Linearized and nonlinear techniques are presented fordetermining estimates of parameter uncertainty within atwo-dimensional iterative Born scheme. The scheme employslow frequency (<100 kHz) magnetic dipole sources in one well,and uses measurements of the vertical magnetic field in asecond well to invert for the electrical conductivitydistribution between the two boreholes. For computationalefficiency a localized nonlinear approximation is employed tocompute the sensitivity matrix. Parameter variance estimatesare determined using an iterative Monte Carlo technique thatassumes the data contain measurement noise, and that constraintassumptions imposed on the model are in error. The a posteriorimodel covariance matrix is determined statistically for thelinearized technique by rerunning the last iteration of thenonlinear inversion N times, each time adding random errors tothe data and constraints. The nonlinear approach involvesrerunning the full inversion N times. Two oil field examplesfrom California indicate that the linearized approach producesthe same general pattern in the uncertainty estimates as thenonlinear estimation process. However, the linearized estimatesare smaller in magnitude and show less spatial variationcompared to the full nonlinear estimates, and the deviationbetween the two techniques increases as the contrast betweenthe maximum and minimum conductivities within the inversiondomain becomes greater.