Spatial Modeling Techniques for Characterizing Geomaterials:Deterministic vs. Stochastic Modeling for Single-Variable and Multivariate Analyses
Katsuaki Koike
摘要：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 typeⅠ 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 stochastie simulation,and(3)a neural network method.Geostatistics proves to be a powerful tool for typeⅡ data.Three new approaches of geostatistics are presented with ease 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.
机标关键词：TechniquesModelingdirectional datamodelingeffective methodstypeneural networkscaling law
分类号：P5(地质学)
论文发表日期：2011-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
英文信息