This study intends to showcase the application of Extreme Gradient boosting (XGboost), a state-of-the-art ensemble-learning technique, for district-scale mineral potential mapping (MPM) of carbonate-hosted ZnPb mineral systems in the Emarat district, W Iran. Notwithstanding the demonstrated effectiveness of XGboost in addressing a range of classification and regression problems, the inadequate number of deposit locations employed as labeled samples, which is intrinsic to district-scale MPM, impedes the practical application of this method. Given this notion, an iterative window-based data augmentation technique was proposed and applied to generate additional labeled samples. The predictive models derived by XGboost were compared to those generated by random forests (RF). Both methods were applied to a set of mineral systems-derived exploration targeting criteria representing critical ore-forming processes. Setting aside the similarities of predictive models derived by both methods, it was recognized that RF is much less sensitive to insufficient labeled samples. It was also recognized that, unlike RF, many parameters require tuning for generating an optimum XGboost-based predictive model. Nevertheless, given the achieved results, XGboost slightly outperformed RF in generating an optimum predictive model. Therefore, XGboost coupled to data augmentation can be deemed a viable alternative for district-scale, data-driven MPM.

这项研究旨在展示极端梯度增强（XGboost）（一种最新的集成学习技术）在碳酸盐承载的Zn的区域规模矿产潜力测绘（MPM）中的应用。伊朗西部Emarat地区的Pb矿物系统。尽管已证明XGboost在解决一系列分类和回归问题方面具有有效性，但作为区域规模MPM固有的被用作标记样本的沉积位置数量不足，仍然阻碍了该方法的实际应用。鉴于此概念，提出了一种基于迭代窗口的数据增强技术，并将其应用于生成其他带标记的样本。将XGboost导出的预测模型与随机森林（RF）生成的模型进行了比较。两种方法均应用于代表关键成矿过程的一组矿物系统衍生的勘探目标标准。抛开这两种方法得出的预测模型的相似性，公认的是，RF对标记不足的样品不那么敏感。还认识到，与RF不同，许多参数需要调整以生成基于XGboost的最佳预测模型。尽管如此，在获得最佳结果的前提下，XGboost在生成最佳预测模型方面略胜于RF。因此，与数据增强耦合的XGboost可被视为区域规模的数据驱动MPM的可行替代方案。