海洋地质前沿2025，Vol.41Issue(9)：56-67,12.DOI:10.16028/j.1009-2722.2024.138
海底冷泉喷发状态智能识别分类研究
Intelligent identification and classification of seafloor cold seep eruption states:the Haima cold seep as an example
摘要
Abstract
Monitoring methane bubble plumes from seafloor cold seeps is crucial to marine research and resource exploration.Current cold seep research mainly uses remotely operated vehicles and autonomous underwater vehicles equipped with acoustic and optical devices for detection.However,issues of low accuracy remain in sub-sequent data analysis and image recognition,and deep learning and intelligent discrimination are urgent needs.Therefore,convolutional neural network(CNN)technology for the intelligent identification and classification of seafloor cold seep eruption states was introduced.By integrating high-quality video data obtained from marine surveys and manual identification,the bubble coverage index(BCI)was calculated.These data were used to estab-lish a calibration dataset for cold seep eruption images based on the ResNet model.In the application of the Haima cold seep research area in the Qiongdongnan Basin,the model reached a recognition accuracy of 98.9%through comparative analysis with calibration results.This study provided a new method for data analysis and image pro-cessing and a reliable support for on-site intelligent decision-making and in-situ sample collection at seafloor cold seeps.The new method could enhance the efficiency of automated cold seep eruption image processing,solve the issue of recognition of cold seep eruption state,and offer a basis for environmental assessment and energy explor-ation of cold seeps.关键词
冷泉/喷发状态/卷积神经网络/残差网络/智能识别Key words
cold seep/eruption state/convolutional neural network/residual network/intelligent recognition分类
海洋科学引用本文复制引用
邴震,陈宗恒,温明明,吕万军,陶军,叶俊聪,黄元铿..海底冷泉喷发状态智能识别分类研究[J].海洋地质前沿,2025,41(9):56-67,12.基金项目
国家重点研发计划"天然气水合物大深度浅表层三维广角探测技术"(2023YFC2808700) （2023YFC2808700）
广东省重点领域研发计划"海底冷泉原位智能监测/探测站研制"(2020B1111510001) （2020B1111510001）
