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A platform for research: civil engineering, architecture and urbanism
Quantifying Geological Uncertainty Using Conditioned Spatial Markov Chains
Geological uncertainty is the changeability due to one geomaterial embedded in another, particularly at the boundaries between the different geomaterial layers. Spatial Markov Chains (spMC) and information entropy were used in this study to simulate subsurface geomaterials and to quantify geological uncertainty in two dimensions (2D) based upon spare borehole data. The proposed methods were illustrated using a bridge site in Wyoming. Results showed that the multinomial categorical simulation (MCS) model provided more accurate description of geomaterials as measured by an uncertainty quantification of 0.006 and a confidence ratio of 91.9%. The reduction of geological uncertainty does not always imply the need for additional boreholes when data are available from boreholes that are well-spaced within the site. However, if there is need for additional boreholes, optimized locations to reduce uncertainty can be determined. Thus, the proposed approach could also serve as a guide for designing a site investigation plan.
Quantifying Geological Uncertainty Using Conditioned Spatial Markov Chains
Geological uncertainty is the changeability due to one geomaterial embedded in another, particularly at the boundaries between the different geomaterial layers. Spatial Markov Chains (spMC) and information entropy were used in this study to simulate subsurface geomaterials and to quantify geological uncertainty in two dimensions (2D) based upon spare borehole data. The proposed methods were illustrated using a bridge site in Wyoming. Results showed that the multinomial categorical simulation (MCS) model provided more accurate description of geomaterials as measured by an uncertainty quantification of 0.006 and a confidence ratio of 91.9%. The reduction of geological uncertainty does not always imply the need for additional boreholes when data are available from boreholes that are well-spaced within the site. However, if there is need for additional boreholes, optimized locations to reduce uncertainty can be determined. Thus, the proposed approach could also serve as a guide for designing a site investigation plan.
Quantifying Geological Uncertainty Using Conditioned Spatial Markov Chains
Oluwatuyi, Opeyemi E. (author) / Rajapakshage, Rasika (author) / Wulff, Shaun S. (author) / Ng, Kam (author)
Geo-Congress 2022 ; 2022 ; Charlotte, North Carolina
Geo-Congress 2022 ; 436-445
2022-03-17
Conference paper
Electronic Resource
English
Quantifying Geological Uncertainty Using Conditioned Spatial Markov Chains
| British Library Conference Proceedings | 2022
| British Library Online Contents | 2006