Interpretation of Magnetic Data Through Particle Swarm Optimization: Mineral Exploration Cases Studies
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This paper emphasizes the use of the particle swarm optimization to infer second moving average residual magnetic anomalies. This approach was used to remove the impact of the regional background up to the third-order polynomial by applying filters of successive window lengths. The body parameters evaluated are the amplitude coefficient, depth, magnetization angle, location of the object, and target shape. After that, the root mean squared error was calculated to compare the measured and calculated magnetic anomalies (the misfit). This proposed technique was utilized for synthetic examples as follows: The first model explains the effect of introducing noise, the second model demonstrates the impact of an interfering structure, and the third model shows the influence of a third-order regional magnetic field. Furthermore, this method was connected to three real examples from diabase dike and chromite ore exploration in Canada, Turkey, and Egypt. In all the cases, the assessed body parameters reasonably approximate the known values.
KeywordsParticle swarm optimization Second moving average method Depth Mineral exploration
The authors would like to thank Prof. Dr. John Carranza, Editor-in-Chief, and the reviewers for their keen interest, valuable comments on the manuscript, and improvements to this work.
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