Enhancing microzonation, ground motion models and ShakeMaps through the spatial statistical modelling of seismological station and crowdsourced smartphone data

Assessing ground shaking at a high spatial resolution after a recent or future earthquake is crucial for a rapid impact assessment and risk management. This is particularly important in urban areas, where small-scale differences can significantly affect the impact of an earthquake on people and property. However, classical seismological networks are usually too sparse to capture the variability of ground shaking at such a high spatial resolution. In this study, we demonstrate how a multivariate spatial statistical model can enhance ShakeMaps by combining station data (e.g. peak ground accelerations) with information from Earthquake Network citizen science initiatives (e.g. smartphone accelerations). The statistical model accounts for the heterogeneity of the data sources in terms of spatial density, measurement uncertainty, and bias. The model achieves data fusion without the need for calibration relationships or co-located information and naturally provides ShakeMap uncertainty.

We apply our approach to the highly monitored area of Campi Flegrei in Italy, where the Earthquake Network initiative involves around 9,000 participants and smartphones. By combining the data gathered from multiple seismic events, we also demonstrate how to generate a high-resolution amplification map of the area, which is useful for enhancing ground motion models.