Earthquake Nowcasting with Deep Learning
Earthquake Nowcasting with Deep Learning
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DOI：
10.3390/geohazards3020011
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Geoffrey Fox;John Rundle;A. Donnellan;Bo Feng
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文献类型：
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作者：
Geoffrey Fox;John Rundle;A. Donnellan;Bo Feng
我们回顾了以前的地震的方法，并基于深度学习引入了基于复发性神经网络和变形金刚的三种不同模型。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，将Nowcast和观测的离发与每个空间区域的差异进行了比较。
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.