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<p>Understanding the underground distribution of geological facies is essential for modern mining operations. This study integrates 11 borehole data points (ranging from 7 to 15 m in depth) and 34 Vertical Electrical Sounding (VES) measurements to model the geological structure of the Gouamiz limestone/gypsum evaporite deposit in Algeria. Borehole data provided direct lithological information, while VES data contributed insights into subsurface resistivity, aiding in the identification of facies in areas lacking borehole coverage. However, VES is subject to limitations, such as reduced resolution at greater depths and potential ambiguities in layer boundaries. Advanced geophysical inversion techniques and unsupervised machine learning algorithms were applied to enhance lithological predictions, achieving approximately 70% overall model accuracy. The resulting 3D geological model delineated substantial reserves: an estimated 2.3 million m<sup>3</sup> of limestone and 0.981 million m<sup>3</sup> of gypsum, representing significant economic potential with prospective market applications in construction, cement manufacturing, and agriculture. The study demonstrates the effectiveness of integrating geophysical and geological data for accurate subsurface characterization and proposes a transferable methodological framework for similar evaporite deposits globally. Nevertheless, limitations such as spatial bias in the calibration dataset and the geological complexity of the deposit were identified. Future research should incorporate additional borehole data, employ supervised learning algorithms, and consider higher-resolution geophysical techniques such as seismic reflection to improve accuracy and robustness. These insights contribute to more informed resource management and support sustainable mining strategies.</p>

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Integrated geophysical and unsupervised machine learning approach for 3D geological modeling of a limestone-gypsum deposit in Algeria: insights from borehole and resistivity data

  • 期刊论文

  • 发表日期：

    2025年6月11日

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  • Youcef Cheikhaoui,
  • Hamza Cheniti,
  • Ismail Benmelik,
  • Hani Amir Aouissi

摘要

Understanding the underground distribution of geological facies is essential for modern mining operations. This study integrates 11 borehole data points (ranging from 7 to 15 m in depth) and 34 Vertical Electrical Sounding (VES) measurements to model the geological structure of the Gouamiz limestone/gypsum evaporite deposit in Algeria. Borehole data provided direct lithological information, while VES data contributed insights into subsurface resistivity, aiding in the identification of facies in areas lacking borehole coverage. However, VES is subject to limitations, such as reduced resolution at greater depths and potential ambiguities in layer boundaries. Advanced geophysical inversion techniques and unsupervised machine learning algorithms were applied to enhance lithological predictions, achieving approximately 70% overall model accuracy. The resulting 3D geological model delineated substantial reserves: an estimated 2.3 million m3 of limestone and 0.981 million m3 of gypsum, representing significant economic potential with prospective market applications in construction, cement manufacturing, and agriculture. The study demonstrates the effectiveness of integrating geophysical and geological data for accurate subsurface characterization and proposes a transferable methodological framework for similar evaporite deposits globally. Nevertheless, limitations such as spatial bias in the calibration dataset and the geological complexity of the deposit were identified. Future research should incorporate additional borehole data, employ supervised learning algorithms, and consider higher-resolution geophysical techniques such as seismic reflection to improve accuracy and robustness. These insights contribute to more informed resource management and support sustainable mining strategies.