论文标题
变压器地震警报模型：一种新的多功能地震预警方法
The transformer earthquake alerting model: A new versatile approach to earthquake early warning
论文作者
论文摘要
地震是对人类，建筑物和基础设施的主要危害。预警方法旨在提前通知即将出现的强烈摇动以实现预防性行动并减轻地震风险。它们的实用性取决于准确性，真实，错过和虚假警报之间的关系以及及时性，警告与强烈摇晃的到来之间的时间。由于简化的建模或短时间警告时间，当前的方法遭受了明显的不良不确定性。在这里，我们提出了一种新颖的预警方法，这是基于深度学习的变压器地震警报模型（团队），以减轻这些限制。团队分析了在任意位置进行任意数量的站点的原始运动波形，使其很容易适应不断变化的地震网络和警告目标。我们评估在两个具有高地震危害，日本和意大利的高地震危险区域的团队，它们的地震性是互补的。在两个数据集中，团队的表现都大大优于现有的预警方法，并提供准确，及时的警告。使用域的适应，团队甚至为培训数据中的任何事件提供了可靠的警报，作为大型事件的记录在许多地区很少见的属性。
Earthquakes are major hazards to humans, buildings and infrastructure. Early warning methods aim to provide advance notice of incoming strong shaking to enable preventive action and mitigate seismic risk. Their usefulness depends on accuracy, the relation between true, missed and false alerts, and timeliness, the time between a warning and the arrival of strong shaking. Current approaches suffer from apparent aleatoric uncertainties due to simplified modelling or short warning times. Here we propose a novel early warning method, the deep-learning based transformer earthquake alerting model (TEAM), to mitigate these limitations. TEAM analyzes raw, strong motion waveforms of an arbitrary number of stations at arbitrary locations in real-time, making it easily adaptable to changing seismic networks and warning targets. We evaluate TEAM on two regions with high seismic hazard, Japan and Italy, that are complementary in their seismicity. On both datasets TEAM outperforms existing early warning methods considerably, offering accurate and timely warnings. Using domain adaptation, TEAM even provides reliable alerts for events larger than any in the training data, a property of highest importance as records from very large events are rare in many regions.
