Identifying the location of intrusions is a key component in exploration for porphyry Cu ± Mo ± Au deposits. In typical porphyry terrains, in the absence of outcrop, intrusions can be difficult to discriminate from the compositionally similar volcanic and volcanoclastic sedimentary rocks in which they are emplaced. The ability to produce lithological maps at an early exploration stage can significantly reduce costs by assisting in planning and prioritization of detailed mapping and sampling. Additionally, a data-driven strategy provides opportunity for the discovery of intrusions not identified during conventional mapping and interpretation. We used random forests (RF), a supervised machine-learning algorithm, to classify rock types throughout the Kliyul porphyry prospect in British Columbia, Canada. Rock types determined at geochemical sampling sites were used as training data. Airborne magnetic and radiometric data, geochemistry, and topographic data were used in classification. Results were validated using First Quantum Minerals’ geologic map, which includes additional detail from targeted location and transect mapping. The petrophysical and compositional similarity of rock types resulted in a noisy classification. Intrusions, particularly the more discrete, were inconsistently predicted, likely due to their limited extent relative to data sampling intervals. Closer examination of class membership probabilities (CMPs) identified locations where the probability of an intrusion being present was elevated significantly above the background. Indeed, a large proportion of mapped intrusions correspond to areas of elevated probability and, importantly, areas were highlighted as potential intrusions that were not identified in geologic mapping. The RF classification produced a reasonable lithological map, if lacking in resolution, but more significantly, great benefit comes from the insights drawn from the RF CMPs. Mapping the spatial distribution of elevated intrusion CMP, a soft classifier approach, produced a map product that can target intrusions and prioritize detailed mapping for mineral exploration.

中文翻译：

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使用随机森林通过机器学习识别不列颠哥伦比亚省火山地形中的侵入岩性：使用软分类器的价值

确定斑岩的位置是斑岩型Cu±Mo±Au矿床勘探的关键组成部分。在典型的斑岩地形中，在没有露头的情况下，很难将侵入岩与成分相似的火山岩和火山碎屑沉积岩区分开来。在勘探的早期阶段生成岩性图的能力可以通过协助计划和确定详细的制图和采样的优先次序来显着降低成本。此外，数据驱动策略为发现常规映射和解释过程中未发现的入侵提供了机会。我们使用随机森林（RF）（一种受监督的机器学习算法）对加拿大不列颠哥伦比亚省Kliyul斑岩远景中的岩石类型进行分类。在地球化学采样点确定的岩石类型用作训练数据。机载磁和辐射数据，地球化学和地形数据被用于分类。使用First Quantum Minerals的地质图对结果进行了验证，其中包括目标位置和断面图的其他详细信息。岩石的岩石物理和组成相似性导致了嘈杂的分类。不一致地预测了入侵，尤其是离散程度更高的入侵，这可能是由于入侵相对于数据采样间隔的程度有限所致。仔细检查班级成员资格（CMP），可以确定出现入侵的可能性大大高于背景的位置。确实，很大一部分映射的入侵对应于概率较高的区域，并且 重要的是，将区域突出显示为地质制图中未发现的潜在入侵。如果没有分辨率，RF分类会产生合理的岩性图，但更重要的是，从RF CMP中获得的见识会带来巨大的好处。绘制高架入侵CMP的空间分布图，这是一种软分类器方法，生成了一种地图产品，可以针对入侵进行定位，并优先为矿物勘探确定详细的映射。