Airborne geophysics provides one of the most relevant data for uranium exploration. However, the application of radiometric surveys is decreasing considerably as the depth of exploration is increasing. Notwithstanding, there is still a large potential for radiometric data, especially using recent data processing techniques such as machine learning methods. In this work, we propose a new method to detect uranium anomalies through regression using the Random Forest machine learning algorithm (RF). The RF regression allows combining airborne geophysical data to predict the expected uranium content, which represents the uranium content generated by environmental effects such as lithology and pedogenesis. Therefore, the deviation (Ud) between the measured uranium and the expected uranium represents the secondary effects such as weathering, soil alteration, hydrothermal alteration or mineralisation process. We evaluated the relevance of the geophysical parameters proposed by previous authors in the prediction of the expected uranium (thorium, thorium potassium ratio, uranium potassium ratio, and Total Gradient Amplitude). Randomly selecting only 10% of the database as training data, we estimate the expected uranium with an R 2 = 0.99 concerning the measured uranium. To assess the reliability of the Ud anomalies, we employed the proposed methodology in the Carajás Mineral Province (CMP), Brazil. In the CPM, the Ud anomalies showed a clear correlation with the several Iron Oxide Copper–Gold deposits (IOCG) and some IOCG-related and granite-related prospects. In situ measurements with a portable gamma-ray spectrometer in the Salobo mine supported the uranium anomalies. The Ud map also highlighted contrasts within granites that correlate with previously reported granitic facies. Therefore, the Ud map generated by RF regression is useful in setting exploration targets for conventional and unconventional uranium resources, as well as in high-detail granitic facies mapping.

中文翻译：

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通过随机森林回归检测铀异常

机载地球物理学为铀勘探提供了最相关的数据之一。然而，随着勘探深度的增加，辐射测量的应用正在大大减少。尽管如此，辐射数据仍有很大的潜力，尤其是使用最新的数据处理技术，如机器学习方法。在这项工作中，我们提出了一种使用随机森林机器学习算法 (RF) 通过回归检测铀异常的新方法。RF 回归允许结合机载地球物理数据来预测预期的铀含量，该含量代表由岩性和成土作用等环境影响产生的铀含量。因此，实测铀与预期铀之间的偏差（Ud）代表了诸如风化、土壤蚀变、热液蚀变或矿化过程。我们评估了之前作者提出的地球物理参数在预测铀（钍、钍钾比率、铀钾比率和总梯度幅度）中的相关性。仅随机选择数据库的 10% 作为训练数据，我们估计预期的铀，关于测量的铀的 R 2 = 0.99。为了评估 Ud 异常的可靠性，我们在巴西的 Carajás 矿产省 (CMP) 中采用了建议的方法。在 CPM 中，Ud 异常与几个氧化铁铜金矿床 (IOCG) 和一些与 IOCG 和花岗岩相关的远景显示出明显的相关性。在萨洛博矿使用便携式伽马射线光谱仪进行的原位测量支持了铀异常。Ud 地图还突出显示了与先前报道的花岗岩相相关的花岗岩内部的对比。因此，RF 回归生成的 Ud 图可用于为常规和非常规铀资源设定勘探目标，以及用于高细节花岗岩相绘图。