JpGU Meeting 2021

Session Outline

Solid Earth Sciences (S)
Session Sub-category		Complex & General (CG)
Session ID		S-CG52
Title		Driving Solid Earth Science through Machine Learning
Short Title		Machine Learning in Solid Earth Sciences
Date & Time	Oral session	JUNE 3 (THU) AM1	Channel	18
	Poster session	JUNE 3 (THU) PM3
Main Convener	Name	Hisahiko Kubo
	Affiliation	National Research Institute for Earth Science and Disaster Resilience
Co-Convener 1	Name	Yuki Kodera
	Affiliation	Meteorological Research Institute, Japan Meteorological Agency
Co-Convener 2	Name	Makoto Naoi
	Affiliation	Kyoto University
Co-Convener 3	Name	Keisuke Yano
	Affiliation	The Institute of Statistical Mathematics
Session Language		J
Scope		The development of machine learning techniques, such as deep learning, has led to innovations in various fields and is gaining ground in the solid earth sciences. This session provides an opportunity to inspire each other for future developments by bringing studies of machine learning applications in various fields including solid earth science. We especially welcome researches 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 deep learning and efforts toward explainable AI.
Presentation Format		Oral and Poster presentation
Collaboration	Joint with	-
	Co-sponsored with	-