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Real-Time Evolutionary Earthquake Location for Seismic Early Warning

RISSC-Lab, AMRA scarl, Via Diocleziano 328, 80124 Napoli, Italy satriano{at}na.infn.it

Anthony Lomax

ALomax Scientific, Mouans-Sartoux, France, 161 Allée du Micocoulier, 06370 Mouans-Sartoux, France anthony{at}alomax.net

Aldo Zollo

RISSC-Lab, Dipartimento di Scienze Fisiche, Università di Napoli Federico II, Via Diocleziano 328, 80124 Napoli, Italy aldo.zollo{at}unina.it

An effective early-warning system must provide probabilistic estimates of the location and size of a potentially destructive earthquake within a few seconds after the event is first detected.

In this work we present an evolutionary, real-time location technique based on an equal differential time (EDT) formulation and a probabilistic approach for describing the hypocenter estimation. The algorithm, at each timestep, relies on the information from triggered arrivals and not-yet-triggered stations. With just one recorded arrival, the hypocentral location is constrained by the Voronoi cell around the first triggering station constructed using the travel times to the not-yet-triggered stations. With two or more triggered arrivals, the location is constrained by the intersection of the volume defined by the Voronoi cells for the remaining, not-yet-triggered stations and the EDT surfaces between all pairs of triggered arrivals. As time passes and more triggers become available, the evolutionary location converges to a standard EDT location.

Synthetic tests performed using the geometry of the Irpinia seismic network, southern Italy (ISNet), and the simulation of an evolutionary location for the 2000 M_w 6.6 Western Tottori, Japan, earthquake indicate that when a dense seismic network is available, reliable location estimates suitable for early-warning applications can be achieved after 1–3 sec from the first event detection. A further simulation with an M_w 6.7 southern Greece earthquake shows that at a regional scale, the real-time location can provide useful constraints on the earthquake position several seconds before a non-real-time algorithm. Finally, we show that the robustness of the algorithm in the presence of outliers can be effectively used to associate phase arrivals coming from events occurring close in time, and we present a preliminary algorithm for event detection.

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