Development of an hybrid GMPE-less ShakeMap implementation for real-time ground shaking maps reconstruction

Ground shaking maps are an essential tool for seismic monitoring and civil defence operations as they provide information about the area and amplitude of the ground motion relative to a seismic event. Such maps are developed integrating spatially sparse data recorded by the stations, which also provide a constraint to the process, and theoretical values obtained from ground motion prediction equations (GMPEs), given the magnitude and location of the earthquake, also accounting for local site effects. One of the problems arising during the development of a real-time implementation of these techniques is the lack of information in real-time needed to compute the GMPE. One possible solution to the problem is to develop algorithms that can constrain the interpolation process using only the ground motion parameters recorded at the stations. We propose a hybrid model combining the conditioned multivariate normal distribution (MVN) technique adopted by ShakeMap and a neural network replacing the GMPE.