Abstract The Dolatabad chromite district, a well-endowed mineralized tract located in SE Iran, hosts numerous podiform chromite deposits. Its favorable geological setting and known chromite endowment suggest that the district has good potential for the discovery of additional chromite mineralization. However, the processes that controlled the development and preservation of the chromite ores are poorly known. As such, a better understanding of the ore-forming processes will be critical for the success of future exploration activities in the Dolatabad chromite district. Recognition of these critical genetic processes and the mapping of their spatial expressions are the precursors to mineral prospectivity mapping (MPM) as well as any mineral exploration activities on the ground. This study adopted a mineral systems approach to MPM, which entailed the formulation of a probabilistic framework to the recognition of the critical genetic processes and translation of these processes to mappable evidence maps. Here, we developed three proxies, representing trap, deposition, and preservation processes, that were subjected to a range of weighting procedures, including continuous (i.e., fuzzy gamma, geometric average, and data-driven index overlay) and data-driven machine-learning (i.e., multilayer perceptron neural network and random forests) methods. The machine-learning procedures outperformed the continuous procedures, suggesting that the former approaches are more reliable in targeting mineralized zones. The targets delineated by random forest algorithm, the superior model generated in this study, predicted c. 71% of the known chromite mineralization in c. 6% of the study area.

中文翻译：

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将矿物系统模型转化为连续和数据驱动的目标模型：以伊朗东南部多拉塔巴德铬铁矿区为例

摘要 Dolatabad 铬铁矿区是位于伊朗东南部的一个资源丰富的矿化区，拥有大量豆状铬铁矿矿床。其有利的地质环境和已知的铬铁矿资源表明该地区具有发现其他铬铁矿矿化的良好潜力。然而，控制铬铁矿开发和保存的过程却鲜为人知。因此，更好地了解成矿过程对于 Dolatabad 铬铁矿区未来勘探活动的成功至关重要。识别这些关键的遗传过程并绘制它们的空间表达图是矿产远景图 (MPM) 以及任何地面矿产勘探活动的先导。本研究采用矿物系统方法进行 MPM，这需要制定一个概率框架来识别关键的遗传过程并将这些过程转换为可映射的证据图。在这里，我们开发了三个代理，代表陷阱、沉积和保存过程，它们受到一系列加权程序的影响，包括连续（即模糊伽马、几何平均和数据驱动的指数叠加）和数据驱动的机器-学习（即多层感知器神经网络和随机森林）方法。机器学习程序优于连续程序，表明前一种方法在瞄准矿化带方面更可靠。本研究中生成的高级模型随机森林算法描绘的目标预测了 c。c. 71% 已知的铬铁矿矿化。6% 的研究区域。