整合组合数据分析 (CoDA) 和随机森林用于石质学特定的地质化学基线确定.
Federica Meloni1, Caterina Gozzi2, Jacopo Cabassi3
1Department of Earth Sciences, Via G. La Pira, 4, 50121, Firenze, Italy; CNR-IGG, Via G. La Pira, 4, 50121,, Firenze, Italy.
The Science of the total environment
|December 15, 2025
概括
本研究引入了一种结合数据分析和机器学习的新方法,用于确定复杂的采矿地区的精确地质化学基线. 这有助于区分自然土壤条件与污染,这对环境管理至关重要.
科学领域:
- 环境地质化学环境地质化学
- 应用地质学的应用地质学
- 数据科学在地球科学中的数据科学.
背景情况:
- 准确的地质化学基线对于区分天然土壤组成与采矿受影响地区的人为污染至关重要.
- 具有历史采矿历史的地质复杂地区对建立可靠的背景值构成挑战.
- 在具有长期工业影响和多样化地质构成的地区,现有的方法可能不足.
研究的目的:
- 开发和验证一种改进的方法来定义特定于石质学的地化学基线.
- 准确区分自然地质化学变化与土壤遗留污染.
- 支持复杂地质环境中的监管决策和整治策略.
主要方法:
- 将组合数据分析 (CoDa) 与随机森林算法集成,以进行可靠的岩石学分类.
- 对300多个地表和地下样本进行矿物学和微量元素 (Hg,As,Sb,Cr,Cu,Co,V,Ni) 的分析.
- 应用可靠的统计方法 (中位数±2MAD) 来计算基线值.
主要成果:
- 成功估计了Hg,As和Sb的地化学基线,在火山和沉积物领域展示了强大的岩石学控制.
- 确定了与地质环境相关的基线值的显著变化,有助于区分自然异常与污染.
- 观察到的基线值超过了意大利Hg和As的法定门,突出了需要特定地点的法规的需要.
结论:
- 拟议的工作流程有效地在复杂的,历史上开采的地区建立了石质学特定的地化学基线.
- 该方法提供了对不同地质领域的自然变异性和弹性的关键见解.
- 调查结果强调了定制监管限制的必要性,以及在类似受污染的地区更广泛应用的潜力.
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