Underwater wireless optical communication (UWOC) is able to provide large bandwidth, low latency, and high security. However, there still exist bandwidth limitations in UWOC systems, with a lack of effective compensation methods. In this paper, we systematically study the bandwidth limitation due to the transceiver and underwater channel through experiments and simulations, respectively. Experimental results show that by using the 7-tap maximum likelihood sequence estimation (MLSE) detection, the maximum bitrate of the simple rectangular shape on–off-keying (OOK) signaling is increased from 2.4 Gb/s to 4 Gb/s over 1 GHz transceiver bandwidth, compared to the conventional symbol-by-symbol detection. For the bandwidth limitation caused by the underwater channel, we simulate the temporal dispersion in the UWOC by adopting a Monte Carlo method with a Fournier–Forand phase function. With MLSE adopted at the receiver, the maximum available bitrate is improved from 0.4 to 0.8 Gb/s in 12 m of harbor water at the threshold of hard-decision forward-error-correction (HD-FEC, 3.8 × 10⁻³). Moreover, when the bitrate for 0.4 Gb/s 12 m and 0.8 Gb/s 10 m OOK transmission remains unchanged, the power budget can be reduced from 33.8 dBm to 30 dBm and from 27.8 dBm to 23.6 dBm, respectively. The results of both experiments and simulations indicate that MLSE has great potential for improving the performance of bandwidth-limited communication systems.

With the development of ocean exploration technology and the rapid growth in the amount of marine science observation data, people are faced with a great challenge to identify valuable data from the massive ocean observation data. A recommendation system is an effective method to improve retrieval capabilities to help users obtain valuable data. The two most popular recommendation algorithms are collaborative filtering algorithms and content-based filtering algorithms, which may not work well for marine science observation data given the complexity of data attributes and lack of user information. In this study, an approach was proposed based on data similarity and data correlation. Data similarity was calculated by analyzing the subject, source, spatial, and temporal attributes to obtain the recommendation list. Then, data correlation was calculated based on the literature on marine science data and ranking of the recommendation list to obtain the re-rank recommendation list. The approach was tested by simulated datasets collected from multiple marine data sharing websites, and the result suggested that the proposed method exhibits better effectiveness.

An efficient and precise time-frequency analysis method for real-time ocean bottom seismometer (RTOBS) data in the South China Sea (SCS) is presented. Overcoming the limitations of conventional methods, the method involves temporal segmentation, unique frequency octaves, and Fourier transforms to generate power spectral density (PSD) and probability density function profiles. The method demonstrates superior precision, computational efficiency, and full-bandwidth (0 to Nyquist) capability compared to traditional techniques, as validated through theoretical and empirical evaluations. Applied to SCS RTOBS data, it unveils temporal PSD variations, shedding light on underwater noise sources like earthquakes, offshore blasting, ship-induced disturbances, and tidal effects. Establishing background noise levels in the SCS supports noise source categorization and ocean environment monitoring. Furthermore, comparing onshore and offshore seismic stations advances interdisciplinary research, fostering a comprehensive understanding of acoustics and seismology in the region.

The passive localization of dual targets composed of a surface ship and a submerged source located nearby beneath the ship is an intriguing problem. This study develops a passive localization method based on multipath arrival angles for dual targets, with similar source levels in the deep-ocean direct arrival zone, using a horizontal line array. Compared to the classical minimum variance distortionless response method, the sparse Bayesian learning method is used to improve resolution for multipath arrival angles under coherent signal conditions, enhancing both the effective range and localization accuracy. The effectiveness of the proposed method has been validated through simulation and experiment.

Antarctic ice-sheet sounding is critical for advancing insights into polar environmental systems. Seismic exploration constitutes an effective technique for Antarctic ice-sheet detection, as seismic waves exhibit substantial penetration depth and high resolution in homogeneous snow-ice media. Nevertheless, extreme low temperatures, harsh ambient conditions, and the heterogeneous geographic and environmental characteristics of the Antarctic region impose significant constraints on both detection efficiency and precision. To address these limitations, this paper proposes a seismic signal receiving system specifically engineered for the heterogeneous and harsh Antarctic environment. For detection efficiency, the system incorporates wired, wireless, and autonomous storage nodes to enable a distributed hybrid network, coupled with a power management module reducing the standby power from 7 W to 30 mW, and a wind-solar hybrid energy supply unit supporting operation down to -50◦C. To enhance precision, a neural network-based calibration method is utilized to mitigate temperature-induced drift in the analog-to-digital converter (ADC), achieving a calibration accuracy of 9.3 x 10−3 ppm. Additionally, high-precision acquisition and synchronization schemes are adopted, which realize a time synchronization precision of ±50 ns, input-referred noise of 0.20 μVrms@40 dB, and a dynamic range of 130 dB. Field validation experiments were performed near Taishan Station. The system successfully recorded signals from an artificial seismic source, based on which the ice thickness at the survey site was estimated to be 3.6 km; this estimation is highly consistent with data acquired via ground-penetrating radar (GPR). These findings verify the system’s performance and reliability, thereby laying a critical technological foundation for subsequent Antarctic ice-sheet investigations.

Seafloor observation network can perform all-weather, long-term, continuous, real-time, and in-situ observation of the ocean by combing various observation methods including cabled seafloor nodes, self-contained nodes, as well as mobile platforms, where reliable and long-term high-speed underwater wireless communication becomes an essential demand. Recently, underwater wireless optical communication (UWOC) has emerged as a highly promising solution and is rapidly becoming a research hotspot for meeting this requirement. In this article, we demonstrate the experiment and application of high-speed UWOC system for deep sea seafloor observation network. To the best of our knowledge this is the first long-term real-time deep-sea UWOC link with bitrate as high as 125 Mbps. Between 30 m distance and at a depth of 1650 m, two-way Ethernet UWOC links are realized with 125 Mbps direction-adjustable green light link and 6.25 Mbps non-line-of-sight (NLOS) blue light link. High quality video transmission of 8K 30 FPS and 4K 120 FPS are realized through high-speed 125 Mbps green light link, with 100% peak signal-to-noise ratio (PSNR) agreement, showing the capability of transmitting high-quality videos lossless. The 30-day long-term measurement results show that the BER performance of both 125 Mbps and 6.25 Mbps links is lower than 10-5, proving the stability and reliability of this UWOC system at depth of 1650 m. The maximum transmission distance for the green and blue light links are estimated to be 117.7 and 128.3 m with considering the geometry loss, which can be extended to 231.6 and 337.5 m without geometry loss. As the first long-term and real-time UWOC system in deep sea, we believe this demonstration can provide valuable experience for further UWOC studies and converged ocean observation networking with cabled and cable-less observation platforms.