深度学习和理论的综合框架,用于在异质含水层中增强高维透性场的识别
Mingxu Cao1, Zhenxue Dai2, Junjun Chen3
1College of Construction Engineering, Jilin University, Changchun, China; Institute of Intelligent Simulation and Early Warning for Subsurface Environment, Jilin University, Changchun, China.
Water research
|November 8, 2024
概括
使用整体MIMR优化的数据同化方法,提高了准确的地下水透率 (k) 现场估计. 这种方法增强了监测网络的设计,以更好地描述异质含水层.
科学领域:
- 水文地质学 水文地质学
- 地质统计学 在地质统计学
- 数据科学数据科学数据科学
背景情况:
- 准确估计高维透性 (k) 场对于地下水流和溶液运输模拟至关重要.
- 设计有效的监测网络,以在异质含水层中进行数据同化是具有挑战性的.
- 由于随机响应数据的随机性,现有的方法可能会错过关键信息.
研究的目的:
- 调查测量类型和监测策略对透性表征准确性的影响.
- 开发一个集体MIMR优化的方法,以改进数据同化.
- 加强对异质含水层中的k-field估计的理解和管理.
主要方法:
- 基于深度学习的替代模型与基于的最大信息最小冗余 (MIMR) 监控设计的整合.
- 开发一个整体MIMR优化的数据同化框架.
- 在十二种场景下,用日志-高斯透性场进行溶液运输的数值模拟.
主要成果:
- 与传统的MIMR方法相比,整体MIMR优化方法显著改善了透性场估计.
- 该研究证实了高预测准确度在前建模中对于可靠的反转的重要性.
- 不同的测量类型和监测策略对表征准确性产生了不同的影响.
结论:
- 拟议的整体MIMR优化方法为异质含水层中的k-field估计提供了更强大的方法.
- 有效的监控网络设计对于成功的数据同化和不确定性降低至关重要.
- 这些发现有助于为一般数据同化任务开发更强大的反转框架.
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