Cho In Ho
CCEE Department, Iowa State University, Ames, IA, 50011, USA.
Sci Rep. 2023 Sep 25;13(1):16009. doi: 10.1038/s41598-023-43181-z.
Predicting individual large earthquakes (EQs)' locations, magnitudes, and timing remains unreachable. The author's prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs ([Formula: see text] 6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.
预测单个大地震的位置、震级和时间仍然无法实现。作者之前的研究表明，单个大地震具有从多层数据变换中获得的独特特征。通过时空卷积，长达数十年的地震目录数据被转换为伪物理量（例如能量、功率、涡度和拉普拉斯算子），这些伪物理量通过高斯曲率转变为类似表面的信息。利用这些新特征，一种规则学习的机器学习方法揭示了有前景的预测规则。本文建议通过傅里叶变换（FT）进行进一步的数据变换。结果表明，基于傅里叶变换的新特征有助于锐化预测规则。对美国西部过去40年里发生的大地震（震级≥6.5）进行的可行性测试显示出了前景，为单个大地震的数据驱动预测提供了线索。机器学习方法、傅里叶变换和高斯曲率之间的结合可能有助于解开长期存在的地震成因之谜。
