Optimized and robust experimental design: a non-linear application to EM sounding

Pragmatic experimental design requires objective consideration of several classes of information including the survey goals, the range of expected Earth responses, acquisition costs, instrumental capabilities, experimental conditions and logistics. In this study we consider the ramifications of maximizing model parameter resolution through non-linear experimental design. Global optimization theory is employed to examine and rank different EM sounding survey designs in terms of model resolution as defined by linearized inverse theory. By studying both theoretically optimal and heuristic experimental survey configurations for various quantities of data, it is shown that design optimization is critical for minimizing model variance estimates, and is particularly important when the inverse problem becomes nearly underdetermined. We introduce the concept of robustness so that survey designs are relatively immune to the presence of potential bias errors in important data. Bias may arise during practical measurement, or from designing a survey using an appropriate model.