Calibration of a Bayesian spatio-temporal ETAS model to the June 2000 South Iceland seismic sequence

• Main Authors: Darzi, Atefe, Halldorsson, Benedikt, Hrafnkelsson, Birgir, Ebrahimian, Hossein, Jalayer, Fatemeh, Vogfjoro, Kristin S
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley-Blackwell 2023
• Subjects: and prediction; Bayesian inference; CATALOG; CLUSTERING MODEL; DEFORMATION; Earthquake interaction; EARTHQUAKE RUPTURE; forecasting; FORECASTS; Geochemistry & Geophysics; HAZARD; NORTH ICELAND; Operational earthquake forecasting; Physical Sciences; Probabilistic forecasting; Science & Technology; SPACE; Statistical methods; TIME; ZONE; Iceland
• Online Access: https://discovery.ucl.ac.uk/id/eprint/10160867/1/ggac387.pdf; https://discovery.ucl.ac.uk/id/eprint/10160867/

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 ...