从现场测量的悬浮沉积物度概况中分离横向向导的组成部分
Zixi Zhao1, Shaotong Zhang2, Jinran Wu3
1Frontiers Science Center for Deep Ocean Multispheres and Earth System; Key Lab of Submarine Geosciences and Prospecting Techniques, MOE; State Key Laboratory of Physical Oceanography; College of Marine Geosciences, Ocean University of China, Qingdao, 266100, China.
Water research
|September 26, 2025
概括
本研究引入了一种数据驱动的动态模式分解 (DMD) 方法,以分离沉积物运输中的水平向导效应,改进细粒度海岸的模型. 该方法准确地重建悬浮沉积物度概况,为沉积物动力学提供了新的见解.
科学领域:
- 沿海的地形和沉积物动态.
- 环境流体力学环境流体力学
- 数据驱动的建模和分析.
背景情况:
- 经典的一维垂直扩散-定位 (1DV) 模型对于细粒度沉积物是有限的,因为它无法考虑水平向导.
- 现有的分离向导效应的方法通常是不切实际的,依赖于昂贵的实验技术或复杂的物理模型.
- 需要有效和实用的方法来分析具有显著水平传输的环境中的沉积物动态.
研究的目的:
- 引入一种新的数据驱动方法,使用动态模式分解 (DMD) 来在沉积物运输分析中分离水平向导元件.
- 克服1DV模型的局限性,通过使细粒度沉积物动态在泥海岸的分析.
- 为理解复杂的沉积物运输过程提供一个计算效率高和实用的方法.
主要方法:
- 在测量悬浮沉积物度 (SSC) 档案中应用分层动态模式分解 (DMD) 方法.
- 三个不同的组件的重建:风暴诱导的垂直混合/背景度 (Profile I),潮再悬浮 (Profile II) 和M2潮转移 (Profile III).
- 在整个研究期内分析每个组件的相对变异贡献率 (RVCR),包括风暴和风暴后阶段.
主要成果:
- 在风暴事件期间,Profile I占主导地位 (峰值98.7%的RVCR),而风暴消散后Profile II占主导地位 (最大70.9%的RVCR),控制细粒度SSC.
- 表 III,代表横向向导,在风暴期间的贡献较低 (<2% RVCR),但在风暴后增加 (最大7.8% RVCR).
- 重建的SSC总概况实现了高精度,其中83.7%的RMSE值低于0.3g/L,验证了DMD方法的有效性.
结论:
- 数据驱动的DMD方法成功地将水平向导组件与观察到的SSC配置文件分开,为传统方法提供了切实可行的替代方案.
- 这种方法为将1DV模型的适用性扩展到泥海岸和复杂的沉积环境提供了关键的技术支持.
- 该研究提出了一种新的,有效的方法来分析沉积物运输动态,特别是区分垂直混合,再悬浮和引流的贡献.
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