plane rupture and hypocenter variations on ground motion.
• To validate simulations and develop a region-specific ground motion model (GMM) for improved hazard assessment.

Main Methods:

• Stochastic finite-fault ground motion simulation methodology with dynamic corner frequency.
• Validation through comparison with existing Ground Motion Models (GMMs) and inter-period correlation analysis.
• Development of a region-specific GMM using Artificial Neural Networks (ANN).

Main Results:

• Simulations indicate high seismic hazard levels, particularly in the northeastern areas adjacent to the North Tabriz Fault.
• Intensity maps using the Modified Mercalli Intensity (MMI) scale highlight the need for urgent preparedness measures.
• The developed ANN-based GMM accurately predicts peak ground motion parameters.

Conclusions:

• Ground motion simulations are essential for understanding seismic hazards in data-scarce regions like the NTF area.
• The study underscores the critical need for enhanced seismic preparedness in urban areas prone to significant earthquakes.
• An accessible online platform featuring the region-specific GMM can aid end-users in seismic risk evaluation.