Abstract Attempts using geochemical data to classify quarry sources which provided reactive rock aggregate, composed of Carboniferous aged pyritic mudrocks and limestones, which has caused structural damage to over 12, 500 homes across Ireland have not yet succeeded. In this paper, a possible solution to this problem is found by performing machine learning models, such as Logistic regression and Random Forest, upon a geochemical dataset obtained through the scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) and Laser ablation-quadrupole-inductively couple plasma mass spectrometry (LA-Q-ICPMS) of pyrite and Isotope ratio mass spectrometry (IRMS) of bulk rock aggregate, to predict quarry source location. When comparing the classification scores, the LA-Q-ICPMS dataset achieved the highest average classification score of 55.38% for Random Forest and 67.73% for Logistic regression based on 10-fold cross validation testing. As a result, this dataset was then used to classify a set of known unknown samples and achieved average classification accuracies of 40.30% for random forest and 66.80% for logistic regression, based on a systematic train-test procedure. There is scope to enhance these classification scores to an accuracy of 100% by combining the geochemical datasets together. However, due to the difficulty in linking pyrites analysed by SEM-EDS to those analysed by LA-Q-ICPMS, and relating a bulk rock analytical technique (IRMS) to mineral geochemistry (SEM-EDS, LA-Q-ICPMS), median values have to be used when combining IRMS (Fe, S) and SEM-EDS (TS and δ34S) datasets with LA-Q-ICPMS data. Therefore, if these combined datasets were used as part of an applied quarry classification system, statistically meaningful mean values taken from a near normally distributed dataset would have to be used in order to accurately represent the quarry composition.

中文翻译：

---

机器学习方法在聚合地球化学中的应用预测采石场源位置：以爱尔兰为例

摘要 尝试使用地球化学数据对提供反应性岩石骨料的采石场源进行分类的尝试尚未成功，这些石料由石炭纪陈年黄铁矿泥岩和石灰岩组成，已对爱尔兰 12, 500 多所房屋造成结构性破坏。在本文中，通过对通过扫描电子显微镜能量色散 X 射线光谱 (SEM-EDS) 和激光获得的地球化学数据集执行机器学习模型，例如逻辑回归和随机森林，找到了解决该问题的可能方法。黄铁矿的烧蚀-四极杆-电感耦合等离子体质谱 (LA-Q-ICPMS) 和散装岩石骨料的同位素比质谱 (IRMS)，以预测采石场源位置。在比较分类分数时，LA-Q-ICPMS 数据集的平均分类分数最高，为 55。基于 10 倍交叉验证测试，随机森林为 38%，逻辑回归为 67.73%。因此，该数据集随后用于对一组已知的未知样本进行分类，并基于系统的训练测试程序，随机森林的平均分类准确率为 40.30%，逻辑回归的平均分类准确率为 66.80%。通过将地球化学数据集组合在一起，可以将这些分类分数提高到 100% 的准确度。然而，由于难以将 SEM-EDS 分析的黄铁矿与 LA-Q-ICPMS 分析的黄铁矿联系起来，以及将大块岩石分析技术 (IRMS) 与矿物地球化学 (SEM-EDS、LA-Q-ICPMS) 联系起来，中值将 IRMS（Fe、S）和 SEM-EDS（TS 和 δ34S）数据集与 LA-Q-ICPMS 数据相结合时，必须使用这些值。所以，