In this study, we applied a probabilistic methodology to seismic hazard assessment in metropolitan France. For that purpose we determined an attenuation law adapted to the French context. This law holds for peak ground acceleration on stiff bedrock for earthquakes with local magnitudes between 2.5 and 5.6 recorded in near field (at distances between 3 and 50 km). Geological conditions are taken into account by means of a three-categories classification of lithologies based on a 1/

Many seismological centers now have Web sites that provide real‐time earthquake information. Webmasters who maintain these sites are aware that the daily rate of “hits” to, or views of, the sites' pages increases when an earthquake attracts public and/or media attention. However, many seismic network operators and webmasters may not be fully aware of how abrupt and rapid a hits surge might be following a felt earthquake as people visit the Web pages to find out information about what they have felt. Within 1 minute to a few minutes of an earthquake's occurrence, the European‐Mediterranean Seismological Centre's (EMSC) Web site (http://www.emsc‐csem.org) can detect the surge in hits (Figure 1). At EMSC, we have developed a system to use this information to discriminate felt events regardless of their magnitude, and to gather in situ information on an earthquake's effects within 10–20 minutes of its occurrence—at a time when any such information is critical to evaluating its impact.

No abstract

SUMMARY In low-seismicity areas such as Europe, seismic records do not cover the whole range of variable configurations required for seismic hazard analysis. Usually, a set of empirical models established in such context (the Mediterranean Basin, northeast U.S.A., Japan, etc.) is considered through a logic-tree-based selection process. This approach is mainly based on the scientist’s expertise and ignores the uncertainty in model selection. One important and potential consequence of neglecting model uncertainty is that we assign more precision to our inference than what is warranted by the data, and this leads to overly confident decisions and precision. In this paper, we investigate the Bayesian model averaging (BMA) approach, using nine ground-motion prediction equations (GMPEs) issued from several databases. The BMA method has become an important tool to deal with model uncertainty, especially in empirical settings with large number of potential models and relatively limited number of observations. Two numerical techniques, based on the Markov chain Monte Carlo method and the maximum likelihood estimation approach, for implementing BMA are presented and applied together with around 1000 records issued from the RESORCE-2013 database. In the example considered, it is shown that BMA provides both a hierarchy of GMPEs and an improved out-of-sample predictive performance.

In Europe, the ISO 15 189 standard requires uncertainty of measurement to be calculated for all measurands. We calculated the analytical imprecision and bias of our factor VIII coagulometric assay method between 5 and 80 U/dl, using plasmas expected to be at 5, 30 and 80 U/dl of factor VIII. We implemented Meijer et al.'s [Clin Chem 2002; 48:1011-1015] long-term coefficient of variance, bias and also uncertainty of measurement calculations. Assessments used reference plasma diluted in severe haemophilic plasma, in immunodepleted factor VIII-deficient plasma and in bovine serum albumin. With plasmas diluted in severe haemophilic and immunodepleted factor VIII-deficient plasma, calculated uncertainty of measurement was 10% compared with 15% (i.e., 50% greater) for plasma diluted in albumin buffer or as calculated from European Concerted Action on Thrombosis consensus values. It is thus important to approximate the patient sample matrix to obtain as precise an estimation as possible of assay method uncertainty of measurement.