A simultaneous successive linear estimator and a guide for hydraulic tomography analysis

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.