Anyfadi Eleni-Apostolia, Gentili Stefania, Brondi Piero, Vallianatos Filippos
Section of Geophysics-Geothermics, Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, 15784 Athens, Greece.
Institute of Physics of Earth's Interior and Geohazards, UNESCO Chair on Solid Earth Physics and Geohazards Risk Reduction, Hellenic Mediterranean University Research & Innovation Center, 73133 Chania, Greece.
Entropy (Basel). 2023 May 13;25(5):797. doi: 10.3390/e25050797.
Aftershocks of earthquakes can destroy many urban infrastructures and exacerbate the damage already inflicted upon weak structures. Therefore, it is important to have a method to forecast the probability of occurrence of stronger earthquakes in order to mitigate their effects. In this work, we applied the NESTORE machine learning approach to Greek seismicity from 1995 to 2022 to forecast the probability of a strong aftershock. Depending on the magnitude difference between the mainshock and the strongest aftershock, NESTORE classifies clusters into two types, Type A and Type B. Type A clusters are the most dangerous clusters, characterized by a smaller difference. The algorithm requires region-dependent training as input and evaluates performance on an independent test set. In our tests, we obtained the best results 6 h after the mainshock, as we correctly forecasted 92% of clusters corresponding to 100% of Type A clusters and more than 90% of Type B clusters. These results were also obtained thanks to an accurate analysis of cluster detection in a large part of Greece. The successful overall results show that the algorithm can be applied in this area. The approach is particularly attractive for seismic risk mitigation due to the short time required for forecasting.
地震余震会破坏许多城市基础设施，并加剧对薄弱建筑已造成的损害。因此，拥有一种预测更强地震发生概率的方法以减轻其影响非常重要。在这项工作中，我们将NESTORE机器学习方法应用于1995年至2022年的希腊地震活动，以预测强烈余震的发生概率。根据主震和最强余震之间的震级差异，NESTORE将震群分为两种类型，A类和B类。A类震群是最危险的震群，其特征是差异较小。该算法需要以区域依赖训练作为输入，并在独立测试集上评估性能。在我们的测试中，主震发生6小时后我们获得了最佳结果，因为我们正确预测了92%的震群，其中100%为A类震群，超过90%为B类震群。这些结果的取得还得益于对希腊大部分地区震群检测的准确分析。总体成功结果表明该算法可应用于这一领域。由于预测所需时间短，该方法对于减轻地震风险特别有吸引力。
