Mineral prospectivity mapping is crucial for identifying areas with economically valuable minerals. Therefore, several methods based on machine learning have been applied to predict the likelihood of mineral occurrences, especially deep learning (DL), which provides a flexible and precise approach to the use of continuous data. It allows the approximation of predictive variables with probability values related to new ore targets. However, in the early stages of mineral exploration, DL-based methods face a challenge related to class and sampling imbalance due to scarce mineral deposits, resulting in a lack of enough samples to train, limiting the model’s predictive ability. This work proposed a detailed and systematic framework to address imbalanced data issues with prior geological transfer learning and a weighted loss function. We exploited the abundant pixel information of input variables to develop a pretext geological classification and a feature data extraction task as an initializer for the trainable variables of the neural network. The proposed workflow was tested in a porphyry-rich Yukon (Canada) region and overperformed other state-of-the-art classification algorithms such as random forest, support vector machines, and logistic regression. Moreover, our results were contrasted against different geological reports, where our mineral prospectivity map was coherent with regional and local potential assessments of porphyry-type mineral occurrences. The quantitative metrics with a validation dataset suggested that the proposed method can effectively predict mineral prospective areas in different imbalanced data scenarios.

中文翻译：

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通过先验地质迁移学习使用不平衡数据进行斑岩型矿物前景图


矿物前景图谱对于识别具有经济价值矿物的区域至关重要。因此，已经应用了几种基于机器学习的方法来预测矿物出现的可能性，尤其是深度学习 （DL），它为使用连续数据提供了一种灵活而精确的方法。它允许使用与新矿石目标相关的概率值来近似预测变量。然而，在矿产勘探的早期阶段，由于矿床稀缺，基于 DL 的方法面临着与类别和采样不平衡相关的挑战，导致缺乏足够的样本进行训练，限制了模型的预测能力。这项工作提出了一个详细而系统的框架，以解决先前地质迁移学习和加权损失函数的不平衡数据问题。我们利用输入变量的丰富像素信息开发了一个前置地质分类和一个特征数据提取任务，作为神经网络可训练变量的初始化器。所提出的工作流程在斑岩丰富的育空地区（加拿大）进行了测试，其性能优于其他最先进的分类算法，例如随机森林、支持向量机和逻辑回归。此外，我们的结果与不同的地质报告进行了对比，在这些地质报告中，我们的矿物前景图与斑岩型矿物矿床的区域和地方潜在评估是一致的。具有验证数据集的定量指标表明，所提方法可以有效预测不同不平衡数据情景下的矿产远景区。