Three-Dimensional Spatio-Temporal Slim Weighted Generative Adversarial Imputation Network: Spatio-Temporal Silm Weighted Generative Adversarial Imputation Net to Repair Missing Ocean Current Data

Abstract

Three-dimensional ocean observation is the foundation for accurately predicting ocean information. Although ocean observation sensor arrays can obtain internal data, their deployment is difficult, costly, and prone to component failures and environmental noise, resulting in discontinuous data. To address the severe missing data problem in three-dimensional ocean flow fields, this paper proposes an unsupervised model: Three-dimensional Spatio-Temporal Slim Weighted Generative Adversarial Imputation Network (3D-STA-SWGAIN). This method integrates spatio-temporal attention mechanisms and Wasserstein constraints. The generator captures the three-dimensional spatial distribution and vertical profile dynamic patterns through the spatio-temporal attention module, while the discriminator introduces gradient penalty constraints to prevent gradient vanishing. The generator strives to generate data that conforms to the real ocean flow field, and the discriminator attempts to identify pseudo-ocean current data samples. Through the adversarial training of the generator and the discriminator, high-quality completed data are generated. Additionally, a spatio-temporal continuity loss function is designed to ensure the physical rationality of the data. Experiments show that on the three-dimensional flow field dataset of the South China Sea, compared with methods such as GAIN, under a 50% random missing rate, this method reduces the error by 37.2%. It effectively solves the problem that traditional interpolation methods have difficulty handling non-uniform missing and spatio-temporal correlations and maintains the spatio-temporal continuity of the current field’s three-dimensional structure.

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