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    Stochastic analysis of oscillatory hydraulic tomography

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    Author
    Wang, Yu-Li
    Yeh, Tian-Chyi Jim
    Xu, Dong
    Li, Kuangjia
    Wen, Jet-Chau
    Huang, Shao-Yang
    Wang, Wenke
    Hao, Yonghong
    Affiliation
    Department of Hydrology and Atmospheric Sciences, University of Arizona
    Issue Date
    2021-02-27
    
    Metadata
    Show full item record
    Publisher
    Elsevier B.V.
    Citation
    Wang, Y. L., Yeh, T. C. J., Xu, D., Li, K., Wen, J. C., Huang, S. Y., ... & Hao, Y. (2021). Stochastic Analysis of Oscillatory Hydraulic Tomography. Journal of Hydrology, 126105.
    Journal
    Journal of Hydrology
    Rights
    © 2021 Elsevier B.V. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    This paper investigates the effectiveness of different oscillatory hydraulic tomography (OHT) frequencies for estimating heterogeneous fields. The analysis first formulates the effects of uncharacterized aquifer responses, T, and S fields as the ensemble mean residual flux and residual storage terms in the stochastic unconditional and conditional mean equations. These terms persist unless the T, S, or head fields are specified everywhere. We then conducted OHT to estimate the T and S fields using Monte Carlo experiments. The experiments show that using the heads in response to periodic pumping with different frequencies or multifrequency, the estimates' performance metrics vary from one realization to others. The mean performance metrics over many realizations are, however, indistinguishable, despite the frequency. We attribute the variation in the performance metrics to the lack of parameter or state variable ergodicity. Lastly, we emphasize the importance of dense monitoring networks and a cost-effective data collection strategy to improve the resolution of characterizing aquifer heterogeneity. © 2021 Elsevier B.V.
    Note
    24 month embargo; first published online 27 February 2021
    ISSN
    0022-1694
    DOI
    10.1016/j.jhydrol.2021.126105
    Version
    Final accepted manuscript
    Sponsors
    CRDF Global
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jhydrol.2021.126105
    Scopus Count
    Collections
    UA Faculty Publications

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