X-band marine radar is an effective tool for sea wave remote sensing. Conventional physical-based methods for acquiring wave parameters from radar sea clutter images use three-dimensional Fourier transform and spectral analysis. They are limited by some assumptions, empirical formulas and the calibration process while obtaining the modulation transfer function (MTF) and signal-to-noise ratio (SNR). Therefore, further improvement of wave inversion accuracy by using the physical-based method presents a challenge. Inspired by the capability of convolutional neural networks (CNN) in image characteristic processing, a deep-learning inversion method based on deep CNN is proposed. No intermediate step or parameter is needed in the CNN-based method, therefore fewer errors are introduced. Wave parameter inversion models were constructed based on CNN to inverse the wave’s spectral peak period and significant wave height. In the present paper, the numerically simulated X-band radar image data were used for a numerical investigation of wave parameters. Results of the conventional spectral analysis and CNN-based methods were compared and the CNN-based method had a higher accuracy on the same data set. The influence of training strategy on CNN-based inversion models was studied to analyze the dependence of a deep-learning inversion model on training data. Additionally, the effects of target parameters on the inversion accuracy of CNN-based models was also studied.

Deterministic sea wave reconstruction techniques are critical for enhancing maritime safety and disaster warnings. Coherent radar remote sensing captures sea surface velocity information to enable more precise wave reconstruction. Existing difference matrix methods address rank-deficient systems through artificial boundary processing, which distorts local hydrodynamic characteristics and propagates errors to global features, thereby limiting the accuracy and stability of reconstructions. To resolve this limitation, this study proposes a physics-constrained deterministic wave reconstruction methodology. We introduce the Data-Anchored Projection model for the differential matrix, extracting hydrodynamic constraints directly from radar backscatter data. This approach achieves stable solutions for rank-deficient systems without artificial boundaries. The model’s performance was rigorously validated through both simulated and real-sea experiments. The simulation results demonstrate a minimum 13% accuracy improvement over conventional methods and high stability under various sea states and at different range resolutions. In a real-sea trial under sea states 3 to 5, reconstruction errors remained below 10%, with consistent stability observed across varying sea states.

Ocean waves are one of the leading environmental factors that cause motion of the ocean’s structure. Wave prediction is of great significance for the safety of marine structures. The deterministic sea wave prediction (DSWP) has been focused on because it provided an accurate temporal wave surface. The propagation velocity of wave components is one of the critical problems in DSWP. In this paper, the research of propagation velocity is focused on. The Taylor expansion to wave number is used to prove that the group velocity is the propagation velocity of wave components. The simulated irregular long crest wave data is generated. Utilizing the simulated data, the calculated wave surfaces based on group velocity are consistent with the simulated results. Meanwhile, the comparisons of calculated results based on the group velocity and phase velocity are given. Then, a tank experiment is set to verify the prediction results. To further investigate the prediction performance under different conditions, the influences of environmental factors, including the wind speed, water depth and sea state are analyzed in this paper.

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

Ensuring the safe operation of marine structures requires accurate phase-resolved wave prediction. However, current studies mostly focus on single-model verification and lack a systematic comparison of mainstream architectures under multiple environmental factors on a unified experimental benchmark, thus offering limited guidance for engineering practice. To fill this gap, we conducted a systematic wave-tank evaluation that quantifies how sea state levels, directional spectrum, prediction distance and lead time jointly affect model accuracy. Four architectures—RNN, LSTM, GRU, and TCN—were trained on 7 × 7 probe matrices acquired under sea states levels (4–7), two directional spreading coefficients (n = 2 and 6), five prediction distances (6.7–33.3 m), and lead times of 1–30Δt. Root-mean-square error (RMSE) served as the quantitative metric. Among recurrent architectures, RNN-WP achieved the lowest high-frequency error under mild sea states (SS4, RMSE = 0.28 m), LSTM-WP delivered the best overall accuracy (SS4–5, RMSE ≤ 0.37 m), and GRU-WP excelled in the medium to high frequency band (SS4–5, RMSE ≤ 0.31 m), whereas TCN-WP remained most robust at long horizons and severe sea states (SS7, RMSE = 0.42 m). Increasing sea-state severity raised RMSE by 40–90%, while a narrower directional distribution amplified errors under extreme conditions. Prediction distance and lead time altered model ranking, confirming that nonlinearity, directional spreading, distance and temporal horizon are the dominant controlling factors for deep learning phase resolved wave prediction.