使用深度学习算法评估来自GNSS和GRACE数据的水文负载位移
Changshou Wei1,2, Maosheng Zhou3,4, Zhixing Du1
1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific reports
|February 19, 2025
概括
一种新的3D卷积神经网络 (3D-CNN) 方法使用卫星和GNSS数据准确估计了水文负载位移. 这种先进的技术显著提高了对环境负载监测的传统方法的精度.
科学领域:
- 地质测量是指地质测量.
- 地球科学 地球科学 地球科学
- 机器学习 机器学习
背景情况:
- 陆地水储量变化导致地显著变形.
- 精确估计水文负载位移对于精确的地质观测和参考框架维护至关重要.
- 像负载格林的函数逆转等传统方法在精度上有局限性.
研究的目的:
- 引入一种新的3D卷积神经网络 (3D-CNN) 方法来估计水文负载位移.
- 为了比较3D-CNN方法与传统技术的精度.
- 分析云南省陆地水储和负载位移的时空特征.
主要方法:
- 利用来自全球导航卫星系统 (GNSS) 站的垂直位移时间序列数据.
- 来自重力恢复和气候实验 (GRACE) 卫星的陆地水储存的综合时空变化.
- 应用了一个3D卷积神经网络 (3D-CNN) 模型来估计位移.
主要成果:
- 3D-CNN方法的反转精度明显高于传统负载的格林函数反转.
- 观察到偏差显著减少:最大偏差减少1.34毫米,绝对最小偏差减少1.47毫米,绝对平均值减少79.6%,标准偏差减少31.4%.
- 分析显示,在陆地水储量和负载位移方面,占主导的年度和半年度周期占比超过90%的差异.
结论:
- 3D-CNN方法为估计陆地水负载位移 (TWLD) 提供了一种新且更精确的方法.
- TWLD表现出显著的空间异质性,与区域降水模式有很强的相关性.
- 集成的GRACE-GNSS TWLD模型为高精度的陆地储水逆转和地质测量应用提供了有价值的数据.
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