Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer-Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps
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...
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| Veröffentlicht in: | Australian journal of earth sciences Jg. 61; H. 2; S. 287 - 304 |
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| Sprache: | Englisch |
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Taylor & Francis
2014
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| ISSN: | 0812-0099, 1440-0952 |
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| 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. |
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| AbstractList | 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. 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(TM) 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 &#x 7684; Hellyer-Mt Charter区 包 括 三 个 已 知 684; 和 具 有 显 著 经 济 意 义 的 大 量 火 山 岩 内 &#x 786b; 化 物 ( ( VHMS ) )矿 床 茂 密 植 被 和 不 &# x4f73; 露 头 对 该 区 持 续 ݨ 4; 详 细 地 质 实 地 制 图 &# x662f; 一 个 相 当 大 的 挑 ء 8; 最 近 几 年 收 集 了 该 &# x533a; 众 多 地 球 物 理 和 ձ f; 壤 地 球 化 学 数 据 这 &# x4e9b; 数 据 是 地 质 资 料 ݨ 4; 丰 富 来 源 ,可 以 协 助 & #x786e; 定 岩 性 的 空 间 分 ^ 03; 为 了 获 得 有 意 义 的 & #x5730; 质 解 释 而 对 多 层 e 70; 据 进 行 综 合 及 分 析 & #x662f; 一 件 不 平 凡 的 任 R a1; ; 但 是 ,机 学 算 法 如 随 ; 机 林 (Random Forests) 和 自 组 地 图 (Self-Organising Map)... |
| Author | Reading, A. M. McNeill, A. W. Cracknell, M. J. |
| Author_xml | – sequence: 1 givenname: M. J. surname: Cracknell fullname: Cracknell, M. J. email: M.J.Cracknell@utas.edu.au organization: CODES Centre of Excellence in Ore Deposits and School of Earth Sciences, University of Tasmania – sequence: 2 givenname: A. M. surname: Reading fullname: Reading, A. M. organization: CODES Centre of Excellence in Ore Deposits and School of Earth Sciences, University of Tasmania – sequence: 3 givenname: A. W. surname: McNeill fullname: McNeill, A. W. organization: Mineral Resources Tasmania, Department of Infrastructure, Energy and Resources |
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| SubjectTerms | Algorithms Economics Forests Geochemistry Geological mapping Geology machine learning Random Forests Self-Organising Maps Spatial distribution Tasmania volcanic-hosted massive sulfide |
| Title | Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer-Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps |
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