
Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(5), P. 869 - 869
Published: April 27, 2025
Ocean temperature data is fundamental to the study of ocean dynamics and climate change, its efficient compression storage are critical for large-scale analysis transmission. However, traditional methods based on Fourier transform struggle balance ratio fidelity when confronted with complex characteristics marine environments. This proposes a convolutional attention autoencoder (CAAE) compress reconstruct three-dimensional fields evaluates performance across different depths ratios. The experimental results indicate that although reconstruction error slightly increases higher ratios, proposed model achieves near-perfect environmental data, performing robustly various spatial locations. work offers viable solution accurate provides valuable insights management datasets.
Language: Английский