Design strategies for electromagnetic geophysical surveys

Acquiring information on the Earth's electric and magnetic properties is a critical task in many geophysical applications. Since electromagnetic (EM) geophysical methods are based on nonlinear relationships between observed d ata and subsurface parameters. designing experiments that provide the maxim um information content within a given budget can be quite difficult. Using examples from direct-current electrical and frequency-domain EM application s, we review four approaches to quantitative experimental design. Repeated forward modelling is effective in feasibility studies, but may be cumbersom e and time-consuming for studying complete data and model spaces. Examining Frechet derivatives provides more insights into sensitivity to perturbatio ns of model parameters, but only in the linear space around the trial model and without easily accounting for combinations of model parameters. A rela ted sensitivity measure, the data importance function, expresses the influe nce each data point has on determining the final inversion model. It consid ers simultaneously all model parameters, but provides no information on the relative position of the individual points in the data space. Furthermore, it tends to be biased towards well resolved parts of the model space. Some of the restrictions of these three methods are overcome by the fourth appr oach, statistical experimental design. This robust survey planning method, which is based on global optimization algorithms, can be customized for ind ividual needs. It can be used to optimize the survey layout for a particula r subsurface structure and is an appropriate procedure for nonlinear experi mental design in which ranges of subsurface models are considered simultane ously.