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]
Masato Okada,et al.
Markov random field modeling for mapping geofluid distributions from seismic velocity structures
,
2014,
Earth, Planets and Space.
[2]
M. Okada,et al.
Geodetic inversion for spatial distribution of slip under smoothness, discontinuity, and sparsity constraints
,
2016,
Earth, Planets and Space.
[3]
Masato Okada,et al.
Machine-learning techniques for geochemical discrimination of 2011 Tohoku tsunami deposits
,
2014,
Scientific Reports.
[4]
Yoshinori Nakanishi-Ohno,et al.
Three levels of data-driven science
,
2016
.