Tuning Antelope configuration for best earthquake location

The large amount of digital data recorded by permanent and temporary seismic networks makes automatic analysis of seismograms and automatic wave onset time picking schemes of great importance for timely and accurate earthquake locations. Since 2002 the Centro di Ricerche Sismologiche (CRS, Seismological Research Center, http://www.crs.inogs.it/) of the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS, Italian National Institute for Oceanography and Experimental Geophysics) is involved in the EU Interreg IIIA project Trans-national seismological networks in the South-Eastern Alps together with other four institutions: the Earth Science Department of the Trieste University in Italy, the Civil Protection Department of the Friuli-Venezia Giulia Autonomous Region (DPCFVG) in Italy, the Environmental Agency of the Republic of Slovenia (ARSO), and the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The Antelope software suite has been chosen as the common basis for real-time data exchange, rapid location of earthquakes and alerting. Each institution has an instance of Antelope running at its data center and acquires data in real-time from its seismic stations and those of the other partners. Antelope detects events by STA/LTA algorithm and the association is based on location by grid search. The actual set up for fast location capabilities uses only P arrivals. The location is performed by grid search over 87x81 nodes for an extension of 7x6.4 degrees (corresponding to cells of 8.9 km in longitude and 8.7 km in latitude) centered in Lat=46.26o, Lon=13.28o with depth steps at 0, 2, 4, 6, 8, 10, 12, 14, 16, 20 and 24 km, using the 1D uniform velocity model IASPEI91. Recently the CRS acquired a new SUN cluster hardware: consequently a new set up of the Antelope software suite has been tested to improve location accuracy using a denser grid and also S-phases arrivals. The results of the performances of the new configuration will be shown; in particular, we compute the variance of the differences between the location data sets of the two different configurations, inferring the precision of each data set by comparing them with the reference OGS bulletin database. We adopt the recall, precision and accuracy estimators to appraise objectively the results and compare them with those of the other datasets.