Low electrical resistivity associated with plunging of the

The inversion is the Non-Linear Conjugate Gradient (NLCG) algorithm of Rodi and Mackie (2001). This seeks to efficiently find a model that minimizes the objective function) P(τ χ S 2 m + = where ∑ = = N 1 i 2 i 2 i 2 σ r χ is the sum of the squared data residuals weighted by their variances and P(m) is a structure penalty function. At each choice of trade-off parameter τ, the resulting model m has the minimum structure (as measured by the structure penalty) for the resulting misfit. Ideally, the goal of the inversion process is to find the model that fits the data just well enough to reflect all the geological information but none of the statistical noise. Clearly the resulting image depends on the choice of structure penalty. In this work, the penalty is a weighted integral of the Laplacian squared of the model. The weighting increases both horizontal and vertical derivatives by a factor equal to the vertical scale of each model element, which increases with depth and with horizontal distance outside the profile.