Mapping Volcanic Facies for Critical Mineral Exploration Through Seismic Attributes and Machine Learning

Abstract Critical minerals (CM) are essential for emerging technologies; however, their exploration remains challenging because they occur in complex geological settings and often exhibit weak seismic expression. Volcanic facies (VF) are one of their sources of occurrence, where minerals such as lead, cobalt, copper, and nickel often accumulate and scatter due to hydrothermal processes. This study presents a facies-specific machine learning (ML) workflow that integrates a multi-attribute approach with supervised and unsupervised ML, aiming to resolve VF in a complex geologic setting in the Otway Basin, South Australia. Twenty-two seismic attributes were calculated from which facies-specific attribute combinations were selected. Attributes were then used as input into Self-Organizing Maps (SOM) and Independent Component Analysis (ICA) to find sills, lava flows, and dykes, while Probabilistic Neural Networks (PNN) was applied for supervised classification of pyroclastics, mounds, and laccoliths that were not identified using unsupervised methods. Results indicate that SOM better discriminates between sills and lava and effectively identifies dyke geometry. ICA improved the delineation of dyke boundaries and enhanced the contrast between the intrusions and the background medium, revealing features that were less distinct in SOM results. PNN successfully resolved complex features such as laccoliths. Our methodology demonstrates that specific attribute combinations processed through SOM, ICA, and PNN can effectively discriminate between closely related volcanic features that are structurally favorable for CM accumulation.