Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia
Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia
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DOI：
10.1190/geo2017-0590.1
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发表时间：
2018-03
期刊：
影响因子：
3.3
通讯作者：
S. Kuhn;M. Cracknell;A. Reading
中科院分区：
文献类型：
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作者：
S. Kuhn;M. Cracknell;A. Reading
西澳大利亚东部金矿区是世界上主要的黄金产区之一；然而，大面积的预期基岩被覆盖并且缺乏详细的岩性测绘。远离矿区附近的环境，勘探新的金矿前景需要使用有限的可用数据和对不确定性的可靠估计来绘制地质图。我们使用机器学习算法随机森林 (RF) 对历史上重要的 Junction 金矿附近的未勘探区域的岩性进行分类，使用地球物理和遥感数据，在这个勘察阶段没有可用的地球化学采样。使用占总地面面积 1.6% 的稀疏训练样本，我们生成了精细的岩性图。尽管包括了后来侵入的部分研究区域和可变的覆盖深度，但分类是稳定的，并且它保留了训练数据中定义的地层单元。我们使用信息熵评估与这种新的 RF 分类相关的不确定性，识别精炼地图中最有可能被错误分类的区域。我们发现信息熵与不准确性密切相关，为勘探者提供了一种机制，将未来的支出引导到最有可能被错误绘制或地质复杂的地区。我们的结论是，当面对有限的数据时，该方法可以成为绿地、造山金矿环境中的地球科学家可用的有效附加工具。我们确定该方法可以用于大幅改进现有地图，或者以稀疏观测为起点生成新地图。它可以在类似的情况下实施（露头信息有限且没有地球化学数据），作为传统解释的客观、数据驱动的替代方案，并具有量化不确定性的附加价值。
The Eastern Goldfields of Western Australia is one of the world’s premier gold-producing regions; however, large areas of prospective bedrock are under cover and lack detailed lithologic mapping. Away from the near-mine environment, exploration for new gold prospects requires mapping geology using the limited data available with robust estimates of uncertainty. We used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remote-sensing data, with no geochemical sampling available at this reconnaissance stage. Using a sparse training sample, 1.6% of the total ground area, we produce a refined lithologic map. The classification is stable, despite including parts of the study area with later intrusions and variable cover depth, and it preserves the stratigraphic units defined in the training data. We assess the uncertainty associated with this new RF classification using information entropy, identifying those areas of the refined map that are most likely to be incorrectly classified. We find that information entropy correlates well with inaccuracy, providing a mechanism for explorers to direct future expenditure toward areas most likely to be incorrectly mapped or geologically complex. We conclude that the method can be an effective additional tool available to geoscientists in a greenfield, orogenic gold setting when confronted with limited data. We determine that the method could be used either to substantially improve an existing map, or produce a new map, taking sparse observations as a starting point. It can be implemented in similar situations (with limited outcrop information and no geochemical data) as an objective, data-driven alternative to conventional interpretation with the additional value of quantifying uncertainty.