使用多维离子因果非线性算法模型识别矿井水源
Qiushuang Zheng1, Changfeng Wang2, Yang Yang2
1School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China. zqsbupt@163.com.
Scientific reports
|February 8, 2024
概括
研究人员开发了一种新的R-SVM模型,用于准确区分矿井水源. 这种方法有效地使用离子元件对水类进行分类,有助于防止和控制水损害.
科学领域:
- 水文地质学 水文地质学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 矿井水的涌入在地下采矿操作中构成重大风险.
- 准确识别水源类型对于有效的风险管理和预防策略至关重要.
- 现有的水源区分方法可能缺乏复杂水地质条件所需的精度.
研究的目的:
- 开发和验证一个强大的模型,根据离子组成区分矿井水源.
- 建立一种快速而准确的方法来识别不同类型的水.
- 提供一个工具,支持在采矿区内预防和控制水损害的决策.
主要方法:
- 应用非线性算法理论来建立R-SVM (弹性支持矢量机) 模型.
- 根据水样分析,选择六个关键的离子成分 (Na+,Ca2+,Mg2+,Cl-,SO42-,HCO3-) 作为区分因子.
- 使用SPSS统计和MATLAB进行模型开发和与训练和预测样本进行差别分析.
主要成果:
- R-SVM模型成功地将矿井水源分为四类:水,奥尔多维纪碳酸盐水和两种类型的砂岩裂纹水.
- 该模型在高州煤矿的案例研究中实现了高分类准确率90.90%.
- 开发的模型证明了矿井水源识别的强大适用性和区分能力.
结论:
- R-SVM模型提供了一种准确有效的方法来区分矿井水源.
- 这种方法为矿山工程中的水损害预防和控制提供了重要的指导价值.
- 该研究强调了数据驱动模型在解决矿业中复杂的水文地质挑战方面的潜力.
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