Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
SM8.3 | Study of site effects using emerging methods on big data and AI
Study of site effects using emerging methods on big data and AI
Convener: Fabian Bonilla | Co-convener: Fabrice Cotton
In the last twenty years, dense accelerometer networks have been installed worldwide. The recorded data show that the local geology can strongly affect the ground motion by augmenting the site amplification, the duration of the signal, and the spatial variability of local site effects. In certain cases, when the ground motion is strong enough, the material may develop large deformations that alter the physical properties of the medium reducing the shear modulus, increasing the damping, producing liquefaction and permanent displacements among other things. These phenomena are the so-called nonlinear site effects.

Concomitantly, numerical simulations of wave propagation have exponentially increased in the last years, mainly due to the increasing computer power and the development of greatly optimized codes. This makes possible to  tackle  more complex problems,  and in some  cases,  to  go  to higher  frequencies.

In spite of these advances, there are still some problems to be solved, and one of them is the media characterization, this is, the velocity model at different scales that will control the wave propagation. In particular, we lack of a detailed description of the shallow geology that may strongly affect the high frequency ground motion due to the geometry of the local structures,  and  the  rheology  of  the  material  when  strong  motion could  trigger  nonlinear  soil response and pore pressure excess. The combination of these processes definitely influences the resulting ground motion at the Earth’s surface, and more importantly, its uncertainty. This session aims to present studies related to these research subjects, from field data analyses (site response studies), numerical simulations, case studies from recent events, high frequency attenuation (kappa), spatial variability of ground motion (including microzonation studies) and new results using new observables (e.g. DAS data) and/or emerging methods from AI to analyze large ground motion databases to develop proxies to predict site response.