The structural approach to joint inversion, entailing common boundaries
or gradients, offers a flexible and effective way to invert diverse
types of surface-based and/or crosshole geophysical data. The
cross-gradients function has been introduced as a means to construct
models in which spatial changes in two distinct physical-property models
are parallel or antiparallel. Inversion methods that use such structural
constraints also provide estimates of nonlinear and nonunique
field-scale relationships between model parameters. Here, we jointly
invert crosshole radar and seismic traveltimes for structurally similar
models using an iterative nonlinear traveltime tomography algorithm.
Application of the inversion scheme to synthetic data demonstrates that
it better resolves lithologic boundaries than the individual inversions
alone. Tests of the scheme on GPR and seismic data acquired within a
shallow aquifer illustrate that the resultant models have improved
correlations with flowmeter data in comparison with models based on
individual inversions. The highest correlation with the flowmeter data
is obtained when the joint inversion is combined with a stochastic
regularization operator and the vertical integral scale is estimated
from the flowmeter data. Point-spread functions show that the most
significant resolution improvements offered by the joint inversion are
in the horizontal direction.