On the rapid magnitude estimation via empirical fitting of the Brune source model from high-rate GPS solutions

Nowadays, Earthquake and Tsunami Early Warning Systems (ETEWSs) worldwide rely on ground-motion observations from strong-motion accelerometers and broadband seismometers. These measurements enable the rapid estimation of magnitude, hypocenter, and other source parameters, allowing the location and intensity of strong shaking to be determined. Although ETEWSs perform well in estimating the magnitude of small-to-moderate earthquakes, traditional inertial sensors generally struggle to capture the full dynamic range of ground displacements, particularly at low frequencies, i.e., below the relative corner frequency. This limitation becomes especially pronounced during large earthquakes (Mw > 7), which are dominated by near-field body forces generated at the source. Consequently, early warning algorithms face significant challenges in estimating source parameters in real time, particularly for these highly damaging, large-magnitude events that are potentially tsunamigenic provided seafloor deformation is involved.

To address this limitation, geodetic data - specifically GNSS displacements - have recently been incorporated into early warning algorithms and ground-motion models, serving as a critical complement to traditional seismic approaches. This study focuses on the use of high-rate Global Navigation Satellite System (GNSS) observations (>1 Hz), which provide high-fidelity recordings of ground displacement that are essential for rapid magnitude estimation.

We analyze some moderate-magnitude (Mw 5 - 6.5) seismic events in the Mediterranean region for which high-rate GNSS solutions are available. For each event with a known moment magnitude (Mw), an empirical scaling factor has been derived by fitting the observed displacement spectrum to the low-frequency plateau of the theoretical Brune source model. The primary objective of this research is to investigate the stability and potential variability of this scaling factor across the compiled event catalogue. Assessing the existence of a robust or “general” scaling factor is crucial, as its reliable determination could be directly applied to rapid magnitude estimation in the immediate aftermath of future moderate and large earthquakes, thereby significantly improving early warning system performance.