Japan Geoscience Union Meeting 2019

Presentation information

[E] Poster

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI29] Near Surface Investigation and Modeling for Groundwater Resources Assessment and Conservation

Tue. May 28, 2019 3:30 PM - 5:00 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Jui-Pin Tsai(National Cheng Kung University, Taiwan), Hwa-Lung Yu(National Taiwan University), Ching-Chung Cheng(National Chiao Tung University Taiwan), Jing-Sen Cai(China University of Geosciences)

[MGI29-P06] Data Assimilation in Hydraulic Conductivity Identification using Bayesian Statistical Method

*Shih-Yang Cheng1, Kuo-Chin Hsu1 (1.National Cheng Kung Univ.)

Keywords:Data assimilation, Bayesian statistical method, Hydraulic conductivity, Nonlinear

Characterizing spatially heterogeneous hydraulic conductivity plays a crucial role in groundwater resources management and subsurface contaminant remediation. Since the direct measurements of hydraulic conductivity are sparse, the uncertainty is inherent in the estimated parameters. We propose decreasing the uncertainty by assimilating auxiliary data (electrical resistivity) with the direct data (hydraulic conductivity) using Bayesian statistical method. Different from classical geostatistical methods, both linear and nonlinear relations between the direct and auxiliary data can be considered in Bayesian statistical method. A synthetic example is designed to evaluate the method. Results show that the accuracy of hydraulic conductivity is improved through the Bayesian statistical method. Moreover, the effectiveness of auxiliary data in estimation error decrease is more significant when high correlation exists. However, the relation type has little influence on the accuracy. The estimation uncertainty quantified by variance is also compared. The uncertainty is inversely proportional to the amounts of auxiliary data and the correlation in both linear and nonlinear cases. The usefulness of auxiliary data in variance reduction is more obvious in linear cases. A systemic discussion of the sampling strategies of auxiliary data for improving hydraulic conductivity identification is also proposed.