基于机器学习的深海热液循环系统研究进展OA

Deep-sea hydrothermal circulation systems play a crucial role in understanding the Earth's material cycling and the evolution of extreme ecosystems.However,their unique environments have long constrained ef-fective observation and in-depth study.In recent years,the application of machine learning techniques in geosciences has been expanded,providing new perspectives for exploring deep-sea hydrothermal systems.This paper systematically reviews recent advances in this area,focusing on the classification for hydrothermal vent de-tection and basement lithology identification,prediction for polymetallic sulfide distribution,analysis of hydro-thermal biological habitat,and image recognition of subsea imagery,and ecosystem structure investigation.Addi-tional to significant achievements made,challenges in data scarcity,spatiotemporal sampling bias,limited model generalization,and poor interpretability remain.To address these issues,future research should focus on improv-ing deep-sea observation and data-sharing mechanisms,developing models specified for the complex deep-sea en-vironment,and promoting interdisciplinary integration to construct a multi-source,data-driven research frame-work.