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混合地质统计和深度学习框架用于历史矿山尾矿的地化学表征
Keyumars Anvari1, Jörg Benndorf2, Gabriel Gerber3
1Faculty of Geosciences, Geoengineering and Mining, Institute of Mine Surveying and Geodesy, TU Bergakademie Freiberg, 09599, Freiberg, Germany.
Scientific reports
|October 7, 2025
概括
这项研究引入了一种混合地质统计-深度学习框架,用于模拟矿山尾矿中的地化学分布. 这种方法增强了资源回收和环境修复努力,以实现可持续采矿.
科学领域:
- 地质科学和环境科学 地球科学和环境科学
- 地球科学中的人工智能
背景情况:
- 对关键原材料 (CRM) 的需求不断增长,需要可持续的矿山尾矿管理.
- 矿山尾矿既为金属回收提供了机会,也因为危险物质而带来了环境风险.
研究的目的:
- 开发和验证一种新的混合地质统计-深度学习框架,用于模拟旧矿尾矿中的地化学分布.
- 提高在异质尾矿沉积物中的地化学预测的准确性和分辨率.
主要方法:
- 平常 kriging (OK) 与 1D 卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM) 模型的集成.
- 利用来自OK空间协差结构的特征来为深度学习模型提供信息.
- 混合框架应用于历史尾矿现场.
主要成果:
- 混合框架显著优于传统的地理统计方法.
- 在整个尾矿现场实现了高分辨率的地化学预测.
- 成功解释了尾矿沉积物中的空间异质性.
结论:
- 开发的框架适用于可持续的资源回收和矿山尾矿环境修复.
- 这种方法符合循环经济原则,用于管理人为沉积物.
- 突出了将地理统计学和深度学习整合为先进的尾矿特征的潜力.
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