1 1 2 DECREASING GROUND MOTION UNCERTAINTY ( SIGMA ) THROUGH SITE MONITORING AND CHARACTERISATION : THE EXAMPLE OF EUROSEISTEST 1

1 2 DECREASING GROUND MOTION UNCERTAINTY (SIGMA) THROUGH SITE MONITORING AND CHARACTERISATION: THE EXAMPLE OF EUROSEISTEST 1 Olga-Joan KTENIDOU, Zafeiria ROUMELIOTI, Norman ABRAHAMSON, Fabrice COTTON, and Kyriazis PITILAKIS 1 2 In PSHA, the greatest reduction of uncertainty in seismic risk can be gained by reducing uncertainty in ground motion estimation, which is primarily done with ground motion prediction equations (GMPEs). The global aleatory uncertainty (σ) in GMPEs is large, despite recent improvements in networks and data. One reason is that, in the ergodic approach, we combine data in space to make up for lack of data in time (Anderson and Brune, 1999). To reduce σ, first we need adequate data to estimate the systematic site and path response on a deterministic case-to-case basis, and then remove its (now epistemic) uncertainty from the global (aleatory) uncertainty. Then we can make site-specific or even path-specific GMPEs, and substitute global σ with a lower site(or path-) specific uncertainty (single-station σ, or φss after Al Atik et al., 2010). Site monitoring and characterisation (e.g., seismological stations located within critical facilities, on-site geotechnical or geophysical measurements) help characterise site-specific properties (e.g. local attenuation and/or amplification) and reduce part of the seismic hazard uncertainties. To date, no clear cost-benefit analyses has been conducted on how such on-site measurements contribute to this reduction. In this work we show the impact of monitoring and characterisation on the uncertainty of the ground motion estimates. For this study of single station sigma we choose the EUROSEISTEST valley (Figure 1), a geologically complex and seismically active region with a permanent strong motion array consisting of 14 surface and 6 downhole stations (Pitilakis et al., 2013; http://euroseisdb.civil.auth.gr). Site conditions range from soft sediments to hard rock, with Vs30 ranging from 190 to 1840 m/s (EC8 classes A to C/D). We create a database of 690 records based on quality criteria. The final dataset includes 133 crustal events, with moment magnitudes from M2.0-M5.6 and depths less than 16 km, recorded at 22 stations, at distances from 5-220 km (Figure 2). One of the distinct advantages of our dataset is the good existing knowledge of site conditions, which is a result of more than 20 years of geophysical, geotechnical, and site response studies. The key to using lower σ values in site-specific GMPEs is being able to achieve a reliable site-specific site/path term. This knowledge makes it possible to test our analysis for different levels of site information, starting from crude soil vs. rock descriptions, and moving to a more elaborate site description based on Vs30. Another advantage of this dataset is that the events have been carefully relocated and magnitudes re-estimated (this study; Galanis, 2010) with respect to routine parameters retrieved from monthly seismic bulletins. Hence, we can also test our analysis for different levels of source information, i.e. for the typical catalogue quality and for expert relocation quality. We create a simple GMPE from our data, constraining it with the help of global parameters. We then analyse between-event and within-event residuals to compute standard deviations. First we