Uncertainty assessment of nonstationary data

点击翻译按钮获取中文摘要

Geostatistics uses statistical techniques to assess the resources or reserves for a geological deposit. Lately, these techniques have become increasingly popular for understanding the uncertainty in sparsely sampled mineral deposits, petroleum reservoirs, CO2 sequestration sites, contaminated areas and many other spatially distributed phenomenon that follow geological deposits. This work relates to the development of techniques to better model deposits that exhibit complex nonstationary geological features. Recent research has explored modeling methodologies that consider nonstationary features in the context of mining or petroleum studies; typical features include channels, veins, curvilinear strata, anticlines, roll front deposits, etc. The proposed work is to expand these nonstationary techniques to include environmental data, specifically spatial-temporal data. There are three aspects of research planned (1) generalize nonstationary spatial modeling techniques to include cases of nonstationary spatial-temporal data and (2) develop techniques and tools to infer the complex input statistics required for nonstationary techniques and (3) condition object based models to hard and soft data to generate conditioned nonstationary models.