Automated seismic event location by waveform coherence analysis

Automated location of seismic events is a very important task in microseismic monitoring operations as well for local and regional seismic monitoring. Since microseismic records are generally characterized by low signal-to-noise ratio, automated location methods are requested to be noise robust and sufficiently accurate. Most of the standard automated location routines are based on the automated picking, identification and association of the first arrivals of P and S waves and on the minimization of the residuals between theoretical and observed arrival times of the considered seismic phases. Although current methods can accurately pick P onsets, the automatic picking of the S onset is still problematic, especially when the P coda overlaps the S wave onset. In this paper, we propose a picking free earthquake location method based on the use of the short-term-average/long-term-average (STA/LTA) traces at different stations as observed data. For the P phases, we use the STA/LTA traces of the vertical energy function, whereas for the S phases, we use the STA/LTA traces of a second characteristic function, which is obtained using the principal component analysis technique. In order to locate the seismic event, we scan the space of possible hypocentral locations and origin times, and stack the STA/LTA traces along the theoretical arrival time surface for both P and S phases. Iterating this procedure on a 3-D grid, we retrieve a multidimensional matrix whose absolute maximum corresponds to the spatial coordinates of the seismic event. A pilot application was performed in the Campania-Lucania region (southern Italy) using a seismic network (Irpinia Seismic Network) with an aperture of about 150 km. We located 196 crustal earthquakes (depth < 20 km) with magnitude range 1.1 < ML < 2.7. A subset of these locations were compared with accurate manual locations refined by using a double-difference technique. Our results indicate a good agreement with manual locations. Moreover, our method is noise robust and performs better than classical location methods based on the automatic picking of the P and S waves first arrivals.