Bragato, P. L.

Gentili, S.

Pesaresi, D.

Snidarcig, A.

Earth-prints Repository

PERFORMANCES OF TWO AUTOMATIC EARTHQUAKE
 LOCATION SYSTEMS IN THE NORTH-EASTERN ITALY
S. Gentili(1),  P. L. Bragato (1), D. Pesaresi (1,2) and  A. Snidarcig(1)
(1) Ist. Naz. di Oceanografia e di Geofisica Sperimentale (OGS), (2) Ist. Naz. Di Geofisica e Vulcanologia (INGV)
Centro di Ricerche
Sismologiche
ISTITUTO NAZIONALE
DI OCEANOGRAFIA E
DI GEOFISICA SPERIMENTALE
the common basis for near real-time data exchange, rapid location of S i n c e  
the earthquakes and alerting. Each institution has an instance of the system running 1996 at 
C e n t r o  d i  at its data center and acquires data in near real-time from its stations and those 
Ricerche Sismologiche (CRS, http://www.crs.inogs.it) of the Istituto Nazionale di of the other partners (Fig.1).   
performance Oceanografia e di Geofisica Sperimentale (OGS) In this poster, the FAAS and Antelope are analyzed. Their  it is active the Friuli Automatic 
procedures for earthquake detection, picking, location and magnitude Alert System (FAAS, Bragato and Govoni; 1999) which monitors the Friuli-Venezia 
and the surrounding area estimation are shortly described and the results compared with the manually Giulia region (NE Italy)  based on 21 short period 
(in Fig.1 revised data available in the NEI North Eastern Italy) network bulletin (OGS stations (red triangles , Priolo et al.; 2005). 
database) from December 2005 to June 2006. We analyze the detection Since 2002 the CRS is involved in the EU Interreg IIIA project “Trans-national 
capabilities, quality of time arrival picks and locations and the differences seismological networks in the South-Eastern Alps” together to other four 
among the various magnitudes (M  and M ). In particular, for pickings and institutions monitoring the area (Bragato et al, 2003): DST of Trieste University and 
L D
Civil Protection of Regione Autonoma Friuli-Venezia Giulia (Italy), ARSO locations we furnish an absolute estimation of the error in respect to the real, 
(Slovenia), and ZAMG (Austria). The Antelope software suite has been chosen as unknown values.
Two automatic systems
Fig. 1: Station currently acquired at CRS
Antelope: 
event detection: STA/LTA detection + association based on location by grid 
search; 
picking: only P arrivals are used;
location: grid search over 87X81 nodes for an extension of 7x6.4 degrees 
centered in 46.26, 13.28 (Fig.4) with dept 
steps at 0, 2, 4, 6, 8, 10, 12, 14, 16, 
20 and  24 km, using the uniform 
velocity 1D model IASPEI91. The 
location procedure has been 
mainly  set-up and tuned at DST, 
Trieste.
magnitude: M  estimated using 
L
the program “orbampmag” 
developed by Nikolaus Horn at 
ZAMG, Vienna.
Fig 6: Antelope picks
Fig.5: example of location by Antelope
Processing steps
FAAS: 
event detection: STA/LTA 
triggering on remote stations +  
coincidence between at least 
4 stations;
picking: P and S arrivals;
location: Hypo71 program;
magnitude: M  using the 
D
same formulas as of Rebez and 
Renner (1991); M  according to 
L
the formula by Bakun and 
Joyner (1984).
Fig.3: CRS 
web page 
reporting 
FAAS 
locations
Fig. 2: FAAS picks
Comparison
picking: P waves picking times of FAAS and Antelope 
have been compared  with those in the OGS database. 
From the Figures 7 and 8 it is possible to see that both 
FAAS and Antelope pick some hundredth of seconds 
after the OGS. The mean and the variance have been 
calculated considering the entire data set and after 
eliminating the outliers (picks farther than 1s from the 
corresponding manual ones). Other to the variance 
relative to the manual picks, following Gentili and Bragato (2006) we have estimated the absolute variance of the 
three data sets (i.e. the variance of the difference between the real, unknown arrival times and the picked ones). In 
