Researchers at the Colorado School of Mines are teaming up with Minalyze AB to build an advanced geosciences research laboratory for non-destructive compositional analysis of drill core, the Sweden-based company says.
“This new laboratory establishes the Colorado School of Mines as a global leader in this emerging field with important applications in the development of Earth resources such as the critical minerals needed in the manufacturing of clean energy technologies,” Thomas Monecke, Director of the Center for Advanced Subsurface Earth Resource Models (CASERM) at the Colorado School of Mines, says.
“Minalyze’s choice of CASERM as a research partner is a testament to the calibre of our faculty and students we have, and the establishment of the new research facility will help our research team to advance solutions for the mining sector and contribute to our fundamental understanding of the geological processes resulting in the concentration of metals in the Earth’s crust.”
The new laboratory will support research conducted within CASERM, a collaborative research venture between the Colorado School of Mines and Virginia Tech supported by a consortium of mining companies and federal agencies aiming to transform the way geoscience data is used across the mining value chain.
Minalyze’s X-ray Fluorescence-backed CS scanner has been used throughout the mining sector for drill core analysis and, more recently, is being used in artificial intelligence-backed projects.
In addition to ore deposit research, the new core scanning laboratory will offer unparalleled opportunities for undergraduate and graduate student education, according to Minalyze.
Annelie Lundström, Chief Executive Officer of Minalyze AB, added: “We are excited to collaborate with the CASERM research team and look forward to helping build a strong future in Earth resource research at the Colorado School of Mines and Virginia Tech.”
Initial research using the new analytical capabilities will focus on the identification of elemental enrichment and depletion patterns around ore deposits that were caused by the interaction of ore-forming fluids with the host rocks during deposit formation, Minalyze explained. Identification of these vectors to ores requires the use of machine-learning techniques that are currently developed and tested by the CASERM research team.
In addition to data science, the research team is planning on conducting method developments involving the integration of additional sensors in the core scanner.
