  A Jacobian-free inversion method is presented to accelerate Electrical
Resistivity Tomography (ERT) for shallow aquifer characterization. The ERT
problem typically implements the adjoint state method to efficiently compute
Jacobian during the inversion. However, the adjoint state method needs
intrusive forward model code changes and may not be computationally scalable
with many observations especially when one performs 3D ERT surveys with dense
multi-electrode arrays. Here the Principal Component Geostatistical Approach
(PCGA), a fast and scalable Jacobian-free inverse modeling method, is applied
to solve a high dimensional data-intensive ERT problem. The PNNL's ERT
simulation software E4D was linked to the python interface pyPCGA without
intrusive code change and the example code is upload in a public repository.
The result in this study shows that high-resolution 3D subsurface
characterization is computationally feasible, which would have a great
potential for implementations in practice.
