A machine-learning approach for the reconstruction of ground shaking fields in real-time

Real-time monitoring is of primary importance for rapid and targeted emergency operations after potentially destructive earthquakes. A key aspect in determining the impact of an earthquake is the reconstruction of the ground shaking field, usually expressed as a peak ground parameter. Traditional algorithms approach this task by computing the ground shaking field from the punctual data at the stations and relying on ground motion prediction equations (GMPEs) computed on estimates of the earthquake location where the instrumental data are missing. The results of such algorithms are then subordinate to the evaluation of location and magnitude which can take several minutes. To fill the gap between the arrival of the data and the (first preliminary) estimation (usually computed in a few minutes), we introduce a new data-driven algorithm that exploits the information from the station data only.