Advances in deep-sea hydrothermal circulation studies enabled by machine learning

Deep-sea hydrothermal circulation systems play a crucial role in understanding Earth’s material cycling and the evolution of extreme ecosystems. However, their unique environmental conditions have long constrained effective observation and in-depth study. In recent years, the application of machine learning techniques in geosciences has expanded, providing new perspectives for exploring deep-sea hydrothermal systems. This paper systematically reviews recent advances in this area, focusing on the use of classification models for hydrothermal vent detection and basement lithology identification, predictive models for polymetallic sulfide distribution and hydrothermal biological habitat analysis, and image recognition technologies for subsea imagery processing and ecosystem structure investigation. Despite significant progress, challenges such as data scarcity, spatiotemporal sampling bias, limited model generalization, and poor interpretability remain. To address these issues, future research should focus on improving deep-sea observation and data-sharing mechanisms, developing models tailored to the complex deep-sea environment, and promoting interdisciplinary integration to construct a multi-source, data-driven research framework.