general, given two independent data sets A and B, it holds the relation var(A-B)=var(A)+var(B). Combining in a system 
the three equations derived for the available data sets, we have obtained:
var(Antelope)=0.014s,   var(FAAS)=0.014s,   var(OGS)=0,009s
location: regarding the accuracy in location, 
the systematic error of both FAAS and Antelope 
relative to the OGS bulletin (m in figures 18-23) is of 
the order or smaller than 1 km, while the variance 
is much higher for Antelope than for FAAS. Using 
the same method described above for the picks 
and considering the small area monitored by 
FAAS, we have estimated the absolute variance 
of the locations in the three data sets referred to 
the real, unknown epicenters: 
var (Antelope)=31km  var (Antelope)=15 km
LONG ,  LAT
 var (FAAS)=8 km  var (FAAS)=4 km
LONG ,  LAT
 var (OGS)=0km  var (OGS)=2 km
LONG  ,  LAT
FAAS performs better than Antelope. Such result is 
probably related to the use of S picks by FAAS.
event detection: the capability of detecting earthquakes depending on their 
magnitude has been analyzed (maps in the Figures 9-11 and histograms in the 
Figures 12-17). For the two systems we have considered the area monitored by 
FAAS (coordinates box: LON=[12 14],LAT=[45.5 47]). For Antelope we have also 
considered  the larger transfrontalier area of interest for the INTERREG project 
(coordinates box: LON=[12.1 15.7], LAT=[44.5 47.3]). It emerges that for the smaller 
box, the maximum magnitude of not detected earthquakes is 2.7 for FAAS and 2.9 
for Antelope. For the larger box, the maximum magnitude of the events lost by 
Antelope is 3.3.
magnitude: In order to understand if the M  and M of the three system are coherent, we plot 
D L 
the comparison among the magnitude of the different databases. The OGS bulletin reports M , 
D
which is also computed by FAAS. Figures 24 shows how FAAS underestimates M  by about 0.2, 
D
which is within the standard deviation of the calibration of M  (about 0.3). Both Antelope and FAAS 
D
compute M  (Figure 25): the latter tends to underestimate M  and such result is mainly related to 
L L
the use of a different attenuation law.  
References
Bakun, W., and W. Joyner; 1984: The ML scale in central California. Bull. Seis. Soc. Am., 74, earthquakes in Northeastern Italy, Journal of Seismology, Vol 10, No. 1, pp. 73-89
1827-1843.  
Priolo, E., Barnaba, C., Bernardi, P., Bernardis, G., Bragato, P.L., Bressan, G., Candido, M., 
Bragato P.L., Costa G., Horn N., Michelini A., Mocnic G. and Zivcic M.; 2003: Real-time data Cazzador, E., Di Bartolomeo, P., Durì, G., Gentili, S., Govoni, A., Klinc, P., Kravanja, S., 
and network integration in the southern Alps, Geoph. Res. Abstracts, 5, paper number 08690. Laurenzano, G., Lovisa, L., Marotta, P., Michelini, A., Ponton, F., Restivo, A., Romanelli, M., 
Snidarcig, A., Urban, S., Vuan, A. and Zuliani D.; 2005: Seismic monitoring in Northeastern Italy: 
Bragato P.L. and Govoni A.; 1999: The Friuli automatic earthquake alert system. Boll. Geof. 
a ten-year experience, Seismological Research Letters, Vol. 76, No. 4, pp. 446-454.
Teor. App., 40, 59-77.
Rebez, A. and Renner G.; 1991: Duration magnitude for the northeastern Italy seismometric 
Gentili S. and Bragato P.L.; 2006: A neural-tree-based system for automatic location of 
network. Boll. Geof. Teor. Appl., 33, 177-186.
Fig.4: Antelope location grid 
Fig 11
Fig 24
Fig 25
Fig 14
Fig 13
Fig 12
Fig 17
Fig 16
Fig 15
Fig 20
Fig 19
Fig 18
Fig. 23
Fig 22
Fig 21
Fig 9
Fig 10
Fig 8Fig 7
Fig. 1: Station currently acquired at CRS in the 
framework of the project “Trans-national 
seismological networks in the South-Eastern Alps”


Performances of two automatic earthquake location systems in the north-eastern italy

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Performances of two automatic earthquake location systems in the north-eastern italy

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