Geomodeling with Integration of Multi source Data by Bayesian Kriging in Underground Space

Abstract:

A subsurface model is usually built by integrating multi source geological data such as boreholes, geological maps and seismic interpretations. However, uncertainties inherited in these data are rarely quantified in the modeling process. In this study, Bayesian kriging method is introduced to integrate multi source geological data and estimate formation surface elevations. In this method, linear Bayes theory is applied to kriging estimation. Geological data is classified into hard and soft data. Hard data refers to coal seam data with enough confidence, such as boreholes. Soft data refers to coal seam data with uncertainty such as geological maps, cross sections and seismic interpretation information. Areal variable theory is employed to analyze spatial variation of both hard and soft data. This method is applied to the coal seam modeling of a coal mine in China. The estimates and errors of surface elevations are compared with those obtained from ordinary kriging method. Results show that Bayesian kriging method gives better results in terms of giving smaller errors of estimation. Therefore, Bayesian kriging is a useful method to incorporate multi source geological information and quantify uncertainties of geological data.