预测和实际应用的石矿化在武dao地区,贵州,中国
Shiqiang Yang1, Wu Yang2, Tao Cui2
1106 Geological Party, Guizhou Bureau of Geology and Mineral Exploration and Development, ZunYi, China.
PloS one
|July 31, 2024
概括
由于浅层沉积物减少,中国武道区的矿产预测具有挑战性. 这项研究成功地采用机器学习来识别高潜力区域,从而发现了峰矿藏.
科学领域:
- 地质科学 地质科学
- 经济地质学 地质学
- 数据科学在地球科学中的数据科学.
背景情况:
- 武道区是一个全球重要的采矿地区,拥有新明,大唐和卢龙等主要石矿床.
- 由于浅层资源的枯竭,发现新的矿床越来越困难,需要先进的预测方法.
研究的目的:
- 开发和评估武道区有效的金属学预测方法.
- 通过使用数据驱动的方法,确定石勘探的高潜力区域.
主要方法:
- 利用证据权重和机器学习算法来进行矿产勘探.
- 使用混矩阵,ROC曲线和预测效率曲线评估预测模型性能.
主要成果:
- 机器学习模型准确地预测了沉积点的位置,其中95.9%属于高潜力区域,占地面积的10%.
- 展望地图强调了东部地区和南十字域的粘土石和碳酸盐岩石作为主要目标.
- 随后的勘探导致发现了峰岩矿 (1.82米厚,61.24%Al2O3,2895万).
结论:
- 机器学习和证据权重是复杂地质环境中矿物预测的有效工具.
- 这项研究证实了在武道区发现新石资源的潜力.
- 成功发现峰泉矿床证明了先进地质预测技术的实际应用.
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