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.
Highlights
After the occurrence of a large earthquake, the aim of the civil protection unit is to rapidly assess spatial distribution of damage levels with special attention to highest degrees
To guarantee the homogeneity of the database, we only considered macroseismic intensity measures issued from expert surveys
For any variable X taken among the eight selected Ground Motion Parameters (GMPs), and the categorical variable I which is dependent on variable X, a Naïve Bayes classifier predicts the conditional probability distribution of I given log X by using Bayes’ rule: Pr Pr (I)
Summary
After the occurrence of a large earthquake, the aim of the civil protection unit is to rapidly assess spatial distribution of damage levels with special attention to highest degrees. The first main implication of this fact is that the classes are not proportional to one another, meaning that there is no assurance that the effects observed for a degree II are two times those of a degree I (while for example we can exactly define the proportionality between the energy released by a MW = 4.0 and a MW = 5.0 earthquake) This implies that intensity measures have a high error content, pushing strongly towards the impossibility of interpreting decimal values as an improvement in the actual intensity estimate. In this study, we re-elaborated the Faenza and Michelini (2010) dataset with the addition of 82 new data points related to 18 events which occurred in the time-span from 2002 to 2016 in Italy, using high quality accelerometric data Such data points consist in GMP/MCS-intensity data couples obtained by coupling each expert-assessed intensity value with the nearest available waveform in a 3-km radius. For each one we present both our improved version of the GMICE, as a comparison, and the Naïve Bayes classification, as a suggested best practice
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