Estimation of MCS intensity for Italy from high quality accelerometric data, using GMICEs and Gaussian Na\xefve Bayes Classifiers

Abstract

Macroseismic intensity provides a qualitative description of seismic damage. It can be associated with Ground Motion Parameters (GMPs), which are extracted in near real-time from instrumental recordings during an earthquake. Several formulations of this empirical association exist in literature for Italy, mainly focusing on the relationship between intensity expressed on the Mercalli-Cancani-Sieberg (MCS) scale and peak ground acceleration or velocity. They are usually in the form of Ground Motion to Intensity Conversion Equations (GMICEs), which treat intensity as a continuous quantity. We propose an alternative approach, the Gaussian Naïve Bayes (GNB) classifiers, which allows to correctly treat intensity according to its ordinal definition. As a comparison, we also implement a modified version of the standard GMICE approach. We expand the existing database of GMP/MCS-intensity points with new, high-quality accelerometric data recorded in Italy in the period from 2002 to 2016 and resample the database by treating the intermediate intensities with half integer values (which are not meaningful in the MCS description) as both belong to the above and below full integer classes with an assigned weight. As a result, we estimate a new set of regression relations and GNB probability distributions between integer MCS intensity classes and eight GMPs (peak acceleration, velocity, displacement, Arias and Housner intensities, spectral acceleration at 0.3, 1.0 and 3.0 s). Results based on PGA and PGV are the most stable on the whole intensity scale. GNB models score better than GMICEs in terms of performance on unseen data and classification scores.