AuthorGanfoud, Ahmed Abulaid.
KeywordsHydraulic engineering -- Laboratory manuals.
Hydraulic engineering -- Experiments.
Hydraulic laboratories -- Arizona -- Tucson.
Hydraulics -- Laboratory manuals.
Hydraulics -- Experiments.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Degree ProgramGraduate College
Civil Engineering and Engineering Mechanics
Degree GrantorUniversity of Arizona
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Fusion of Time-Lapse Gravity Survey and Hydraulic Tomography for Estimating Spatially Varying Hydraulic Conductivity and Specific Yield FieldsTsai, Jui-Pin; Yeh, Tian-Chyi Jim; Cheng, Ching-Chung; Zha, Yuanyuan; Chang, Liang-Cheng; Hwang, Cheinway; Wang, Yu-Li; Hao, Yonghong; Univ Arizona, Dept Hydrol & Atmospher Sci; Department of Civil Engineering; National Chiao-Tung University; Hsinchu Taiwan; et al. (AMER GEOPHYSICAL UNION, 2017-10)Hydraulic conductivity (K) and specific yield (S-y) are important aquifer parameters, pertinent to groundwater resources management and protection. These parameters are commonly estimated through a traditional cross-well pumping test. Employing the traditional approach to obtain detailed spatial distributions of the parameters over a large area is generally formidable. For this reason, this study proposes a stochastic method that integrates hydraulic head and time-lapse gravity based on hydraulic tomography (HT) to efficiently derive the spatial distribution of K and Sy over a large area. This method is demonstrated using several synthetic experiments. Results of these experiments show that the K and Sy fields estimated by joint inversion of the gravity and head data set from sequential injection tests in unconfined aquifers are superior to those from the HT based on head data alone. We attribute this advantage to the mass constraint imposed on HT by gravity measurements. Besides, we find that gravity measurement can detect the change of aquifer's groundwater storage at kilometer scale, as such they can extend HT's effectiveness over greater volumes of the aquifer. Furthermore, we find that the accuracy of the estimated fields is improved as the number of the gravity stations is increased. The gravity station's location, however, has minor effects on the estimates if its effective gravity integration radius covers the well field.
A Reduced-Order Successive Linear Estimator for Geostatistical Inversion and its Application in Hydraulic TomographyZha, Yuanyuan; Yeh, Tian-Chyi J.; Illman, Walter A.; Zeng, Wenzhi; Zhang, Yonggen; Sun, Fangqiang; Shi, Liangsheng; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2018-03)Hydraulic tomography (HT) is a recently developed technology for characterizing high-resolution, site-specific heterogeneity using hydraulic data (n(d)) from a series of cross-hole pumping tests. To properly account for the subsurface heterogeneity and to flexibly incorporate additional information, geostatistical inverse models, which permit a large number of spatially correlated unknowns (n(y)), are frequently used to interpret the collected data. However, the memory storage requirements for the covariance of the unknowns (n(y) x n(y)) in these models are prodigious for large-scale 3-D problems. Moreover, the sensitivity evaluation is often computationally intensive using traditional difference method (n(y) forward runs). Although employment of the adjoint method can reduce the cost to n(d) forward runs, the adjoint model requires intrusive coding effort. In order to resolve these issues, this paper presents a Reduced-Order Successive Linear Estimator (ROSLE) for analyzing HT data. This new estimator approximates the covariance of the unknowns using Karhunen-Loeve Expansion (KLE) truncated to n(kl) order, and it calculates the directional sensitivities (in the directions of n(kl) eigenvectors) to form the covariance and cross-covariance used in the Successive Linear Estimator (SLE). In addition, the covariance of unknowns is updated every iteration by updating the eigenvalues and eigenfunctions. The computational advantages of the proposed algorithm are demonstrated through numerical experiments and a 3-D transient HT analysis of data from a highly heterogeneous field site.
Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic TomographyDong, Yanhui; Fu, Yunmei; Yeh, Tian‐Chyi Jim; Wang, Yu‐Li; Zha, Yuanyuan; Wang, Liheng; Hao, Yonghong; Univ Arizona, Dept Hydrol & Atmospher Sci (AMER GEOPHYSICAL UNION, 2019-04-23)Hydraulic tomography (HT) has emerged as a potentially viable method for mapping fractures in geologic media as demonstrated by recent studies. However, most of the studies adopted equivalent porous media (EPM) models to generate and invert hydraulic interference test data for HT. While these models assign significant different hydraulic properties to fractures and matrix, they may not fully capture the discrete nature of the fractures in the rocks. As a result, HT performance may have been overrated. To explore this issue, this study employed a discrete fracture network (DFN) model to simulate hydraulic interference tests. HT with the EPM model was then applied to estimate the distributions of hydraulic conductivity (K) and specific storage (S-s) of the DFN. Afterward, the estimated fields were used to predict the observed heads from DFN models, not used in the HT analysis (i.e., validation). Additionally, this study defined the spatial representative elementary volume (REV) of the fracture connectivity probability for the entire DFN dominant. The study showed that if this spatial REV exists, the DFN is deemed equivalent to EPM and vice versa. The hydraulic properties estimated by HT with an EPM model can then predict head fields satisfactorily over the entire DFN domain with limited monitoring wells. For a sparse DFN without this spatial REV, a dense observation network is needed. Nevertheless, HT is able to capture the dominant fractures.