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Dauphin, Y.N., Fan, A., Auli, M. and Grangier, D. (2017). Language Modeling with Gated Convolutional Networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, 6-11 August 2017, 933-941.

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• TITLE: Investigation of Relation between Solar Activity and Earthquakes with Deep Learning Method

  AUTHORS: Leilei Li, Hong Gu, Ryosuke Kikuyama, Ryuei Nishii, Pan Qin

  KEYWORDS: Deep Learning, Earthquakes, Prediction, Solar Activities, Temporal Convolution Network

  JOURNAL NAME: International Journal of Geosciences, Vol.12 No.8, August 25, 2021

  ABSTRACT: Solar activity (SA) has been hypothesized to be a trigger of earthquakes, although it is not as intuitively associated as other potential triggers such as tidal stress, rainfall, and the building of artificial water reservoirs. Here, we investigate the relation between SA and global earthquake numbers (GEN) by using a deep learning method to test the hypothesis. We use the daily data of GEN and SA (1996/01/01-2019/12/31) to construct a temporal convolution network (TCN). From the computational results, we confirm that the TCN captures the relation between SA and earthquakes with magnitudes from 4.0 to 4.9. We also find that the TCN achieves better fitting and prediction performance compared with previous work.