A deep neural network framework for real-time on-site estimation of acceleration response spectra of seismic ground motions

Abstract

Various earthquake early warning (EEW) methodologies have been proposed globally for speedily estimating information (i.e., location, magnitude, ground-shaking intensities, and/or potential consequences) about ongoing seismic events for real-time/near real-time earthquake risk management. Conventional EEW algorithms have often been based on the inferred physics of a fault rupture combined with simplified empirical models to estimate the source parameters and intensity measures of interest. Given the recent boost in computational resources, data-driven methods/models are now widely accepted as effective alternatives for EEW. This study introduces a highly accurate deep-learning-based computational framework named ROSERS (i.e., Real-time On-Site Estimation of Response Spectra) to estimate the acceleration response spectrum (Sa(T)) of the expected on-site ground-motion waveforms using early non-damage-causing early p-waves and site characteristics. The framework is trained using a carefully selected extensive database of recorded ground motions. Due to the well-known correlation of Sa(T) with structures’ seismic response and resulting damage/losses, rapid and accurate knowledge of expected on-site Sa(T) values is highly beneficial to various end-users to make well-informed real-time and near-real-time decisions. The framework is thoroughly assessed and investigated through multiple statistical tests under three historical earthquake events. These analyses demonstrate that the overall framework leads to excellent prediction power and, on average, has an accuracy above 85% for hazard-consistent early-warning trigger classification