多源地学大数据是当代矿产资源评价的主要数据源，然而，大多数矿产资源评价模型存在多源地学大数据处理的技术瓶颈。鉴于此，项目组拟把解决大规模目标优化问题性能优越的蝙蝠算法体系引入矿产资源评价研究领域，研制多源地学大数据矿产资源评价的蝙蝠算法体系模型。把矿产资源评价各环节的关键科学问题作为目标优化问题用矿产资源评价蝙蝠算法体系模型快速求解，实现两个示范区多源地学大数据找矿证据的自动提取，进而建立结合蝙蝠算法体系与最优路径森林的找矿证据组合优化矿产资源评价模型和蝙蝠算法体系优化极限学习机的矿产资源评价模型，完成两个示范区矿产资源评价研究工作，创建多源地学大数据矿产资源评价蝙蝠算法体系模型应用研究的典型范例。预期研究成果能够促进当代矿产资源评价理论与方法体系的发展，提高矿产资源评价过程的多源地学大数据处理效率和自动化水平，拓宽蝙蝠算法体系的应用研究领域，具有重要的理论和实际意义。

Multi-source big-geo-data are main data sources for modern mineral resource assessment. Unfortunately, most mineral resource assessment models have their technical bottlenecks in multi-source big-geo-data processing. For this reason, our project team plans to introduce bat algorithm system, which has a superior performance in solving large-scale objective optimization problems, to the field of mineral resource assessment to develop a bat algorithm system model for multi-source big-geo-data mineral resource assessment. As objective optimization problems, the key scientific problems of each step of the mineral resource assessment procedure are quickly solved using the bat algorithm system model of mineral resource assessment. Ore-finding evidences are extracted automatically from the multi-source big-geo-data of the two demonstration areas, the ore-finding evidence combination optimization models, which are a combination of the bat algorithm system and optimum-path forest, and the optimal extreme learning machines, the parameters of which are optimized by the bat algorithm system, are set up and the mineral resource assessments of the two demonstration areas are completed. Typical examples of applying the bat algorithm system model to multi-source big-geo-data mineral resource assessment are established in the two demonstration areas. The expected achievements can promote the development of theory and method system of modern mineral resource assessment and improve the effectiveness of multi-source big-geo-data processing and automation level of the mineral resource assessment procedure as well as widen application fields of the bat algorithm system. Therefore, our research has an important significance in both theory and practice.