Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern California
Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern California
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DOI：
10.1109/escience51609.2021.00020
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Bo Feng;G. Fox
中科院分区：
文献类型：
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作者：
Bo Feng;G. Fox
过去几十年来，地球科学和地震学利用最先进的技术和设备监测全球地震事件。由于数据量巨大，现代GPU驱动的深度学习为分析数据和发现模式提供了一种很有前途的方法。近年来，有很多成功的深度学习模型用于拾取地震波。然而，对可能造成灾害的极端地震的预测在历史上仍然是一个欠发达的课题。时空动力学挖掘与预测的相关研究已经取得了一些成功的预测结果，这是许多科学研究领域的一个重要课题。它们的大多数研究都有许多使用深度神经网络的成功应用。在地质学和地球科学研究中，地震预测是世界上最具挑战性的问题之一，尖端的深度学习技术可能有助于发现一些有价值的模式。在这个项目中，我们提出了一种深度学习建模方法，即EQPred，通过在区域粗粒度空间网格中发现随时间变化的视觉动态来挖掘从数据到临近极端地震的时空模式。在这种建模方法中，我们使用具有地球科学和地震学领域知识的合成深度学习神经网络，利用卷积长短期记忆神经网络预测地震模式。我们的实验表明，在南加州地震的位置预测和震级预测之间有很强的相关性。消融研究和可视化验证了所提出的建模方法的有效性。
Geoscience and seismology have utilized the most advanced technologies and equipment to monitor seismic events globally from the past few decades. With the enormous amount of data, modern GPU-powered deep learning presents a promising approach to analyze data and discover patterns. In recent years, there are plenty of successful deep learning models for picking seismic waves. However, forecasting extreme earthquakes, which can cause disasters, is still an underdeveloped topic in history. Relevant research in spatiotemporal dynamics mining and forecasting has revealed some successful predictions, a crucial topic in many scientific research fields. Most studies of them have many successful applications of using deep neural networks. In Geology and Earth science studies, earthquake prediction is one of the world’s most challenging problems, about which cutting-edge deep learning technologies may help discover some valuable patterns. In this project, we propose a deep learning modeling approach, namely EQPred, to mine spatiotemporal patterns from data to nowcast extreme earthquakes by discovering visual dynamics in regional coarse-grained spatial grids over time. In this modeling approach, we use synthetic deep learning neural networks with domain knowledge in geoscience and seismology to exploit earthquake patterns for prediction using convolutional long short-term memory neural networks. Our experiments show a strong correlation between location prediction and magnitude prediction for earthquakes in Southern California. Ablation studies and visualization validate the effectiveness of the proposed modeling method.