caa a22 4500 605538875 CHVBK 20210128100904.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s11053-014-9241-0 doi (NATIONALLICENCE)springer-10.1007/s11053-014-9241-0 A Methodology for Sensitivity Analysis Based on Regression: Applications to Handle Uncertainty in Natural Resources Characterization [Elektronische Daten] [Yevgeniy Zagayevskiy, Clayton Deutsch] Uncertainty in a natural resource model represents risk that should be minimized for improved management. Natural resources are components of complex earth science systems, which can be exhaustively sampled, numerically modeled with Monte Carlo simulation, or both, to understand their underlying nature. A numerical model represents relationships between a response variable and predictor variables. Uncertainty in a response variable can be observed directly, but understanding the importance of each predictor variable requires further post-processing of the numerical model. A methodology of local sensitivity analysis, based on linear and quadratic regression models, is developed to help understand the uncertainty contribution of each predictor variable to the response model. Sensitivity coefficients, predicted response values, and summary statistics with model utility tests for the regression models are evaluated. The importance of standardized sensitivity coefficients and other measures are developed. Standardized sensitivity coefficients represent the contribution of uncertainty in the predictor variables to uncertainty in model response. Results of the sensitivity analysis are visually summarized in the form of extended tornado charts. The proposed methodology is applied to representative petroleum and mining case studies. The methodology is robust, efficient, descriptive, and straightforward. Understanding of the contribution of each predictor variable to the response variable is useful for minimization of model response uncertainty, decision-making, and further study. International Association for Mathematical Geosciences, 2014 Local sensitivity analysis nationallicence Sensitivity coefficients nationallicence Tornado chart nationallicence Linear regression model nationallicence Quadratic regression model nationallicence Zagayevskiy Yevgeniy Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada aut Deutsch Clayton Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada aut Natural Resources Research Springer US; http://www.springer-ny.com 24/3(2015-09-01), 239-274 1520-7439 24:3<239 2015 24 11053 https://doi.org/10.1007/s11053-014-9241-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11053-014-9241-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zagayevskiy Yevgeniy Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Deutsch Clayton Centre for Computational Geostatistics (CCG), School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering, Markin/CNRL Natural Resources Engineering Facility, University of Alberta, 9105 116th St., T6G 2W2, Edmonton, AB, Canada aut NATIONALLICENCE 773 0- Natural Resources Research Springer US; http://www.springer-ny.com 24/3(2015-09-01), 239-274 1520-7439 24:3<239 2015 24 11053