Earthquake Magnitude Estimation through Mixed-Effects Ground-Motion Modeling of Early P-Wave Arrivals

Abstract

A regional earthquake early warning system (EEWS) warrants potential predictive models to accurately extract earthquake parameters like magnitude and intensity from the first few seconds of a P-wave arrival. In this study, a maiden predictive model depicting the relationship between peak displacement amplitude (Pd) and magnitude (ML) is proposed for the western Himalayan region through a mixed-effects regression and compared with those from similar tectonic regimes. This model for EEWS is derived from the vertical-component waveforms with a high signal-to-noise ratio, using three different time-window lengths (Td) of 1, 2, and 3 s, just after the P onset. Waveforms from 83 earthquakes in the magnitude (ML) range of 3 and 5.5 registered at 27 strong motion seismic stations are used for this purpose. The hypocentral distance range varies between 5 and 264 km. A comparative analysis between the models obtained through linear and linear mixed-effects (lme) regression reveals that the latter is robust. It is observed that the intra-event uncertainties are significantly reduced after site corrections and contribute more toward the total variabilities, compared to the inter-event uncertainties. Based on the results from this study, it is emphasized that the local site effects should be incorporated while developing the predictive models for EEWS. Importantly, the displacement magnitude (Mpd) derived from Pd values, accurately matches with ML, even for the data not used to derive the model, lending credence to the final model. A scaling relation between the peak ground velocities (PGV) and Pd values is also established to evaluate the seismic hazard levels. Advocating that the adapted models should be calibrated for a targeted region, the dissimilarities among different models and the implications from epistemic uncertainties are also discussed in the present study.