The 6 May 1976 Friuli earthquake: re-evaluating and consolidating

The aim of this paper is to propose the creation, in terms of European Macroseismic Scale (EMS-98), of the entire macroseismic fi eld of the 6 May 1976 Friuli earthquake. Only forty odd years have passed, and nothwithsatnding that there is a huge quantity of existing data, it was still disturbing to fi nd that much of the original data are missing and probably lost forever Efforts have therefore been made to fi nd additional and still unknown primary data. For the majority of the collected national data sets, a reevaluation was then possible. This study presents the comprehensive macroseismic data set for 14 European countries. It is, to our knowledge, one of the largest European data sets, consisting of 3423 intensity data points (IDPs). The earthquake was felt from Rome to the Baltic Sea, and from Belgium to Warsaw. The maximum intensity 10 EMS-98 was reached in eight localities in Friuli (Italy). Compared to previous studies, the Imax values have changed from country to country, in some cases being lowered due to methodological differences, but in the case of three among the most hit countries, Imax is now higher than in the previous studies, mainly due to the new data.Published417-4444T. Sismicità dell'ItaliaJCR Journa

Fingerprint resampling: A generic method for efficient resampling

In resampling methods, such as bootstrapping or cross validation, a very similar computational problem (usually an optimization procedure) is solved over and over again for a set of very similar data sets. If it is computationally burdensome to solve this computational problem once, the whole resampling method can become unfeasible. However, because the computational problems and data sets are so similar, the speed of the resampling method may be increased by taking advantage of these similarities in method and data. As a generic solution, we propose to learn the relation between the resampled data sets and their corresponding optima. Using this learned knowledge, we are then able to predict the optima associated with new resampled data sets. First, these predicted optima are used as starting values for the optimization process. Once the predictions become accurate enough, the optimization process may even be omitted completely, thereby greatly decreasing the computational burden. The suggested method is validated using two simple problems (where the results can be verified analytically) and two real-life problems (i.e., the bootstrap of a mixed model and a generalized extreme value distribution). The proposed method led on average to a tenfold increase in speed of the resampling method