Vertical Attention-Based Siamese ConvLSTM Network for Argo Data Error Detection

The international array for real-time geostrophic oceanography (Argo) project is committed to rapidly and precisely acquiring comprehensive 3-D data on ocean temperature and salinity, which is crucial for monitoring ocean climate change and natural phenomena. During the buoy observation, environmental factors, human mistakes, and equipment malfunctions can cause abnormalities such as density inversion and spike, and thus detecting the errors in Argo data is significant to ensure its reliability and applicability. Traditional methods mainly rely on the knowledge and judgment of marine experts, ensuring high accuracy but requiring large amounts of effort. Machine-learning methods are used for automatic Argo data error detection, while they still struggle with extracting deep and discriminative features from profiles. Recently, deep-learning methods have received increasing attention in this field, yet their effectiveness have not been widely explored, faced with challenges of imbalanced samples, joint detection, and complicated patterns. In this article, a novel vertical attention-based siamese ConvLSTM (VAS-CLSTM) network is proposed for the accurate error detection of Argo data. First, an oversampling approach with optimized deep clustering based on inheritance theory and Mahalanobis distance is designed to effectively augment the error samples. Second, a siamese convolutional long-short-term memory (ConvLSTM) network with contextual connection and spatial-temporal adjacent profile search is built to learn interactively from temperature and salinity profiles. Third, a depth-based vertical attention mechanism with grouped weights and vertical trends is proposed for adaptive modeling and flexible learning. Experimental results of North and South Atlantic datasets show that the proposed VAS-CLSTM method effectively improves the accuracy and reliability of error detection in Argo observation data.