Developing the First Intensity Prediction Equation Based on the Environmental Scale Intensity: A Case Study from Strong Normal-Faulting Earthquakes in the Italian Apennines

Abstract

AbstractEarthquakes produce effects on the built and natural environment, the severity of which decays with distance from the epicenter. Empirical relations describing the intensity attenuation with distance are fundamental for seismic hazard assessment and for deriving parameters for preinstrumental events. Seismic intensity is usually assigned based on damage to buildings and infrastructures; this can be challenging for intensity degrees higher than X or when macroseismic fields of multiple events close in time are overlapping. A complementary approach is the study of earthquake environmental effects (EEEs), which are used to assign intensity on the environmental scale intensity (ESI) scale. However, a quantitative comparison between the ESI and traditional scales, and an equation describing the ESI attenuation with distance are still lacking. Here, we analyze 14 historical and instrumental events (time window 1688–2016) in the central and southern Apennines (Italy), comparing ESI and Mercalli–Cancani–Sieberg (MCS) intensities. Our results show that ESI consistently provides higher intensity near the epicenter and the attenuation is steeper than MCS. We derive the first intensity prediction equation for the ESI scale, which computes local intensity as a function of distance and epicentral intensity value. We document that, in the near field, the MCS attenuation for shallow crustal events occurred in the twenty-first century is steeper than previous events, whereas the ESI attenuation shows a consistent behavior through time. This result questions the reliability of current empirical relations for the investigation of future events. We recommend including EEEs in intensity assignments because they can guarantee consistency through time and help in evaluating the spatial and temporal evolution of damage progression during seismic sequences, thus ultimately improving seismic risk assessment.