Japan Geoscience Union Meeting 2016

Presentation information


Symbol M (Multidisciplinary and Interdisciplinary) » M-TT Technology & Techniques

[M-TT27] New frontier of data analysis in geoscience: Data-driven approach

Sun. May 22, 2016 1:45 PM - 3:15 PM A04 (APA HOTEL&RESORT TOKYO BAY MAKUHARI)

Convener:*Tatsu Kuwatani(Japan Agency for Marine-Earth Science and Technology), Takeshi Komai(none), Hideaki Miyamoto(The University Museum, The University of Tokyo), Katsuaki Koike(Laboratory of Environmental Geosphere Engineering, Department of Urban Management, Graduate School of Engineering, Kyoto University), Takane Hori(R&D Center for Earthquake and Tsunami, Japan Agency for Marine-Earth Science and Technology), Hiromichi Nagao(Earthquake Research Institute, The University of Tokyo), Chair:Masaoki Uno(Graduate School of Environmental Studies, Tohoku University), Peng Hong(The University Museum, The University of Tokyo)

1:45 PM - 2:00 PM

[MTT27-01] Simultaneous estimation of melting degree and source composition of MORB: an application of data-driven analysis to Geochemistry

*Tatsu Kuwatani1, Kenji Nagata2, Shotaro Akaho3, Hikaru Iwamori1 (1.Japan Agency for Marine-Earth Science and Technology, 2.The University of Tokyo, 3.National Institute of Advanced Industrial Science and Technology)

Keywords:data-driven, Bayesian estimation, sparse modeling, Markov chain Monte Carlo method

Geochemical data sets, such as major, trace and isotopic compositions, preserve precious information about various physical and chemical processes. For example, bulk compositions of igneous rocks directly reflect melting of original rocks, magma mixing and rock-fluid interaction in the earth's interior. However, it has been still difficult to extract physical and chemical processes quantitatively due to many unknown factors and insufficient quality of data sets. Recently, many sophisticated data-driven methodologies have been proposed to extract useful information from high-dimensional data sets in information sciences. In this presentation, we will briefly overview data-driven analytical technologies and introduce an application to simultaneous estimation of melting degrees and a mantle source composition from MORB bulk compositions based on Bayesian estimation and Markov chain Monte Carlo (MCMC) optimization.