Academic Scope
This sub-session focuses on emerging technologies that support mineral exploration, resource assessment, and geoscientific interpretation. Advances in artificial intelligence, geostatistics, subsurface modeling, and integrated data systems are creating new opportunities to improve exploration success and reduce uncertainty in resource evaluation.
The sub-session welcomes contributions related to AI-assisted mineral exploration, geological interpretation, ore grade estimation, resource modeling, drilling optimization, and geotechnical analysis. Research integrating machine learning with geostatistics, subsurface characterization, and digital exploration workflows is particularly encouraged.
The session aims to foster innovation in exploration technologies and promote data-driven approaches that enhance geological understanding, resource discovery, and sustainable mineral development.
Session Metadata
Target audience: Researchers in mining engineering, geoscience, and AI Engineers in mining operations and equipment systems Data scientists and AI specialists in industrial applications Industry professionals in digital mining and automation Graduate students and early-career researchers
Recommended Contributions
Original research, case studies, applied methodological papers, and evidence-based perspectives aligned with the scope of this sub-session.
Author Guidance
Abstracts should be submitted in English (250–300 words), clearly linked to this sub-session code, and should state objective, methodology, key results, and relevance.
Review Fit
Submissions should demonstrate scientific relevance, originality, methodological quality, clarity, and alignment with the session theme.
Fees & Bank Transfer
Use the details below to transfer your registration fee. After transferring, log in and confirm the payment on your submission page — the secretariat will verify the receipt.
- Bank
- ГОЛОМТ
- Account #
47001 500 8115007020- Holder
- ШУТИС ГУУС
- Currency
- USD
