Development of a simultaneous search-based pilot point method for subsurface characterization

Abstract

Strategically applied geo-environmental clean-up methods require a better groundwater flow and transport model. Hydraulic conductivity of the subsurface is one of great sources of uncertainty of this model. In order to search hydraulic conductivities, the simultaneous search-based pilot point method (SSBM) was developed to reduce computational procedure of pilot point method and increase characterization accuracy using a global optimization tool (genetic algorithm). SSBM searches pilot point locations and hydraulic conductivities at selected pilot points simultaneously. In the four different scenarios, the comparison between random pilot point locations and SSBM showed that SSBM produced less than two orders magnitude differences in terms of average of minimum fitness for thirty trials (e.g. 4.05E−02 for scenario 2). With respect to average minimum fitness and average hydraulic conductivity difference, SSBM was comparable to D-optimality based pilot point method (DBM). SSBM produced lower average minimum fitness values and similar average hydraulic conductivity difference but it had more variance. Through these results, SSBM showed the potential to replace the DBM through reduced computational procedures in sensitivity calculation with consideration of variance minimization.