Uncertainty analysis in regularized multidimensional electromagnetic imaging: A nonlinear most-squares approach

Formally, model uncertainty and non-uniqueness can be reduced by combining measurements of fundamentally different physical attributes of a subsurface target under investigation or by using available a priori information about the target. Geophysical measurements are typically band-limited in nature and contain noise. The nonlinear most-squares formalism allows for combining observations and their associated errors in an objective manner to determine the model bounds. A regularized most-squares appraisal method is described in detail and illustrated with a field example.