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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geoinformatics
Volume 12 - 2024 | doi: 10.3389/feart.2024.1473325

Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization

Provisionally accepted
Saâd Soulaimani Saâd Soulaimani 1,2*Ayoub Soulaimani Ayoub Soulaimani 3Kamal Abdelrahman Kamal Abdelrahman 4*Abdelhalim Miftah Abdelhalim Miftah 5Mohammed S. Fnais Mohammed S. Fnais 4Biraj Kanti Mondal Biraj Kanti Mondal 6
  • 1 Resources Valorization, Environment and Sustainable Development Research Team, Department of Mines, Rabat School of Mines, Rabat, Morocco
  • 2 Geology and Sustainable Mining Institute, Mohammed VI Polytechnic University, Ben Guerir, Morocco
  • 3 Natural resources and sustainable development Laboratory, Department of Earth Sciences, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco
  • 4 Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia
  • 5 Faculty of Sciences and Techniques, Laboratory Physico-chemistry of Processes and Materials, Hassan Premier University, Settat, Beni Mellal-Khenifra, Morocco
  • 6 Department of Geography, Netaji Subhas Open University, Kolkata, West Bengal, India

The final, formatted version of the article will be published soon.

    Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this strategy is leveraging population search algorithms to fine-tune hyperparameters, thus boosting prediction accuracy. Notably, Bayesian optimization has been applied for the first time to select the most effective learning parameters for artificial neural network classifiers used in lithology identification. This technique utilizes the capability of Bayesian optimization to utilize past classification outcomes to enhance the lithology models performance based on physical parameters calculated from well log data. In a comparison of artificial neural network architectures, the Bayesian-optimized artificial neural network (BOANN) demonstrably achieved the superior classification accuracy in validation and significantly outperformed a non-optimized wide, bilayer, and tri-layer network configurations, indicating that incorporating Bayesian optimization can significantly advance lithofacies recognition, thus offering a more accurate and intelligent solution for identifying lithology.

    Keywords: Geology, Lithology identification, machine learning, Neural Network, Bayesian optimization

    Received: 30 Jul 2024; Accepted: 25 Oct 2024.

    Copyright: © 2024 Soulaimani, Soulaimani, Abdelrahman, Miftah, Fnais and Mondal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Saâd Soulaimani, Resources Valorization, Environment and Sustainable Development Research Team, Department of Mines, Rabat School of Mines, Rabat, Morocco
    Kamal Abdelrahman, Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia

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