Geophysical measurements increasingly are being used in hydrologic field stud-­­ ies because of their ability to provide high-­­resolution images of the subsurface. In particular, tomographic imaging methods can produce maps of physical property distributions that have significant potential to improve subsurface characteriza-­­ tion and enhance monitoring of hydrologic processes. In the tomographic imag-­­ ing approach, geophysical images of the subsurface are converted to hydrologic property maps using petrophysical relations. In field studies, this transformation is complicated because measurement sensitivity and averaging during data inver-­­ sion result in tomographic images that have spatially variable resolution (i.e., the estimated property values in the geophysical image represent averages of the true subsurface properties). Standard approaches to petrophysics do not account for vari-­­ able geophysical resolution, and thus it is difficult to obtain quantitative estimates of hydrologic properties. We compare two new approaches that account for variable geophysical resolution: a Random Field Averaging (RFA) method and Full Inverse Statistical Calibration (FISt). The RFA approach uses a semi-­­analytical method whereas FISt calibration is based on a numerical solution to the problem.