A machine learning approach for regional geochemical data: Platinum-group element geochemistry vs geodynamic settings of the North Atlantic Igneous Province

• Main Authors: Jordan J. Lindsay, Hannah S.R. Hughes, Christopher M. Yeomans, Jens C.Ø. Andersen, Iain McDonald
• Format: Article
• Language: English
• Published: Elsevier 2021-05-01
• Series: Geoscience Frontiers
• Subjects: Platinum-group elements; Machine learning; Plume; Geochemistry; Metallogenesis; Mantle
• Online Access: http://www.sciencedirect.com/science/article/pii/S1674987120302309

Whilst traditional approaches to geochemistry provide valuable insights into magmatic processes such as melting and element fractionation, by considering entire regional data sets on an objective basis using machine learning algorithms (MLAs), we can highlight new facets within the broader data structure and significantly enhance previous geochemical interpretations. The platinum-group element (PGE) budget of lavas in the North Atlantic Igneous Province (NAIP) has been shown to vary systematically according to age, geographic location and geodynamic environment. Given the large multi-element geochemical data set available for the region, MLAs were employed to explore the magmatic controls on these shifting concentrations. The key advantage of using machine learning in analysis is its ability to cluster samples across multi-dimensional (i.e., multi-element) space. The NAIP data set is manipulated using Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbour Embedding (t-SNE) techniques to increase separability in the data alongside clustering using the k-means MLA. The new multi-element classification is compared to the original geographic classification to assess the performance of both approaches. The workflow provides a means for creating an objective high-dimensional investigation on a geochemical data set and particularly enhances the identification of metallogenic anomalies across the region. The techniques used highlight three distinct multi-element end-members which successfully capture the variability of the majority of elements included as input variables. These end-members are seen to fluctuate in prominence throughout the NAIP, which we propose reflects the changing geodynamic environment and melting source. Crucially, the variability of Pt and Pd are not reflected in MLA-based clustering trends, suggesting that they vary independently through controls not readily demonstrated by the NAIP major or trace element data structure (i.e., other proxies for magmatic differentiation). This data science approach thus highlights that PGE (here signalled by Pt/Pd ratio) may be used to identify otherwise localised or cryptic geochemical inputs from the subcontinental lithospheric mantle (SCLM) during the ascent of plume-derived magma, and thereby impact upon the resulting metallogenic basket.