Forecast Probabilities for Large Events Estimated by Earthquake Deficits

Abstract:

We examined quiescence and activation models to obtain the conditional probability that a large earthquake will occur in a specific time period on different scales in Taiwan. The basic idea of the quiescence and activation models is to use earthquakes that have magnitudes larger than the completeness magnitude to compute the expected properties of large earthquakes. We calculated the probability time-series for the whole Taiwan region and for three sub-areas of Taiwan – the western, eastern, and northeastern Taiwan regions – using 40 years of data from the Central Weather Bureau catalog. In the probability time-series for the eastern and northeastern Taiwan regions, a high probability value is usually yielded in cluster events such as events with foreshocks and events that all occur in a short time period. In addition to the time-series, we produced probability maps by calculating the conditional probability for every grid at the time just before a large earthquake. The probability maps show that high probability values are yielded around the epicenter before a large earthquake. The ROC curves of the probability maps demonstrate that the probability maps are not random forecasts, but it also suggests that lowering the magnitude of a forecasted large earthquake may not improve the forecast method itself. From both the probability time-series and probability maps, it can be observed that the probability obtained from the quiescence model increases before a large earthquake and the probability obtained from the activation model increases as the large earthquakes occur. The results led us to conclude that the quiescence model has better forecast skill than the activation model.