[S-CG62] Potentiality of Machine Learning in Solid Earth Sciences

Sun. May 26, 2019 3:30 PM - 5:00 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Takahiko Uchide(Research Institute of Earthquake and Volcano Geology, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST)), Hirokuni Oda(Institute of Geology and Geoinformation, Geological Survey of Japan, AIST)

The recent development of the machine learning techniques including the deep learning is leading innovations in various fields. The applications of these techniques to solid earth sciences are also expected to develop new frontiers by, for example, the classification, the pattern recognition, and the regression of data. On the other hand, there are concerns on the human-interpretability of the deep learning.
This session will provide an opportunity to share the application studies to various fields in solid earth science and inspire each other. We also look forward to studies addressing the black box issue of the deep learning.

[SCG62-P01] Earthquake Magnitude Prediction with Seismic Nucleation Phase based on Machine Learning

*Kangming Wu¹, Hiroe Miyake² (1.GSII, The University of Tokyo, 2.III, The University of Tokyo)

[SCG62-P02] Automatic signal classification for continuous seismic waveform records based on an unsupervised learning algorithm: Application to artificial noises and low-frequency tremors

*Yuki Kodera¹, Shin'ichi Sakai², Takahito Nishimiya¹ (1.Meteorological Research Institute, Japan Meteorological Agency, 2.Earthquake Research Institute, the University of Tokyo)

[SCG62-P03] Detection of Gas Bubble signals recorded at the OBS Stations by Machine-Learning

*emmy TY CHANG¹ (1.Institute of Oceanography, National Taiwan University)

[SCG62-P04] Automatic Phase Picking of Seismograms by Deep Neural Network

*Takahiko Uchide¹ (1.Research Institute of Earthquake and Volcano Geology, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST))

[SCG62-P05] Seismic event detection in time-frequency domain with CNN and phase picking with HED

*Liping Fan¹ (1.Institute of Geophysics, China Earthquake Administration)

[SCG62-P06] Broadband ground-motion synthesis using embeddig machine learning

*Tomohisa Okazaki¹, Hirotaka Hachiya¹, Naonori Ueda¹, Asako Iwaki², Takahiro Maeda², Hiroyuki Fujiwara² (1.Center for Advanced Intelligence Project, RIKEN, 2.National Research Institute for Earth Science and Disaster Resilience)

[SCG62-P07] LSTM and CNN Applications to Forecast Earthquake Magnitude Probability Distribution

*Qi Liu¹, Hiroe Miyake², Hiroshi Tsuruoka³ (1.GSII, The University of Tokyo, 2.III, The University of Tokyo, 3.ERI, The University of Tokyo)

[SCG62-P08] FORCsensei: A machine learning framework to estimate optimized first-order reversal curve distributions

David Heslop^1,2, Andrew Roberts^1,2, *Hirokuni Oda², Xiang Zhao^1,2, Richard Harrison³, Adrian Muxworthy⁴, Pengxiang Hu^1,2, Tetsuro Sato² (1.Australian National University, 2.Institute of Geology and Geoinformation, Geological Survey of Japan, AIST, 3.University of Cambridge, 4.Imperial College London)

Session information