Revolutionizing Petrology and Mineralogy: The Study of AI and Advanced Sensor Technologies

Year : 2025 | Volume : 02 | Issue : 02 | Page : 1 11
    By

    Kazi Kutubuddin Sayyad Liyakat,

  1. Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Maharashtra, India

Abstract

Petrology and mineralogy are fundamental to understanding Earth’s intricate processes, from crustal evolution to economic resource formation. However, traditional methods, while precise, are often laborious, time-consuming, and occasionally subject to interpretive bias. This abstract explores the transformative potential of integrating cutting-edge Artificial Intelligence (AI) and advanced sensor technologies to revolutionize data acquisition, analysis, and interpretation in these critical geosciences.  Advanced sensor technologies, including high-resolution spectral imaging (hyperspectral, Raman), automated X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS), micro-computed tomography (µCT), and even drone-mounted LiDAR, are generating unprecedented volumes of multi-modal data. These sensors provide rapid, non-destructive, and highly detailed information on mineral composition, crystal structure, textural relationships, and bulk rock chemistry, both in the laboratory and in field settings. AI algorithms, including machine learning (ML), deep learning (DL), and computer vision, are uniquely positioned to process and interpret these vast, complex datasets. These algorithms enable automated mineral identification, precise rock classification, quantitative textural analysis, and the detection of subtle geological patterns and anomalies that might elude human observation. Applications span from automated thin section analysis and mineral liberation analysis to predictive modeling of ore grades, identification of alteration zones, and classification of extraterrestrial materials. The synergy reduces subjectivity, significantly enhances the efficiency and accuracy of geological investigations, and accelerates the discovery pipeline. This integration promises not only to streamline existing workflows but also to unveil previously undetectable relationships within geological systems, pushing the boundaries of scientific inquiry. The future of petrology and mineralogy lies in these intelligent, data-driven approaches, fostering autonomous laboratories, intelligent field mapping systems, and advanced predictive models crucial for sustainable resource exploration, environmental monitoring, and understanding planetary evolution.

Keywords: Petrology, mineralogy, artificial intelligence, machine learning, sensor technology, hyperspectral imaging, XRD, XRF, SEM-EDS, automated analysis, geosciences

[This article belongs to International Journal of Minerals ]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat. Revolutionizing Petrology and Mineralogy: The Study of AI and Advanced Sensor Technologies. International Journal of Minerals. 2025; 02(02):1-11.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat. Revolutionizing Petrology and Mineralogy: The Study of AI and Advanced Sensor Technologies. International Journal of Minerals. 2025; 02(02):1-11. Available from: https://journals.stmjournals.com/ijmi/article=2025/view=232613


References

1.      James C. Hower , Robert F. Rathbone, J.David Robertson, Gilman Peterson, Alan S. Trimble, (1999). Petrology, mineralogy, and chemistry of magnetically-separated sized fly ash, Fuel, Volume 78, Issue 2, January 1999, Pages 197-203. https://doi.org/10.1016/S0016-2361(98)00132-X
2.      S.K. Haldar, Josip Tišljar, (2014). Chapter 1 – Rocks and Minerals, Introduction to Mineralogy and Petrology, 2014, Pages 1-37.

  1. A. Tamboli, V. A. Sawant, M. H. M. and S. Sathe, (2024). AI-Driven-IoT(AIIoT) Based Decision-Making- KSK Approach in Drones for Climate Change Study, 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), Gobichettipalayam, India, 2024, pp. 1735-1744, doi: 10.1109/ICUIS64676.2024.10866450.
  2. T. Shaikh, (2025). Empowering the IoT: The Study on Role of Wireless Charging Technologies, Journal of Control and Instrumentation Engineering, vol. 11, no. 2, pp. 29-39, Jul. 2025.
  3. KKS Liyakat. (2023).Detecting Malicious Nodes in IoT Networks Using Machine Learning and Artificial Neural Networks, 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1-5, doi:10.1109/ESCI56872.2023.10099544. Available at: https://ieeexplore.ieee.org/document/10099544/
  4. Liyakat, K.K.S. (2023a). Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_3
  5. (2025c). IoT Technologies for the Intelligent Dairy Industry: A New Challenge. In S. Thandekkattu& N. Vajjhala (Eds.), Designing Sustainable Internet of Things Solutions for Smart Industries (pp. 321-350). IGI Global. https://doi.org/10.4018/979-8-3693-5498-8.ch012
  6. (2025d). AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring. In L. Özgür Polat & O. Polat (Eds.), AI-Driven Innovation in Healthcare Data Analytics (pp. 277-306). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7277-7.ch009
  7. Petrelli M. Machine learning in petrology: state-of-the-art and future perspectives. Journal of Petrology. 2024 May 1;65(5):egae036.
  8. Ahari HD. A Review of AI for Efficient Mineral Identification. ISERDAR. 2024 Aug 30;2(2):8-20.
Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 17/10/2025
Accepted 03/11/2025
Published 05/11/2025
Publication Time 19 Days
129

Login


My IP

PlumX Metrics