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Re-examination of Itakpe iron ore deposit for reserve estimation using geostatistics and artificial neural network techniques

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Abstract

This paper re-examines the Itakpe iron ore deposit using geostatistics and artificial neural network techniques. Set of exploration information on the deposit are used to develop ordinary kriging (OK) model that produced a minimal error. The sensitivity analysis is used to choose a multilayer perceptron (MLP) network model as the optimum network for the ANN. The OK model showed a better performance for grade estimation when compared with the MLP model. Thus, using OK, a total resource of about 12% lower than that of the conventional method, which is currently in use in Itakpe, is obtained.

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Authors and Affiliations

  1. Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

    Thomas Busuyi Afeni, Abiodun Ismail Lawal & Rasaq Ayodele Adeyemi

  2. Department of Mining and Geological Engineering, Botswana International University of Science & Technology (BIUST), Palapye, Botswana

    Thomas Busuyi Afeni

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  1. Thomas Busuyi Afeni
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  2. Abiodun Ismail Lawal
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  3. Rasaq Ayodele Adeyemi
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Correspondence to Abiodun Ismail Lawal.

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Responsible Editor: Biswajeet Pradhan

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Afeni, T.B., Lawal, A.I. & Adeyemi, R.A. Re-examination of Itakpe iron ore deposit for reserve estimation using geostatistics and artificial neural network techniques. Arab J Geosci 13, 657 (2020). https://doi.org/10.1007/s12517-020-05644-9

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