JpGU-AGU Joint Meeting 2017

Presentation information

[EJ] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI29] [EJ] Data-driven analysis, modeling and prediction in geosciences

Sat. May 20, 2017 10:45 AM - 12:15 PM A01 (Tokyo Bay Makuhari Hall)

convener:Tatsu Kuwatani(Japan Agency for Marine-Earth Science and Technology), Dmitri Kondrashov(University of California, Los Angeles), Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Sergey Kravtsov(University of Wisconsin Milwaukee), Chairperson:Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Chairperson:Sergey Kravtsov(University of Wisconsin Milwaukee)

10:45 AM - 11:00 AM

[MGI29-07] Data-driven science for solving problems in geosciences

★Invited papers

*Akaho Shotaro1 (1.The National Institute of Advanced Industrial Science and Technology)

Keywords:data-driven science, sparse-modeling, machine learning

Recent technological development of measurement and observation enables us to obtain a large amount of high-dimensional data. Effective use of high-dimensional data requires a robust framework to make the tight connection of information science to the original purpose of data analysis derived from various scientific disciplines [1]. Since 2013, we have launched a big scientific project entitled as “Initiative for high-dimensional data-driven science through deepening sparse modelling (FY2013-FY2017)” funded by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan. The aim of this project is to establish a novel framework of data analysis for natural sciences, namely, data-driven science. Its target fields are very wide including geosciences, astronomy, biology, and medical and brain sciences. In this presentation, we introduce the concept of data-driven science and some applications to geosciences [e.g. 2-5].

[1] Igarashi Y, Nagata K, Kuwatani T, Omori T, Nakanishi-Ohno Y, Okada M (2016) Three levels of data-driven science. J Phys Confere Ser 699: 012001-1 - 012001-13.
[2] Kuwatani T, Nagata K, Okada M, Toriumi M (2014) Markov random field modeling for mapping geofluid distributions from seismic velocity structures. Earth Planet Space 66: 5-1 - 5-9.
[3] Kuwatani T, Nagata K, Okada M, Watanabe T, Ogawa Y, Komai T, Tsuchiya N (2014) Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits. Sci Rep 4: 7077-1—7077-6.
[4] Nakata R, Kuwatani T, Okada M, Hori T (2016) Geodetic inversion for spatial distribution of slip under smoothness, discontinuity, and sparsity constraints. Earth Planet Space 68, 20-1 – 20-10.
[5] Araki T, Ochi T, Matsumoto N, Akaho S (2015) Robust estimation of spatio-temporal distribution of slow slip event by switching model, Spatial Statistics.