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Gravity data interpretation using the particle swarm optimisation method with application to mineral exploration

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Abstract

This paper describes a new method based on the particle swarm optimisation (PSO) technique for interpreting the second moving average (SMA) residual gravity anomalies. The SMA anomalies are deduced from the measured gravity data to eradicate the regional anomaly by utilising filters of consecutive window lengths (s-value). The buried structural parameters are the amplitude factor (A), depth (z), location (d) and shape (q) that are estimated from the PSO method. The discrepancy between the measured and the predictable gravity anomaly is estimated by the root mean square error. The PSO method is applied to two different theoretical and three real data sets from Cuba, Canada and India. The model parameters inferred from the method developed here are compared with the available geological and geophysical information.

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Acknowledgements

The authors would like to thank Prof N V Chalapathi Rao, Editor-in-Chief, Prof Arkoprovo Biswas, Associate Editor, and the two anonymous expert reviewers for their keen interest and constructive comments for improving our original manuscript. The first author wishes to thank the Science and Technology Development Fund (STDF) and the Institut Francais d’Egypte (IFE) for providing full support to the completion of this work.

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Authors and Affiliations

  1. Department of Geophysics, Cairo University, Giza, Egypt

    Khalid S Essa

  2. Institut de Physique du Globe de Strasbourg, EOST, CNRS, University of Strasbourg, Strasbourg, France

    Marc Munschy

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  1. Khalid S Essa
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Correspondence to Khalid S Essa.

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Corresponding editor: Arkoprovo Biswas

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Essa, K.S., Munschy, M. Gravity data interpretation using the particle swarm optimisation method with application to mineral exploration. J Earth Syst Sci 128, 123 (2019). https://doi.org/10.1007/s12040-019-1143-4

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  • DOI: https://doi.org/10.1007/s12040-019-1143-4

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