SM8.4 | Physics-based ground motion simulation to unravel complex basin site effects for seismic hazard assessment
Physics-based ground motion simulation to unravel complex basin site effects for seismic hazard assessment
Convener: Afifa Imtiaz | Co-conveners: Maria KoroniECSECS, Ebru Bozdag, cecile cornou

Urban areas are usually founded on sedimentary basins where the amplification of strong ground motion poses significant risks to residents and infrastructures. The intensity of such ground shaking is a multifaceted phenomenon influenced by various factors related to site and source characteristics. Physics-based ground motion modelling is nowadays becoming a standard procedure to predict and understand complex ground shaking by using detailed 3-D Earth models. However, the potential of such modelling to advance seismic hazard analysis hinges on factors such as their accuracy, flexibility (e.g., meshing capabilities), and accessibility (e.g., software efficiency).

This session aims to explore the complex basin effects on ground motions and associated seismic hazards through physics-based simulations. Topics of interest include advancements in characterizing basin structure at local and regional scales, high-frequency and high-fidelity 3D numerical simulation, propagation of uncertainties on the basin geometrical and mechanical properties into surface ground motion, complex interaction between near-fault ruptures and basin seismic response.

We seek topics that explore techniques to incorporate variable-resolution features such as surface topography, small-scale heterogeneity, and frequency-dependent anelastic attenuation into ground motion simulation as well as methods to integrate multi-scale features such as fault damage zones, near-surface weathering layers, geotechnical data, and other geophysical and geological information. We further invite studies that showcase the application of such complex velocity, attenuation, and structural models in seismic hazard assessment. We encourage submissions based on conventional approaches as well as innovative methods using machine learning and artificial intelligence.