| Title | Design strategies for electromagnetic geophysical surveys |
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| Author | Maurer, H; Boerner, D E; Curtis, A |
| Source | Inverse Problems vol. 16, 2000 p. 1097-1117 |
| Links | Abstract - Résumé
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| Links | Corrigendum
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| Image |  |
| Year | 2000 |
| Alt Series | Geological Survey of Canada, Contribution Series 2000067 |
| Document | serial |
| Lang. | English |
| Media | paper |
| Subjects | geophysics; e m surveys; magnetic disturbances; magnetic field; magnetic interpretations; magnetic properties; magnetometers; magnetosphere; magnetic surveys; magnetism |
| Illustrations | formulae; graphs; schematic diagrams |
| Released | 2000 01 01 |
| Abstract | 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 data and subsurface parameters, designing experiments that provide the maximum information content within a given budget can be quite difficult. Using examples from direct-current electrical and frequency-domain EM
applications, we review four approaches to quantitative experimental design. Repeated forward modelling is effective in feasibility studies, but may be cumbersome and time-consuming for studying complete data and model spaces. Examining Fr´echet
derivatives provides more insights into sensitivity to perturbations of model parameters, but only in the linear space around the trial model and without easily accounting for combinations of model parameters. A related sensitivity measure, the data
importance function, expresses the influence each data point has on determining the final inversion model. It considers 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 approach, statistical experimental design. This robust survey planning method, which
is based on global optimization algorithms, can be customized for individual needs. It can be used to optimize the survey layout for a particular subsurface structure and is an appropriate procedure for nonlinear experimental design in which ranges
of subsurface models are considered simultaneously. |
| GEOSCAN ID | 211505 |
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