Atefe Darzi

SUMMARY: The reliable forecasting of seismic sequences following a main shock has practical implications because effective post-event response is crucial in earthquake-stricken regions, aftershocks can progressively cause increased damage and compound economic losses. In the South Iceland Seismic Zone (SISZ), one of two large transform zones in Iceland where earthquake hazard is the highest, an intense seismic sequence took place during 17–24 June 2000, starting with a ${M}_{\rm{w}}$ 6.4 main shock on 17 June 2000, followed by another ${M}_{\rm{w}}$ 6.5 main shock four days later and on a different fault. Both earthquakes caused considerable damage and incurred heavy economic losses. They were immediately followed by intense aftershock activity on the causative faults and triggered earthquakes as far as 80 km away along the transform zone. To investigate the feasibility of forecasting the progression of such complex sequences, we calibrated a spatio-temporal epidemic-type aftershock sequence (ETAS) clustering model to the June 2000 seismic sequence in the framework of Bayesian statistics. Short-term seismicity forecasts were carried out for various forecasting intervals and compared with the observations, the first generated a few hours after the first main shock and followed by daily forecasts. The reliability of the early forecasts was seen to depend on the initial model parameters. By using an adaptive parameter inference approach where the posteriors from each preceding forecasting interval served as informative priors for the next, the fast convergence of the parametric values was ensured. As a result, the 16–84 percentile range of the forecasted number of events captured the actual number of observed events in all daily forecasts, and the model exhibited a strong spatial forecasting ability, even only a few hours after the main shock, and over all subsequent daily forecasts. We present the spatio-temporal ETAS parameters for the June 2000 sequence as ideal candidates of prior estimates for future operational earthquake forecasting of other Icelandic aftershock sequences. Past seismic sequences need to be analysed retrospectively to confirm the stability of the parameters of this study, effectively enable the application of the Bayesian ETAS model as an operational earthquake forecasting system for aftershocks in Iceland.

Presented herein is a new set of empirical models for prediction of horizontal peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped elastic response spectra in the 0.01–10 s vibration period range in Iran. The models are based on a carefully curated databank of Iranian strong ground motions composed of 1350 pairs of orthogonal horizontal acceleration time histories from 370 regional earthquakes with $4.5≤Mw≤7.4$ and source‐to‐site distances up to 200 km. The adaptive wavelet denoising approach used for waveform processing in this study allows extending the range of oscillator periods for which predictions can be made. We investigated the possible regional dependency of strong ground motions from three different regions (Alborz–Azerbaijan–Kopeh Dagh, Zagros, and central Iran–Makran) of the Iranian plateau through analysis of variance. Our results show that data from these regions can be combined into a single database because only a few magnitude–distance intervals exhibit significant regional differences. Local site effects are taken into account using a site classification scheme based on $VS30$⁠. The predictive models also include style‐of‐faulting (SoF) terms. Because of the paucity of data for normal‐faulting events, the applicability of the proposed attenuation models is limited to unspecified, strike‐slip, and reverse SoFs. We propose four models that employ point‐source (hypocentral and epicentral) and extended‐source (rupture and Joyner–Boore) distance metrics to enhance the flexibility of the prediction tool within the context of seismic hazard assessment studies. The results of between‐event and within‐event residuals show that the proposed new models are unbiased. We present a detailed comparison of our results with a selection of local, regional, and global predictive models developed by other authors.