  The standard smooth electrical resistivity tomography inversion produces an
estimate of subsurface conductivity that has blurred boundaries, damped
magnitudes, and often contains inversion artifacts. In many problems the
expected conductivity structure is well constrained in some parts of the
subsurface, but incorporating prior information in the inversion is not a
trivial task. In this study we developed an electrical resistivity tomography
inversion algorithm that combines parametric and smooth inversion strategies.
In regions where the subsurface is well constrained, the model was
parameterized with only a few variables, while the rest of the subsurface was
parameterized with voxels. We tested this hybrid inversion strategy on two
synthetic models that contained a well constrained highly resistive or
conductive near-surface horizontal layer and a target beneath. In each testing
scenario, the hybrid inversion improved resolution of feature boundaries and
magnitudes and had fewer inversion artifacts than the standard smooth
inversion. A sensitivity analysis showed that the hybrid inversion successfully
recovered subsurface features when a range of regularization parameters,
initial models, and data noise levels were tested. The hybrid inversion
strategy can potentially be expanded to a range of applications including
marine surveys, permafrost/frozen ground studies, urban geophysics, or anywhere
that prior information allows part of the model to be constrained with simple
geometric shapes.
