The Seismological Society of America will present its highest honor, the 2026 Harry Fielding Reid Medal, to Peter Shearer, professor of geophysics at Scripps Institution of Oceanography, University of California, San Diego.

Where does hydrogen in the deep sea come from? An international team led by the University of Bremen addressed this question and discovered an unexpected process occurring beneath the sparsely studied hydrothermal fields at extremely slow-spreading mid-ocean ridges that could play an important role. Particularly at sites where liquids circulate through sediments. Samples from the Jøtul Hydrothermal Field off Norway were analyzed for the study. The findings have been published in the professional journal Communications Earth & Environment.

Published today (Jan. 5) in Nature Geoscience, the findings suggest that this high point on the northwest section of the ice sheet is highly sensitive to the relatively mild temperatures of the Holocene, the interglacial period that began 11,000 years ago and continues today. 

Digital twin (DT) technology is emerging as a core solution for future marine development and intelligent ocean management. The review systematically reviews digital twin applications in the marine field, clarifies its concept, proposes a five-layer framework, and summarizes key technologies, including sensing, data management, modeling, simulation, and monitoring. It highlights DT’s ability to synchronize physical marine systems with virtual models in real time, enabling simulation, prediction, optimization, and decision-making. The authors further outline challenges and development prospects, showing how DT can support deep-sea resource exploitation, offshore wind energy, marine engineering, vessel autonomy, environmental monitoring, and system reliability assessment.

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to underwater cables—and is critical for safe renewable energy development. Traditional numerical simulations and experiments require enormous computational resources, yet often fail to capture multiscale turbulence and long-term system behavior. This review highlights how machine learning (ML) is emerging as a powerful solution for analyzing, predicting, and even controlling FSI systems. Key progress spans feature detection, reduced-order modeling, physics-informed neural networks, and reinforcement-based flow control. By leveraging data-driven models to extract hidden patterns and reconstruct flow fields, ML shows promise in improving efficiency, predictive accuracy, and automated control across ocean engineering applications, positioning itself as a transformative tool for next-generation design.