UC Riverside

Societal and economic losses in earthquakes are strongly dependent on the strength of shaking of the ground. Such shaking, or ‘ground motion’ can vary widely from place to place for a given earthquake based on characteristics of the earthquake source, crustal properties affecting seismic wave behavior, and differences in the strength of near-surface geologic material. We investigate the variability in ground motion through a range of methods and techniques and address several of these parameters (e.g., source, path, or site effects) that influence seismic hazard in tectonically active regions, namely: King County in Washington State, U.S. near the Cascadia subduction zone, Bío Bío in Chile near the Atacama Trench, and Christchurch, New Zealand near the Pacific-Australian plate boundary. To study these variations in ground motion, we utilize computational simulations of earthquake hazard and economic loss using the FEMA HAZUS (Federal Emergency Management Agency HAZards U.S) model and seismic data acquired through high-density deployments of small, low-cost micro-electro-mechanical-systems (MEMS) Quake-Catcher Network (QCN) accelerometers. While these sensors are lower-resolution compared to traditional, more expensive seismometers, our results suggest that, with appropriate quality control, QCN sensors provide good-quality data that can be integrated with local networks. We propose that the more economical MEMS sensor technology can drive future studies of seismic hazard and risk at higher spatial resolution than previously available and, with ample amounts of seismic data collected (i.e., “big data”), is posed to revolutionize modern seismology.