Data for: The application of machine learning methods to aggregate geochemistry predicts quarry source location: a case study from the Irish aggregate industry.

Attempting to classify the quarry sources which provided reactive rock aggregate, composed of Carboniferous aged pyritic mudrocks and limestones, to over 12, 500 homes across Ireland has not yet been possible using geochemical data. Using this dataset, a solution to this problem is found by applying machine learning models, such as logistic regression and random forest, to a geochemical dataset of scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) and Laser ablation-quadrupole-inductively coupled plasma mass spectrometry (LA-Q-ICPMS) of pyrite, and Isotope ratio mass spectrometry (IRMS) of bulk rock aggregate, to predict quarry source location.

目前，仅依靠常规地球化学数据，尚无法对为爱尔兰逾12500户家庭供应由石炭纪含黄铁矿泥岩与石灰岩构成的活性岩石骨料的采石场来源进行分类。本数据集通过将逻辑回归、随机森林等机器学习模型应用于涵盖黄铁矿扫描电子显微镜-能量色散X射线光谱法（SEM-EDS）、激光剥蚀-四极杆电感耦合等离子体质谱法（LA-Q-ICPMS）检测数据，以及全岩骨料同位素比值质谱法（IRMS）检测数据的地球化学数据集，成功解决了上述分类难题，实现采石场来源位置的精准预测。