中国沿岸主要验潮站海图深度基准面的计算与分析

全球导航卫星系统干涉反射测量（global navigation satellite system-interferometric reflectometry,GNSS-IR）技术可利用信噪比（signal-to-noise ratio,SNR）数据包含的多路径信息反演潮位,但通常需限制仰角范围,导致可用数据量少以及时间分辨率不足。针对上述问题,提出一种数据质量控制方法,重构SNR残差序列获得仅受多路径影响的SNR序列,再设计并训练Transformer神经网络模型对数据进行分类,在潮位反演前筛除无效SNR数据,将高仰角数据纳入可用范围。实验表明,该方法可大幅度提升高仰角数据有效率,将反演站点的可用数据仰角范围扩展至5°~30°,从而显著提升可用数据量和潮位反演值的时间分辨率,对利用GNSS-IR技术的海啸、风暴潮实时监测等应用和长期海平面变化等海洋研究具有重要意义。

Computations and Analyses of Chart Datum to Coastal Tide Gauges of China

Global navigation satellite system-interferometric reflectometry (GNSS-IR) technology has become one of the effective methods to retrieve tide levels by utilizing multipath information in signal-to-noise ratio (SNR) data. However, it is usually necessary to limit the elevation range, resulting in less usable data and insufficient time resolution. To solve the above problems, a preprocessing data quality control method is proposed.First, the significant difference between usable and unusable data is obtained through comparative analysis. Based on this difference, a GNSS-IR data quality control method is proposed to classify SNR data and screen out unusable data by data reconstruction and building a Transformer model. Second, appropriate data sets are selected to verify and evaluate the effectiveness and generalization ability of the method.The results show that there are significant differences in amplitude characteristics between usable and unusable data, and the proportion of usable SNR data for 5°-30° elevation is significantly improved by the proposed method, thus extending the elevation range of usable data to 5°-30° at the inversion site.The proposed method significantly improves the amount of usable data and the time resolution of the GNSS-IR tide level retrieval method, which is of great significance for the application of GNSS-IR technology in real-time monitoring of tsunami and storm surge, as well as marine studies such as long-term sea level changes. Moreover, the method is a preprocessing data quality control method, which can identify and screen unusable data before tidal level retrieval, to break through the limitation that the post-processing method needs to accumulate SNR data for a long time. Therefore, it is better applicable to a scene where the tidal level changes dramatically in a short time, such as a tsunami.