Numerical data of probabilistic 3D lithological map of Japanese crust

Nozomu Takeuchi; Kenta Ueki; Tsuyoshi Iizuka; Jun Nagao; Akiko Tanaka; Sanshiro Enomoto; Yutaka Shirahata; Hiroko Watanabe; Makoto Yamano; Hiroyuki Tanaka

doi:10.1016/j.dib.2019.104497

Numerical data of probabilistic 3D lithological map of Japanese crust

Data Brief. 2019 Sep 11:26:104497. doi: 10.1016/j.dib.2019.104497. eCollection 2019 Oct.

Authors

Nozomu Takeuchi¹, Kenta Ueki^{1

2}, Tsuyoshi Iizuka³, Jun Nagao³, Akiko Tanaka⁴, Sanshiro Enomoto^{5

6}, Yutaka Shirahata⁷, Hiroko Watanabe⁷, Makoto Yamano¹, Hiroyuki Tanaka¹

Affiliations

¹ Earthquake Research Institute, University of Tokyo, Bunkyo-ku, Tokyo, 113-0032, Japan.
² Research Institute for Marine Geodynamics, Japan Agency for Marine-Earth Science and Technology, Yokosuka, 237-0061, Japan.
³ Department of Earth and Planetary Science, University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
⁴ Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology, Tsukuba, 305-8567, Japan.
⁵ Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo, Kashiwa, 277-8583, Japan.
⁶ Department of Physics, University of Washington, Seattle, WA 98195, United States.
⁷ Research Center for Neutrino Science, Tohoku University, Sendai, 980-8578, Japan.

Abstract

The 3D lithological distribution model presented in this data article is related to "Stochastic modeling of 3-D compositional distribution in the crust with Bayesian inference and application to geoneutrino observation in Japan" by Takeuchi et al. (2019) [1]. Our target region is set to the crust and uppermost mantle beneath Japanese main islands and their vicinity. We discretized the target region into 79,968 grid points. We defined 31 rock types; 29 major crustal rock types, plus sediment and mantle. Our lithology model represents a probabilistic distribution map inferred from a seismic tomography model and allows quantitative studies with error estimations, making it fundamentally different from previous models. To enable such quantitative applications, we provide explicit numerical data for the probabilities of the 31 rock types for each grid point. We also provide explicit values of the bulk proportion lithology model at various depths and for the bulk whole crust. Further, a figure of synthetic gravity data is presented to correct a minor error in the related paper [1].

Keywords: Bayesian; Crustal lithology; Crustal structure; Geoneutrinos; Rock composition.