在采矿环境中,机器学习可以预测地下的物理性质和石灰岩状况.
A Balaguera1,2, M Torné3, R Carbonell3
1Geosciences Barcelona, GEO3BCN, CSIC, Lluís Solé i Sabarís, s/n, Barcelona, 08028, Spain. abalaguera@geo3bcn.csic.es.
Scientific reports
|July 21, 2025
概括
机器学习模型可以准确预测岩石的物理性质,并对石质位置进行分类,从而改善矿物勘探. 这增强了对脱碳能源挑战的地下地质特征.
科学领域:
- 地质科学 地质科学
- 矿产资源勘探 矿产资源勘探 矿产资源勘探
- 机器学习应用 机器学习应用
背景情况:
- 脱碳需要在矿产资源勘探和开采方面取得进展.
- 伊比利亚金字塔带是一个主要的金属产地,是地质研究的试验场所.
- 精确地描述岩石物理特性 (PPR) 和石质单位对于资源评估至关重要.
研究的目的:
- 开发岩石物理性质 (PPR) 的预测模型.
- 根据预测的PPR对石质学单位进行分类.
- 评估机器学习 (ML) 模型在地质表征中的有效性.
主要方法:
- 分析了1000多个地表岩石样本和来自Riotinto矿的6个钻孔.
- 使用机器学习 (ML) 模型进行PPR数据的质量控制.
- 应用传统的统计模型和先进的ML算法 (随机森林,XGBoost,k-NN,SVR) 进行预测和分类.
主要成果:
- 地质演化可以导致PPR在石质学中发生显著的重叠,从而挑战传统模型.
- ML模型在预测PPR和分类灯光局部方面达到80%以上的准确性.
- 证明了传统的统计模型在精确的岩石学预测方面的局限性.
结论:
- 机器学习模型为地下石质学表征提供了一种创新的方法.
- 这项研究重新定义了石灰岩位置的识别,提高了地质评估的准确性.
- 强调了机器学习在采矿和地质学中的潜力,通过整合各种数据源来进行3D表征.
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