Predictive groundwater modeling requires accurate information about
aquifer characteristics. Geophysical imaging is a powerful tool for
delineating aquifer properties at an appropriate scale and resolution,
but it suffers from problems of ambiguity. One way to overcome such
limitations is to adopt a simultaneous multitechnique inversion
strategy. We have developed a methodology for aquifer characterization
based on structural joint inversion of multiple geophysical data sets
followed by clustering to form zones and subsequent inversion for zonal
parameters. Joint inversions based on cross-gradient structural
constraints require less restrictive assumptions than, say, applying
predefined petro-physical relationships and generally yield superior
results. This approach has, for the first time, been applied to three
geophysical data types in three dimensions. A classification scheme
using maximum likelihood estimation is used to determine the parameters
of a Gaussian mixture model that defines zonal geometries from
joint-inversion tomograms. The resulting zones are used to estimate
representative geophysical parameters of each zone, which are then used
for field-scale petrophysical analysis. A synthetic study demonstrated
how joint inversion of seismic and radar traveltimes and electrical
resistance tomography (ERT) data greatly reduces misclassification of
zones (down from 21.3% to 3.7%) and improves the accuracy of retrieved
zonal parameters (from 1.8% to 0.3%) compared to individual
inversions. We applied our scheme to a data set collected in
northeastern Switzerland to delineate lithologic subunits within a
gravel aquifer. The inversion models resolve three principal
subhorizontal units along with some important 3D heterogeneity.
Petro-physical analysis of the zonal parameters indicated approximately
30% variation in porosity within the gravel aquifer and an increasing
fraction of finer sediments with depth.