The growing global resource scarcity along with the criticality of high-tech-relevant raw material, poses immense challenges for the sustainable development of our society. Reducing the environmental footprint of mineral exploration and extraction requires sustainable solutions that are socio-economically viable. In this context, an accurate and effective resource characterization is essential not only for supporting economic resilience but also for mitigating environmental impacts and advancing the transition toward sustainable, semi-circular economic models. Emerging technologies, from autonomous robotic explorers to real-time data analytics, are redefining what is possible in mineral exploration and production. These innovations open opportunities to re-evaluate previously “non-economical” deposits, including abandoned sites, ultra-deep reserves, and small-scale resources, and to optimize recovery processes and footprints.
This session targets innovative tools and methodologies that are redefining raw material exploration and characterization. We emphasize multi-scale, multi-source and multi-disciplinary approaches that integrate advanced sensing, modelling, automation and data-driven solutions. The session focuses in particular on method innovations in the field of remote sensing, geophysics, geochemistry, raw material processing, as well as on recycling processes.
We encourage interdisciplinary studies which use a combination of methods to solve challenges as diverse as, but not limited to:
• Next-generation sensing and imaging: non-destructive techniques, core scanners, and airborne/ground-based sensors for high-resolution, accurate, precise, and efficient resource identification.
• Smart field and analytical approaches: geophysical and geochemical mapping, isotope dating, and novel sampling workflows for multi-scale ore body understanding.
• Digital modelling and simulation: advanced conceptual models and quantification methods for deposits and mineral systems.
• Automation and real-time decision-making: AI-driven, automated data processing that enhances resource management, mining selectivity, and recycling efficiency.
• Information integration and visualization: innovative platforms for merging data streams from diverse sensors to improve accuracy and reduce uncertainty.
• Data-driven discovery: machine learning, geostatistics, data fusion, and computational advances unlocking new insights in mineralogy and geochemistry.