Intelligent Quality Control of Ocean Buoy Profile Data Using a GRU-Mean Teacher Framework

Wenmiao Shao^1,2Chunlin Ning^1,3,4,5*Benjun Ma²Chao Li¹Huanyong Li^1,6Zihao Yao^1,7Lingkun Zeng^1,8

• ¹First Institute of Oceanography Ministry of Natural Resources, Qingdao, China
• ²Harbin Engineering University College of Underwater Acoustic Engineering, Harbin, China
• ³Harbin Engineering University, Harbin, China
• ⁴Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
• ⁵Shandong Key Laboratory of Marine Science and Numerical Modeling, Qingdao, China
• ⁶College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, China
• ⁷College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China, Harbin, China
• ⁸China University of Petroleum East China School of Marine and Space Information, Qingdao, China

To address the limitations in identifying complex anomaly patterns and the heavy reliance on manual labeling in traditional oceanographic data quality control (QC) processes, this study proposes an intelligent QC method that integrates Gated Recurrent Units (GRU) with a Mean Teacher–based semi-supervised learning framework. Unlike conventional deep learning approaches that require large amounts of high-quality labeled data, our model adopts an innovative training strategy that combines a small set of labeled samples with a large volume of unlabeled data. Leveraging consistency regularization and a teacher – student network architecture, the model effectively enhances its ability to learn anomalous features from unlabeled observations. The input incorporates multiple sources of information, including temperature, salinity, vertical gradients, depth one-hot encodings, and seasonal encodings. A bidirectional GRU combined with an attention mechanism enables precise extraction of profile structure features and accurate identification of anomalous observations. Validation on real-world profile datasets from the Bailong (BL01) moored buoy and Argo floats demonstrates that the proposed model achieves outstanding performance in detecting temperature and salinity anomalies, with ROC-AUC scores of 0.966 and 0.940, and precision–recall AUCs of 0.952 and 0.916, respectively. Manual verification shows over 90% consistency, indicating high sensitivity and robust generalization capability under challenging scenarios such as weak anomalies and structural profile shifts. Compared to existing fully supervised models, the proposed This is a provisional file, not the final typeset article semi-supervised QC framework exhibits superior practical value in terms of labeling efficiency, anomaly modeling capacity, and cross-platform adaptability.