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Numerical data of probabilistic 3D lithological map of Japanese crust
- Source :
- Data in Brief, Data in Brief, Vol 26, Iss, Pp-(2019)
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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 structure, Crustal lithology, Rock composition, Geoneutrinos
- Subjects :
- Discretization
Lithology
Geoneutrino
Crustal structure
lcsh:Computer applications to medicine. Medical informatics
Bayesian inference
Bayesian
Mantle (geology)
03 medical and health sciences
0302 clinical medicine
Geoneutrinos
lcsh:Science (General)
030304 developmental biology
0303 health sciences
Multidisciplinary
Probabilistic logic
Crust
Geophysics
Crustal lithology
Seismic tomography
Rock composition
lcsh:R858-859.7
Earth and Planetary Science
030217 neurology & neurosurgery
Geology
lcsh:Q1-390
Subjects
Details
- ISSN :
- 23523409
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- Data in Brief
- Accession number :
- edsair.doi.dedup.....ae263ddba286d020e1c639f20c79f4dd