[S-CG51] Driving Solid Earth Science through Machine Learning

Mon. May 30, 2022 11:00 AM - 1:00 PM Online Poster Zoom Room (27) (Ch.27)

convener:Hisahiko Kubo(National Research Institute for Earth Science and Disaster Resilience), convener:Yuki Kodera(Meteorological Research Institute, Japan Meteorological Agency), Makoto Naoi(Kyoto University), convener:Keisuke Yano(The Institute of Statistical Mathematics), Chairperson:Hisahiko Kubo(National Research Institute for Earth Science and Disaster Resilience), Yuki Kodera(Meteorological Research Institute, Japan Meteorological Agency), Keisuke Yano(The Institute of Statistical Mathematics)

Machine learning (ML) has brought innovations and remarkable results in various science fields including solid earth science. This session provides an opportunity to inspire each other for future developments by bringing studies of ML applications in various fields including solid earth science. We especially welcome studies that have led to the discovery of phenomena and theories in solid earth science. We also look forward to studies on the black box problem of AI and efforts toward explainable AI.

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11:00 AM - 1:00 PM

[SCG51-P01] Machine learning approach to maximum magnitude estimates for seismic hazard

*Hiroe Miyake¹ (1.Earthquake Research Institute, University of Tokyo)

11:00 AM - 1:00 PM

[SCG51-P02] Application of deep learning picker to automatically determined earthquakes around the 2011 Tohoku earthquake

*Koji Tamaribuchi¹ (1.Meteorological Research Institute)

11:00 AM - 1:00 PM

[SCG51-P03] Hypocenter Determination Using Deep Learning Technique Based on Arrival Time Probability Trace

*Makoto Naoi¹ (1.Kyoto University)

11:00 AM - 1:00 PM

[SCG51-P04] Tsunami inundation prediction using seafloor pressure data by power-law regression and multilayer perceptron

*Masato Kamiya¹, Toshitaka Baba² (1.Graduate School of Sciences and Technology for Innovation, Tokushima University, 2.Graduate School of Technology, Industrial and Social Sciences, Tokushima University)

11:00 AM - 1:00 PM

[SCG51-P05] Seismic wave detection at Hakone volcano using machine learning: Toward improving the detection accuracy when there are multiple seismic waves in the same time window

*Ahyi KIM¹, Yuji Nakamura¹, Hiroki Uematsu¹, Yohei Yukutake², Yuki Abe³ (1.Yokohama City University, 2.Earthquake Research Institute, the University of Tokyo, 3.Hot Springs Research Institute, Kanagawa)

11:00 AM - 1:00 PM

[SCG51-P06] Temperature estimation at depth by physics-informed neural network: accuracy evaluation with 2D numerical data

*Kazuya Ishitsuka¹ (1.Kyoto University)

11:00 AM - 1:00 PM

[SCG51-P07] Development of a method for detecting tectonic tremor using deep learning

*Amane Sugii¹, Yoshihiro Hiramatsu¹, Takahiko Uchide², Kazutoshi Imanishi² (1.Kanazawa University, 2.National Institute of Advanced Industrial Science and Technology)

Session information

[S-CG51] Driving Solid Earth Science through Machine Learning

[SCG51-P01] Machine learning approach to maximum magnitude estimates for seismic hazard

[SCG51-P02] Application of deep learning picker to automatically determined earthquakes around the 2011 Tohoku earthquake

[SCG51-P03] Hypocenter Determination Using Deep Learning Technique Based on Arrival Time Probability Trace

[SCG51-P04] Tsunami inundation prediction using seafloor pressure data by power-law regression and multilayer perceptron

[SCG51-P05] Seismic wave detection at Hakone volcano using machine learning: Toward improving the detection accuracy when there are multiple seismic waves in the same time window

[SCG51-P06] Temperature estimation at depth by physics-informed neural network: accuracy evaluation with 2D numerical data

[SCG51-P07] Development of a method for detecting tectonic tremor using deep learning

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