Koike Katsuaki

doi:10.3799/dqkx.2011.022

Volume 36 Issue 2

Mar. 2011

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Koike Katsuaki, 2011. Spatial Modeling Techniques for Characterizing Geomaterials: Deterministic vs.Stochastic Modeling for Single-Variable and Multivariate Analyses. Earth Science, 36(2): 209-226. doi: 10.3799/dqkx.2011.022

Citation:

Koike Katsuaki, 2011. Spatial Modeling Techniques for Characterizing Geomaterials: Deterministic vs.Stochastic Modeling for Single-Variable and Multivariate Analyses. Earth Science, 36(2): 209-226. doi: 10.3799/dqkx.2011.022

Citation:

Koike Katsuaki, 2011. Spatial Modeling Techniques for Characterizing Geomaterials: Deterministic vs.Stochastic Modeling for Single-Variable and Multivariate Analyses. Earth Science, 36(2): 209-226. doi: 10.3799/dqkx.2011.022

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Spatial Modeling Techniques for Characterizing Geomaterials: Deterministic vs.Stochastic Modeling for Single-Variable and Multivariate Analyses

doi: 10.3799/dqkx.2011.022

Koike Katsuaki

New Frontier Sciences, Graduate School of Science and Technology, Kumamoto University, Japan

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Author Bio:
Katsuaki Koike, E-mail: koike@gpo.kumamoto-u.ac.jp
Received Date: 2010-07-15
Publish Date: 2011-03-01

Abstract

Abstract

Sample data in the Earth and environmental sciences are limited in quantity and sampling location and therefore, sophisticated spatial modeling techniques are indispensable for accurate imaging of complicated structures and properties of geomaterials. This paper presents several effective methods that are grouped into two categories depending on the nature of regionalized data used. Type I data originate from plural populations and type II data satisfy the prerequisite of stationarity and have distinct spatial correlations. For the type I data, three methods are shown to be effective and demonstrated to produce plausible results: (1) a spline-based method, (2) a combination of a spline-based method with a stochastic simulation, and (3) a neural network method. Geostatistics proves to be a powerful tool for type II data. Three new approaches of geostatistics are presented with case studies: an application to directional data such as fracture, multi-scale modeling that incorporates a scaling law, and space-time joint analysis for multivariate data. Methods for improving the contribution of such spatial modeling to Earth and environmental sciences are also discussed and future important problems to be solved are summarized.
- geostatistics,
- spline,
- neural network,
- geological modeling,
- mineral resources,
- fracture distribution,
- water environment

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References(65)

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