Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps

Abstract

The Hellyer–Mt Charter region of western Tasmania includes three known and economically significant volcanic-hosted massive sulfide (VHMS) deposits. Thick vegetation and poor outcrop present a considerable challenge to ongoing detailed geological field mapping in this area. Numerous geophysical and soil geochemical datasets covering the Hellyer–Mt Charter region have been collected in recent years. These data provide a rich source of geological information that can assist in defining the spatial distribution of lithologies. The integration and analysis of many layers of data in order to derive meaningful geological interpretations is a non-trivial task; however, machine learning algorithms such as Random Forests and Self-Organising Maps offer geologists methods for indentifying patterns in high-dimensional (many layered) data. In this study, we validate an interpreted geological map of the Hellyer–Mt Charter region by employing Random Forests™ to classify geophysical and geochemical data into 21 discrete lithological units. Our comparison of Random Forests supervised classification predictions to the interpreted geological map highlights the efficacy of this algorithm to map complex geological terranes. Furthermore, Random Forests identifies new geological details regarding the spatial distributions of key lithologies within the economically important Que-Hellyer Volcanics (QHV). We then infer distinct but spatially contiguous sub-classes within footwall and hangingwall, basalts and andesites of the QHV using Self-Organising Maps, an unsupervised clustering algorithm. Insight into compositional variability within volcanic units is gained by visualising the spatial distributions of sub-classes and associated statistical distributions of key geochemical data. Compositional differences in volcanic units are interpreted to reflect contrasting primary composition and VHMS alteration styles. We conclude that combining supervised and unsupervised machine-learning algorithms provides a widely applicable, robust means, of analysing complex and disparate data for machine-assisted geological mapping in challenging terranes.

塔斯马尼亚岛西部的Hellyer-Mt Charter区包括三个已知的和具有显著经济意义的大量火山岩内硫化物（ ( VHMS ) ）矿床。茂密植被和不佳露头对该区持续的详细地质实地制图是一个相当大的挑战。最近几年收集了该区众多地球物理和土壤地球化学数据。这些数据是地质资料的丰富来源，可以协助确定岩性的空间分布。为了获得有意义的地质解释而对多层数据进行综合及分析是一件不平凡的任务;但是，机学算法如随机林(Random Forests) 和自组地图(Self-Organising Map) 给地质学家提供方法以识别高维（多层次）数据中的模式。在这项研究中，我们通过采用随机ForestsTM证实了该区解释过的地质地图以将地球物理学和地球化学数据分类为21个不同的岩性单元。我们将在随机林监督下的分类预测与解释过的地质图进行比较,突出了该算法用于复杂地质地体制图的有效性。此外，随机林可识别关于有重要经济意义的Que-Hellyer火山岩内关键岩性的空间分布的新地质细节。然后，我们采用自筹备地图(无监督的聚类算法))，推断出QHV中地板和顶壁、玄武岩和安山岩之内的不同但在空间上连续的子类。通过可视化的子类空间分布和关键地球化学数据相关统计分布,我们获得了对火山单元内的成分变异的认知。在火山单位组成的差异被解释为反映了主要成分和VHMS蚀变方式之间的反差。我们的结论是,结合监督和无监督机学算法,为在具有挑战性的岩层内进行机辅助地质填图提供了一个广泛适用的分析复杂且不同数据的方式。

KEYWORDS:

• geological mapping
• volcanic-hosted massive sulfide
• machine learning
• Random Forests
• Self-Organising Maps
• Tasmania

ACKNOWLEDGEMENTS

We thank Jocelyn McPhie for her constructive comments on a draft version of this manuscript. Airborne geophysics data were sourced from Mineral Resources Tasmania, Landsat ETM+data were sourced from the United States Geological Survey and soil geochemistry data were obtained from an Aberfoyle Resources Ltd legacy dataset. This research was conducted at the Australian Research Council Centre of Excellence in Ore Deposits (CODES) under Project No. P3A3A. M. Cracknell was supported through a University of Tasmania Elite Research PhD Scholarship. A. McNeill publishes with the permission of the Director of Mines, Mineral Resources Tasmania. Random Forests™ is a trademark of Leo Breiman and Adele Cutler. We thank Frank Bierlein and an anonymous reviewer for comments that improved the clarity of the manuscript and the language used to describe the methods and interpretations.