Hellenic Unified Seismological Network: an evaluation of its performance through SNES method

Abstract

S U M M A R YIn this paper, we analyse the location performance of the Hellenic (Greek) Unified Seismological Network (HUSN) by Seismic Network Evaluation through Simulation method (SNES). This method gives, as a function of magnitude, hypocentral depth and confidence level, the spatial distribution of the: number of active stations in the location procedure and their relative azimuthal gaps and confidence intervals in hypocentral parameters regarding both the geometry of the seismic network and the use of an inadequate velocity model.Greece is located on a tectonically active plate boundary at the convergence of the Eurasian and African lithospheric plates and exhibits a high level of seismicity. The HUSN monitors the seismicity in Greek territory from 2007. At present it is composed by 88 seismic stations appropriately distribute in the area of Greece.The application of the SNES method permitted us to evaluate the background noise levels recorded by the network stations and estimate an empirical law that links the variance of P and S traveltime residuals to hypocentral distance. The statistical analysis of the P and S traveltime residuals allowed us to assess the appropriateness of the velocity model used by the HUSN in the location routine process.We constructed SNES maps for magnitudes (M L ) of 2, 2.5 and 3, fixing the hypocentral depth to 10 km and the confidence level to 95 per cent. We also investigated, by two different vertical sections, the behaviour of the errors in hypocentral parameters estimates as function of depth. Finally, we also evaluated, fixing the hypocentral depth to 10 km and the confidence level to 95 per cent, the Magnitude of Completeness.Through the application of the SNES method, we demonstrate that the HUSN provides the best monitoring coverage in western Greece with errors, that for M L = 2.5, are less than 2 and 5 km for epicentre and hypocentral depth, respectively. At magnitude 2.5, this seismic network is capable of constraining earthquake hypocentres to depths of about 160 km and more, and provides a threshold of completeness down to magnitude 2 for most of Greek territory.We delineate some seismogenic areas of southern Greece that probably are not adequately covered by HUSN. Use of the SNES technique could optimize upgrades of the network in these areas.

Gutenberg-Richter Law Parameters Analysis Using the Hellenic Unified Seismic Network Data Through FastBee Technique

“…These findings is in good agreement with the studies [6,23], which analyzed the location performance of the HUSN network by numerical simulation of the detecting earthquakes.…”

“…Parameters According to the HUSN Earthquakes Catalogue The reliability of these finding is supported by the coincidence of the results for the location performance of stations HUSN [23,6]. In these studies showed that for the major part of Greek territory Mc = 2,0.…”

“…3) was chosen, inside one of a more seismic active zone of the central Greece, in the western part of the Corinth Gulf. The recent studies of HUSN performance have shown that in this area the calculation accuracy of the seismic parameters on the average equals the 1km for the epicenter and 2km for focal depth [6]. The data sampling was taken from the rectangular area with size 75x75 km and in the range of depth 1-35 km (Fig.…”

“…In the early operation period of the HUSN network the location performance of this seismic network have been evaluated by numerical simulation in detecting earthquakes [23,6]. These results have shown that in most parts of Greece, the minimum completeness magnitude Mc is expected to drop down to magnitude 2.0 M, with accuracy of the epicenter and depth determination less than 2 and 5 Km respectively.…”

Retrieval of Ocean Bottom and Downhole Seismic sensors orientation using integrated MEMS gyroscope and direct rotation measurements

“…As well known, the magnitude detection threshold and location performance of a seismic network are mainly related to the noise level and geometry of the stations that make up the network (D'Alessandro et al, 2011a(D'Alessandro et al, , b, 2012b(D'Alessandro et al, , 2013bD'Alessandro and Stickney, 2012;D'Alessandro and Ruppert, 2012;D'Alessandro and D'Anna, 2013). OBS are increasingly used to monitoring offshore areas while downhole sensors always have been used to reduce natural noise level.…”