Application of Indicator Geostatistics for Probabilistic Uncertainty and Risk Analyses of Geochemical Data

Abstract

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest–Ordinary Kriging Model

“…Many studies have been conducted in geographic information system (GIS) environments to create predictive maps by applying geostatistics, and many of these studies have employed Ordinary Kriging (OK) [5][6][7]. Crossvalidation is frequently used to verify Kriging-based estimated values [8][9][10]. Additionally, the Kriging application process offers numerous options and parameters that can affect the prediction results.…”

Geostatistical Simulation of Compositional Data Using Multiple Data Transformations

“…있고, 불확실성 모델링이 가능한 장점으로 인해 지구 과학의 많은 분야에서 활용되어 왔다 (Kyriakidis et al, 2004;Oh, 2005;Goovaerts, 2010;Park, 2010;Park, 2011). 특히 지구통계학적 시뮬레이션은 단일 위치나 동시에 여러 위치에서 고려하고 있는 속성값의 불확실성 추정에 활용될 수 있다 (Goovaerts, 1997;Deutsch and Journel, 1998;Chilès and Delfiner, 2012).…”