- journal_title：Vadose Zone Journal
- Contributor：Shuyun Liu ; Tian-Chyi J. Yeh
- Publisher：Soil Science Society of America
- Date：2004-
- Format：text/html
- Language：en
- Identifier：10.2113/3.2.681
- journal_abbrev：Vadose Zone Journal
- issn：1539-1663
- volume：3
- issue：2
- firstpage：681
- section：ORIGINAL RESEARCH
Electrical resistivity tomography (ERT), during the past few years, has emerged as a potentially cost-effective, noninvasive tool for imaging changes of moisture content in the vadose zone. The accuracy of ERT surveys, however, has been the subject of debate because of its nonunique inverse solution and spatial variability in the constitutive relation between resistivity and moisture content. In this paper, an integrative inverse approach for ERT, based on a stochastic information fusion concept of Yeh and Šimůnek, was developed to derive the best unbiased estimate of the moisture content distribution. Unlike classical ERT inversion approaches, this new approach assimilates prior information about the geological and moisture content structures in a given geological medium, as well as sparse point measurements of the moisture content, electrical resistivity, and electric potential. Using these types of data and considering the spatial variability of the resistivity–moisture content relation, the new approach directly estimates three-dimensional moisture content distributions instead of simply changes in moisture content in the vadose zone. Numerical experiments were conducted to investigate the effect of uncertainties in the prior information on the estimate. The effects of spatial variability in the constitutive relation were then examined on the interpretation of the change in moisture content, based on the change in electrical resistivity from the ERT survey. Finally, the ability of the integrative approach was tested by directly estimating moisture distributions in three-dimensional, heterogeneous vadose zones. Results show that the integrative approach can produce accurate estimates of the moisture content distributions and that incorporating some measurements of the moisture content is essential to improve the estimate.