Enhancing ShakeMaps using crowdsourced smartphone data and macroseismic information through spatial statistical modelling

The assessment of ground shaking at high spatial resolution after a recent or future earthquake is crucial for rapid impact assessment and risk management. This is even more important in the urban context, where small-scale differences can have a significant effect on the impact of the earthquake on people and property. Classical seismological networks, however, are usually too sparse to capture the variability of ground shaking at high spatial resolution. In this paper, we show how a multivariate spatial statistical model can be used to improve ShakeMaps by integrating station data (e.g. peak ground accelerations), data from citizen science initiatives (e.g. smartphone accelerations and felt reports), and macroseismic data. 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 and co-located information, and provides the ShakeMap uncertainty in a natural way.

Our approach is applied to events measured by a seismological network and by the smartphones of the Earthquake Network citizen science initiative, and for which felt reports from the LastQuake app of the European-Mediterranean Seismological Centre and macroseismic information by the Italian National Institute of Geophysics and Volcanology are available.