结合机器学习和波形变换的集成工作流,用于对异质地下水系统的自动表征
Musaab A A Mohammed1,2,3, Norbert P Szabó4, Abdelrhim Eltijani5
1Faculty of Earth and Environmental Sciences and Engineering, University of Miskolc, Miskolc, Egyetemváros, 3515, Hungary. musab20501@gmail.com.
Scientific reports
|February 10, 2025
概括
这项研究提出了一种自动机器学习方法,以改善地下水含水层的表征. 该方法准确地绘制了石质学和水层学图,有助于有效地管理地下水.
科学领域:
- 水文地质学 水文地质学
- 地质物理学 地质物理学
- 机器学习 机器学习
背景情况:
- 准确的含水层表征对于地下水管理至关重要,但传统方法昂贵且耗时.
- 地理学井日志提供了一个有效的替代方案,但手动解释可能具有挑战性和模两可.
- 对于大规模的水文地质研究,需要对井日志进行自动解释.
研究的目的:
- 开发和验证一个自动化的机器学习方法,用于增强含水层的特征.
- 提高岩石学和水文平面绘图的准确性和效率.
- 为了创建地下水流和污染物运输建模的3D石质模型.
主要方法:
- 使用封闭的反复单元 (GRU) 神经网络计算缺失的深度电阻日志.
- 自组织地图 (SOMs) 的应用用于石质单位分布映射.
- 使用连续波形变形分析精细化水文平面图边界.
主要成果:
- GRU网络有效地归算了缺失的日志数据,提高了整体数据质量.
- SOMs成功地划分了三个主要的岩石学单位 (页岩,页岩沙,沙子和石).
- 综合方法生成了一个3D岩石学模型,其中有四个水层地理区域,与透性和页岩体积相关.
结论:
- 自动化方法为含水层表征提供了强大而高效的方法.
- 3D石质模型支持地下水流和污染物运输建模.
- 这种方法可以扩展到其他地区,以改善含水层的管理和开发.
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