Estimation of MCS intensity for Italy from high quality accelerometric data, using GMICEs and Gaussian Naïve 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.

Near-Real-Time Strong Motion Acquisition at National Scale and Automatic Analysis

“…Impulsive motions may occur due to directivity [ 48 ] and fling step [ 49 ] effects along with local soil conditions [ 50 ]. Cataldi et al [ 18 ] proposed a parallel implementation of the instrumental intensity definition, more compliant with the Mercalli–Cancani–Sieberg (MCS) scale. In fact, even if it is common practice to use half-integer intensity values both in macroseismic reports and for instrumental intensity definitions, the MCS scale only defines integer intensity classes.…”

“…Once the ground motion parameters have been calculated, for civil protection purposes, it is also necessary to give information about the possible spatial distribution and degree of the expected damage. In the literature, this information is provided by the macroseismic intensity [ 18 , 42 ]. Intensity is a well-known parameter that explains the effects of the ground motion on a given location and structure and on humans.…”

“…By using a quasi-real time waveform, information related to the seismic event (e.g., seismic moment— and moment magnitude— ) and its effect on the ground where stations are positioned (e.g., the peak ground acceleration, velocity, and displacement (PGA, PGV, and PGD, respectively), Housner intensity ( ), Arias intensity ( ), and spectral acceleration (SA) at different times, commonly at , , and ) can be retrieved [ 18 ]. Combinations of this information help scientists and engineers to determine the possible damage to structures and infrastructures and the possibility of triggering other natural phenomena (e.g., landslide, soil liquefaction) [ 2 , 4 , 18 , 19 ].…”

Quality Classification of Palm Oil Products Using Naïve Bayes Method

“…dimana σ adalah standar deviasi dan µ = mean. Pengklasifikasian Gaussian Naïve Bayes memungkinkan untuk memperlakukan intensitas menurut definisi ordinalnya dengan benar [15]. Berikut dilanjutkan dengan menghitung nilai likelihood dengan menggunakan persamaan pada rumus 2.…”

A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics

“…The GNB model is based on the naive bayes methods and theorem. The GNB technique [45] has the powerful assumption that all the predictors are independent of each other. One feature in a class is independent of another feature in the same class.…”