论文标题
深度学习的地震
Earthquake Nowcasting with Deep Learning
论文作者
论文摘要
我们回顾了以前的地震方法，并使用基于复发性神经网络和变形金刚的三种不同模型引入了基于深度学习的新方法。我们讨论了可观察到的不同选择，并为1950 - 2020年南加州地区提供了有希望的初始结果。地震活性被预测为0.1度空间垃圾箱的函数，时间段从两周到四年不等。总体质量是通过Nash Sutcliffe的效率来衡量的，该效率比较了每个空间区域的Nowcast和观察与随时间的变化的偏差。该软件可作为开源，以及来自USGS的预处理数据。
We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950-2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe Efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open-source together with the preprocessed data from the USGS.
