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Optimizing an experimental design for a CSEM experiment: methodology and synthetic tests

AuthorsRoux, E. ; García, Xavier
KeywordsElectrical properties
Inverse theory
Issue DateApr-2014
PublisherOxford University Press
CitationGeophysical Journal International 197(1): 135-148 (2014)
AbstractOptimizing an experimental design is a compromise between maximizing information we get about the target and limiting the cost of the experiment, providing a wide range of constraints. We present a statistical algorithm for experiment design that combines the use of linearized inverse theory and stochastic optimization technique. Linearized inverse theory is used to quantify the quality of one given experiment design while genetic algorithm (GA) enables us to examine a wide range of possible surveys. The particularity of our algorithm is the use of the multi-objective GA NSGA II that searches designs that fit several objective functions (OFs) simultaneously. This ability of NSGA II is helping us in defining an experiment design that focuses on a specified target area. We present a test of our algorithm using a 1-D electrical subsurface structure. The model we use represents a simple but realistic scenario in the context of CO2 sequestration that motivates this study. Our first synthetic test using a single OF shows that a limited number of well-distributed observations from a chosen design have the potential to resolve the given model. This synthetic test also points out the importance of a well-chosen OF, depending on our target. In order to improve these results, we show how the combination of two OFs using a multi-objective GA enables us to determine an experimental design that maximizes information about the reservoir layer. Finally, we present several tests of our statistical algorithm in more challenging environments by exploring the influence of noise, specific site characteristics or its potential for reservoir monitoring. © The Authors 2014. Published by Oxford University Press on behalf of The Royal Astronomical Society
Description14 pages, 8 figures, 5 tables, 3 appendix
Publisher version (URL)https://doi.org/10.1093/gji/ggt525
Identifiersdoi: 10.1093/gji/ggt525
issn: 0956-540X
e-issn: 1365-246X
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