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Conditional Probabilities for Large Events Estimated by Small Earthquake Rate

机译：用小地震率估计的大事件的条件概率

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We examined forecasting 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 subareas 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 point 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 receiver operating characteristic (ROC) curves of the probability maps demonstrate that the probability maps are not random forecasts, but also suggest 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 lead us to conclude that the quiescence model has better forecast potential than the activation model.
机译：我们检查了预测的静态和激活模型，以获得在特定时期内台湾不同规模发生大地震的条件概率。静态和激活模型的基本思想是使用震级大于完整性震级的地震来计算大地震的预期特性。我们使用中央气象局目录中的40年数据，计算了整个台湾地区和台湾三个分区的概率时间序列（台湾西部，东部和东北地区）。在台湾东部和东北地区的概率时间序列中，通常在群集事件中产生高概率值，例如前震事件和都在短时间内发生的事件。除了时间序列，我们还通过计算大地震发生前每个网格点的条件概率来生成概率图。概率图显示，大地震发生在震中附近有很高的概率值。概率图的接收器运行特性（ROC）曲线表明，概率图不是随机预测，但也表明降低预测的大地震的幅度可能不会改善预测方法本身。从概率时间序列和概率图都可以看出，从静态模型获得的概率在大地震之前增加，而从激活模型获得的概率随着大地震发生而增加。结果使我们得出结论，与激活模型相比，静态模型具有更好的预测潜力。

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