On the Use of Instrumental and Macroseismic Data to Evaluate Ground-Motion Models: The 2019 Mw 6.4 Durres, Albania, Earthquake Sequence

Abstract

In this study, we analyze the existing ground-motion models (GMMs) applicable in Albania for horizontal peak ground acceleration (PGA) and spectral acceleration (SA) using instrumental ground motions, and also incorporate online citizen responses from “Did you feel it?” (DYFI) to compensate for the sparse distribution of strong-motion stations and provide better constraints for near-fault motions. Our evaluation focuses primarily on the damaging 26 November 2019 Mw 6.4 Durres earthquake, incorporating 1360 DYFI online citizen responses collected after the Durres mainshock event, along with two significant September foreshocks and two large November aftershocks with a moment magnitude Mw>5.0. In general, the DYFI intensities exhibit higher values than instrumentation data, and we find that SA at 0.3 s better represents the observed macroseismic intensities for all events. In the meantime, the reversible relationships between macroseismic intensities and PGA/SA, as established by Oliveti et al. (2022) based on a dataset from the European region (Italy), show a better fit for the converted DYFI observations when compared to instrumental data, in contrast to the fit of the converted DYFI observations by Worden et al. (2012). This underscores the importance of regional characterization when considering the datasets from online citizen responses. The extensive DYFI intensities set, particularly in near-fault regions, significantly improves the evaluation of GMMs due to the sparse distribution of instrumentation data. Moreover, we account for data variance, and applied the log-likelihood approaches to select and rank a candidate set of GMMs. In addition to recommending a set of GMMs suitable for the Albania region, our study highlights the valuable applications of using online citizen responses like DYFI for ground-motion estimations, which are crucial in regions with limited instrumental station coverage. These online citizen response datasets contribute to better constraining the selection of GMMs, although careful consideration is necessary when relating intensity to ground motion for regional characterization. Our study makes a significant contribution to GMM selection and provides a valuable reference for the logic tree structure in subsequent seismic hazard assessments on both national and regional scales.