Prediction model and exploration prospect analysis of phosphate mineral resources in China
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摘要:
研究目的 磷矿是具有战略意义的矿产资源，2014年至今，中国地质调查局组织编写的中国矿产地质志，全面描述了中国磷矿全貌及重要成矿规律，加强了对全国磷矿矿情的掌握。本文依托中国矿产地质志项目，系统总结了中国磷矿时空分布特征，圈定了磷矿找矿远景区和磷矿重点勘查区，对未来磷矿找矿工作有一定的参考价值。
研究方法 本文选取了具有代表性的矿床进行详细解剖工作，分别总结研究了沉积型、岩浆岩型和变质型磷矿的成矿条件和成矿模式，建立了典型矿床预测模型。根据预测模型及预测资源量规模（5000×104 t以上）等条件，把磷矿划分为3类成矿预测区。
研究结果 截至目前，中国共有磷矿产地1040处，查明资源储量231.07×108 t，分布在全国30个省（市、自治区）。根据中国磷矿成矿规律，将其划分为27个Ⅲ级成矿区带、4个成矿亚带和21个矿集区。确定了中国磷矿重要成矿预测区12个，预测潜在资源总量可达300.1×108 t。根据磷矿成矿预测成果，圈定了27个磷矿找矿远景区。根据磷矿找矿远景，结合成矿地质条件、矿床地质特征、资源量可靠程度，圈定了19个磷矿重点勘查区，预测资源量达236.83×108 t，可优先安排勘查工作。
结论 未来中国磷矿的找矿重点建议在云南东北、四川西南、贵州开阳、湖北神农架、河北承德、新疆库鲁克塔格等地区，找矿远景较大。
Abstract:This paper is the result of mineral exploration engineering.
Objective Phosphate deposit is a strategic mineral resource since 2014, the compilation of Annals of Geology of Mineral Resources of China has been organized by China Geological Survey, which is comprehensively described the overall picture of phosphate in China and important metallogenic regularities, and strengthened the grasp of phosphate situation in China. Based on the project of Annals of Geology of Mineral Resources of China, this paper systematically summarizes the spatial and temporal distribution characteristics of phosphate ore at the national level, and delineates the potential areas and key exploration areas of phosphate, which has certain reference value for phosphate ore exploration in China in the future.
Methods The metallogenic conditions and metallogenic models of sedimentary, magmatic and metamorphic phosphate deposits are summarized and studied respectively, and the prediction models of typical deposits are established. According to the prediction model and the forecast resources scale (5000×104 t or more), the phosphate deposit is divided into three types of metallogenic prediction areas.
Results Up to now, there are 1, 040 phosphate production areas in China (the identified resource reserves are 231.07×108 t) and distributed in 30 provinces (municipalities, autonomous regions). According to the ore-forming rules of phosphate in China, phosphate in China can be divided into 27 Grade Ⅲ metallogenic belts, 4 ore-forming sub-zones and 21 ore-concentrated areas. 12 important metallogenic prediction areas of phosphate deposit in China are determined, with the total predicted potential resources up to 300.1×108t. According to the prediction results of phosphate ore mineralization, 27 potential areas for phosphate exploration have been delineated in China. According to the exploration prospect of phosphate, combined with metallogenic geological conditions, deposit geological characteristics, ore dressing and smelting performance, and reliable degree of resources, 19 key exploration areas of phosphate ore were delineated, with the predicted resources of 236.83×108 t, and the exploration work can be arranged in priority.
Conclusions In the future, the exploration of phosphate ore in China should be focused on northeast Yunnan Province, southwest Sichuan Province, Kaiyang of Guizhou Province, Shennongjia of Hubei Province, Chengde of Hebei Province, Kuruktag and other areas in Xinjiang, with a great prospect of exploration.
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表 1 中国磷矿的含磷层位（据熊先孝等，2019）
Table 1. Phosphorus-bearing strata in China(after Xiong Xianxiao et al., 2019)
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表 2 中国磷矿成矿区带划分（据熊先孝等，2019）
Table 2. Classification of phosphate metallogenic zones in China (after Xiong Xianxiao et al., 2019)
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表 3 中国磷矿矿集区（据熊先孝等，2019）
Table 3. Phosphate ore concentration areas in China (after Xiong Xianxiao et al., 2019)
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表 4 湖北省荆襄磷矿预测模型（据熊先孝等，2019）
Table 4. Prediction model of Jingxiang phosphate deposit in Hubei Province (after Xiong Xianxiao et al., 2019)
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表 5 河北省矾山磷矿预测模型（据熊先孝等，2019）
Table 5. Prediction model of Fanshan phosphate deposit in Hebei Province(after Xiong Xianxiao et al., 2019)
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表 6 辽宁省建平县勿兰乌苏磷矿预测模型（据熊先孝等，2019）
Table 6. Prediction model of Belanwusu phosphate Deposit in Jianping County, Liaoning Province(after Xiong Xianxiao et al., 2019)
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表 7 中国磷矿找矿远景区一览（据熊先孝等，2019）
Table 7. List of phosphate exploration potential areas in China (after Xiong Xianxiao et al., 2019)
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