The Relationship between Fe Mineralization and Magnetic Basement Faults using Multifractal Modeling in the Esfordi and Behabad Areas(BMD), Central Iran

Multifractal modeling is a mathematical method for the separation of a high potential mineralized background from a non-mineralized background. The Concentration-Distance to Fault structures(C-DF) fractal model and the distribution of the known iron(Fe) deposits/mines seen in the Esfordi and Behabad 1:100,000 sheets from the Bafq region of central Iran are used to distinguish Fe mineralization based on their distance to magnetic basement structures and surface faults, separately, using airborne geophysical data and field surveys. Application of the C-DF fractal model for the classification of Fe mineralizations in the Esfordi and Behabad areas reveals that the main ones show a correlation with their distance from magnetic basement structures. Accordingly, the distances of Fe mineralizations with grades of Fe higher than 55%(43% < Fe ≤ 60%) are located at a distance of less than 1 km, whereas for surfacial faults with grades of 43% ≤ Fe ≤ 60%, the distances are 3162< DF ≤ 4365 m from the faults. Thus, there is a positive relationship between Fe mineralization and magnetic basement structures. Also, the proximity evidence of Precambrian high-grade Fe mineralization related to magnetic basement structures indicates syn-rifting tectonic events. Finally, this C-DF fractal model can be used for exploration of magmatic and hydrothermal ore deposits.

混合算法改善品位不确定露天矿生产调度问题的性能

露天采矿工艺是地表采矿的一种方法,通过开挖坑洞从地表向下开采矿石或废物。工业生产过程中,露天矿的长期生产调度(LTPS)问题是最大的生产难题之一,而基于确定性方法和不确定性的方法被认为是解决此类问题的主要策略。在过去几年中,许多研究人员充分探究了一种成本较低的新型计算法,即元启发式方法,用以解决矿山设计和生产调度问题。该方法尽管无法保证最终方案的最优性,但能够以相对较低的计算成本推算出足够优秀的解决方案。本文提出了增强拉格朗日松弛(ALR)与粒子群优化(PSO),以及ALR和蝙蝠算法(BA)的两种混合算法模型,以解决不确定品位条件下的露天矿生产调度问题。该混合模型采用ALR方法解决露天矿生产调度问题,以提高其计算性能并加快收敛速度,并通过PSO或BA更新拉格朗日系数。所提出的计算模型与ALR遗传算法、ALR传统次梯度法和常规方法(未使用拉格朗日方法)的计算结果进行了比较,结果表明:相比于常规方法,ALR法可以更加有效地解决大规模问题,并提出合理的解决方案。此外,混合算法可以降低计算时间和成本,ALR-BA方法的CPU运算时间比ALR-PSO方法大约高7.4%。