A quantitative classification tool for porphyry Cu alteration systems
Creators: name: Dixon, Oliver James
Publisher: name: The University of St Andrews
Year: 2023
Subjects: Magnetic susceptibility
Machine learning
Magnetic remanence
Hyperspectral analysis
AMS
Porphyry
Hydrothermal alteration
Porphyry copper deposits
Copper
Clean energy transition
Infrared spectroscopy
Central Asian Orogenic Belt
Aktogay

description: Porphyry copper deposits form from upper crustal H₂O saturated magmatic systems, along ancient and active convergent margins. As the world’s major source of copper, gold and molybdenum, along with minor quantities of other base and precious metals, they are an increasingly important source of raw material, essential to the ongoing clean energy transition and in hitting key targets set by global societies today. Driven by the transition to renewable energy sources and electric vehicles, dependent on substantial quantities of iron ore, copper and aluminium, it has been estimated that the demand for copper alone will increase by over 20 times. With current reserves not expected to meet future demand, extensive additional exploration is anticipated. Furthermore, with the depletion of near-surface deposits in mature exploration terranes, there is a clear need for the development of new techniques to identify and unearth deeply buried reserves. Exploration programs rely on many techniques for the identification of the large, distal alteration systems associated with porphyry style mineralisation, which currently focus on pathfinding geochemical indicators. Alongside these techniques, infrared spectroscopy and magnetic susceptibility data is often collected but underutilised, despite providing a rapid and low-cost source of mineralogical information. Using samples collected form the Aktogay porphyry Cu deposit in the Central Asian Orogenic Belt, an extensive Palaeozoic subduction-accretion complex, the ability of magnetic and infrared spectroscopy to quantifiably define and characterise alteration, when used in conjunction with a specifically developed, unsupervised deep learning algorithm, has been examined. Our findings demonstrate that the resulting subdivision around porphyry mineralisation into twelve discrete domains, can uniquely identify otherwise hidden mineral assemblages. Through examining the consistency of key parameters within each domain such as ore grade and geochemical signatures, their statistical significance has been tested, indicating an increased consistency of between 60 and 70% when compared to traditional alteration zones classified by on-site mining geologists. In addition, detailed petrological and magnetic characterisation independently confirms the validity of the newly defined alteration domains, whilst also demonstrating the importance of magnetic characterisation as a new tool, which can improve our understanding and partial quantification of the magnetic effects during hydrothermal fluid-rock interactions throughout ore deposit formation. Quantifiably defining alteration domains provides a reproducible, rapid and non-destructive method that can be deployed in a wide variety of settings, leading to the consistent separation of domains which can be used as a superior proxy for ore distribution and grade.