海洋地质前沿2026，Vol.42Issue(4)：1-15,15.DOI:10.16028/j.1009-2722.2025.076
基于机器学习的深海热液循环系统研究进展
Advances in deep-sea hydrothermal circulation studies with machine learning
摘要
Abstract
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.关键词
深海热液循环系统/机器学习/分类模型/预测模型/图像识别Key words
deep-sea hydrothermal circulation system/machine learning/classification model/prediction model/image recognition分类
海洋科学引用本文复制引用
贺文霄,梁锦,陶春辉..基于机器学习的深海热液循环系统研究进展[J].海洋地质前沿,2026,42(4):1-15,15.基金项目
国家重点研发计划项目"深海硫化物资源移动式高效钻测技术与示范""深海硫化物资源评估方法研究与总体设计"(2023YFC28-11100,2023YFC2811101) （2023YFC28-11100,2023YFC2811101）
