Bayesian analysis on earthquake magnitude related to an active fault in Taiwan

• Bayesian analysis on earthquake magnitude related to a high-risk active fault in Taiwan.
• Using prior data to compensate limited observation.
• New Bayesian algorithms are given in detail.

It is understood that sample size could be an issue in earthquake statistical studies, causing the best estimate being too deterministic or less representative derived from limited statistics from observation. Like many Bayesian analyses and estimates, this study shows another novel application of the Bayesian approach to earthquake engineering, using prior data to help compensate the limited observation for the target problem to estimate the magnitude of the recurring Meishan earthquake in central Taiwan. With the Bayesian algorithms developed, the Bayesian analysis suggests that the next major event induced by the Meishan fault in central Taiwan should be in Mw 6.44±0.33, based on one magnitude observation of Mw 6.4 from the last event, along with the prior data including fault length of 14 km, rupture width of 15 km, rupture area of 216 km2, average displacement of 0.7 m, slip rate of 6 mm/yr, and five earthquake empirical models.