The increased attention given to batteries has given rise to apprehensions regarding their availability; they have thus been categorized as essential commodities. Cobalt (Co), copper (Cu), lithium (Li), nickel (Ni), and molybdenum (Mo) are frequently selected as the primary metallic elements in lithium-ion batteries. The principal aim of this study was to develop a computational algorithm that integrates geostatistical methods and machine learning techniques to assess the resources of critical battery elements within a copper porphyry deposit. By employing a hierarchical/stepwise cosimulation methodology, the algorithm detailed in this research paper successfully represents both soft and hard boundaries in the simulation results. The methodology is evaluated using several global and local statistical studies. The findings indicate that the proposed algorithm outperforms the conventional approach in estimating these five elements, specifically when utilizing a stepwise estimation strategy known as cascade modeling. The proposed algorithm is also validated against true values by using a jackknife method, and it is shown that the method is precise and unbiased in the prediction of critical battery elements.

对电池的日益关注引起了对其可用性的担忧；因此，它们被归类为必需品。钴（Co）、铜（Cu）、锂（Li）、镍（Ni）和钼（Mo）经常被选为锂离子电池中的主要金属元素。本研究的主要目的是开发一种计算算法，该算法集成了地质统计学方法和机器学习技术，以评估铜斑岩矿床内关键电池元素的资源。通过采用分层/逐步协同仿真方法，本研究论文中详细介绍的算法成功地表示了仿真结果中的软边界和硬边界。该方法通过多项全球和本地统计研究进行评估。研究结果表明，所提出的算法在估计这五个元素方面优于传统方法，特别是在使用称为级联建模的逐步估计策略时。所提出的算法还通过使用折刀法对真实值进行了验证，结果表明该方法在关键电池元件的预测方面是精确且无偏的。