We present a novel pilot-point-based hydraulic tomography (HT) inversion procedure to delineate preferential flow paths and estimate hydraulic properties in a fractured aquifer. Our procedure considers a binary prior model developed using a randomized algorithm. The randomized algorithm involves discretizing the domain into grid cells, assigning a binary label to each cell, traversing the grid randomly, and choosing the optimal grid configuration cell-by-cell. This binary prior model is used to guide the placement of pilot points and to constrain aquifer parameters during pilot-point-based HT inversion. A two-dimensional fractured granite rock block was considered to test our methodology under controlled laboratory conditions. Multiple pumping tests were conducted at selected ports and the pressure responses were monitored. The pumping datasets thus obtained were preprocessed using median filters to remove random noise, and then analyzed using the proposed procedure. The proposed binary prior algorithm was implemented in C++ by supplying the forward groundwater model, HydroGeoSphere (HGS). Pilot-point-assisted HT inversion was performed using the parameter-estimation tool, coupled to HGS. The resulting parameter distributions were assessed by: (1) a visual comparison of the K-and S s -tomograms with the known topology of the fractures and (2) comparing model predictions with measurements made at two validation ports that were not used in calibration. The performance assessment revealed that HT with the proposed randomized binary prior could be used to recover fracture-connectivity and to predict drawdowns in fractured aquifers with reasonable accuracy, when compared to a conventional pilot-point inversion scheme.

We present a novel method to estimate the hydraulic and storage properties of a heterogeneous aquifer system using pilot‐point‐based hydraulic tomography (HT) inversion in conjunction with a geophysical a priori model. The a priori model involved a soil stratification obtained by combining electrical resistivity tomography inversion and field data from hydrogeological experiments. Pilot‐point densities were assigned according to the stratification, which also constrained aquifer parameters during HT inversion. The forward groundwater flow model, HydroGeoSphere, was supplied to the parameter‐estimation tool, PEST, to perform HT inversion. The performance of our method was evaluated on a hypothetical, two‐dimensional, multi‐layered, granitic aquifer system representative of those commonly occurring in the Kandi region in Telangana. Inversion results were compared using two commonly adopted methods of modeling parameter‐heterogeneity: (1) using piece‐wise zones of property values obtained from geostatistical interpolation of local‐scale estimates; and (2) HT inversion starting from a homogeneous parameter field with a uniform distribution of pilot‐points. Performances of the inverted models were evaluated by conducting independent pumping tests and statistical analyses (using a Taylor diagram) of the model‐to‐measurement discrepancies in drawdowns. Our results showed that using the aforementioned geophysical a priori model could improve the parameter‐estimation process.

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