[1] In this study, a geostatistically based estimator is developed that simultaneously includes all observed transient hydrographs from hydraulic tomography to map aquifer heterogeneity. To analyze tomography data, a data preprocessing procedure (including diagnosing and wavelet denoising analysis) is recommended. A least squares approach is then introduced to estimate effective parameters and spatial statistics of heterogeneity that are the required inputs for the geostatistical estimator. Since wavelet denoising does not completely remove noise from observed hydrographs, a stopping criterion is established to avoid overexploitation of the imperfect hydrographs. The estimator and the procedures are then tested in a synthetic, cross-sectional aquifer with hierarchical heterogeneity and a vertical sandbox with prearranged heterogeneity. Results of the test indicate that with this estimator and preprocessing procedures, hydraulic tomography can effectively map hierarchical heterogeneity in the synthetic aquifer as well as in the sandbox. In addition, the study shows that using the estimated hydraulic conductivity and specific storage fields of the sandbox, the classic groundwater flow model accurately predicts temporal and spatial distributions of drawdown induced by an independent pumping event in the sandbox. On the other hand, the classic groundwater flow model yields less satisfactory results when equivalent homogeneous properties of the sandbox are used.

Fracture zones and their connectivity in geologic media are of great importance to ground water resources management as well as ground water contamination prevention and remediation. In this paper, we applied a recently developed hydraulic tomography (HT) technique and an analysis algorithm (sequential successive linear estimator) to synthetic fractured media. The application aims to explore the potential utility of the technique and the algorithm for characterizing fracture zone distribution and their connectivity. Results of this investigation showed that using HT with a limited number of wells, the fracture zone distribution and its connectivity (general pattern) can be mapped satisfactorily although estimated hydraulic property fields are smooth. As the number of wells and monitoring ports increases, the fracture zone distribution and connectivity become vivid and the estimated hydraulic properties approach true values. We hope that the success of this application may promote the development and application of the new generations of technology (i.e., hydraulic, tracer, pneumatic tomographic surveys) for mapping fractures and other features in geologic media.

[1] Data from tomographic surveys make an inverse problem better posed in comparison to the data from a single excitation source. A tomographic survey provides different coverages and perspectives of subsurface heterogeneity: nonfully redundant information of the subsurface. Fusion of these pieces of information expands and enhances the capability of a conventional survey, provides cross validation of inverse solutions, and constrains inherently ill posed field-scale inverse problems. Basin-scale tomography requires energy sources of great strengths. Spatially and temporally varying natural stimuli are ideal energy sources for this purpose. In this study, we explore the possibility of using river stage variations for basin-scale subsurface tomographic surveys. Specifically, we use numerical models to simulate groundwater level changes in response to temporal and spatial variations of the river stage in a hypothetical groundwater basin. We then exploit the relation between temporal and spatial variations of well hydrographs and river stage to image subsurface heterogeneity of the basin. Results of the numerical exercises are encouraging and provide insights into the proposed river stage tomography. Using naturally recurrent stimuli such as river stage variations for characterizing groundwater basins could be the future of geohydrology. However, it calls for implementation of sensor networks that provide long-term and spatially distributed monitoring of excitation as well as response signals on the land surface and in the subsurface.

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