[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125944-en":3,"doc-seo-125944-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125944,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Research on seamount substrate classification method based on machine learning","The study targets the difficulty of identifying and evaluating deep-sea substrates in Western Pacific seamount areas. It builds a substrate classification point set using in-situ video footage from the Jiaolong manned deep submersible and shipborne deep-water multibeam data. An mRMR-XGBoost substrate classification model is proposed to exploit acoustic and optical information. Classification experiments and box sampling results reach 92.5% accuracy, outperforming commonly used machine learning models, with the best performance for nodule and gravel substrates.","TYPE Original Research PUBLISHED 06 August 2024  \nDOI 10.3389/fmars.2024.1431688  \nOPEN ACCESS  \nEDITED BY  \nZifeng Zhan,  \nChinese Academy of Sciences (CAS), China  \nREVIEWED BY  \nNitin Agarwala,  \nCentre for Joint Warfare Studies, India Gaoxue Yang,  \nChang ’an University, China  \n*CORRESPONDENCE YongFu Sun  \n [sunyongfu@ndsc.org.cn](sunyongfu@ndsc.org.cn)  \nRECEIVED 12 May 2024  \nACCEPTED 19 July 2024  \nPUBLISHED 06 August 2024  \nCITATION  \nHuang D, Sun Y, Gao W, Xu W, Wang W, Zhang Y and Wang L (2024) Research on seamount substrate classification method based on machine learning.  \nFront. Mar. Sci. 11:1431688 .  \ndoi: 10.3389/fmars.2024.1431688  \nCOPYRIGHT  \n© 2024 Huang, Sun, Gao, Xu, Wang, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nResearch on seamount substrate classification method based on machine learning  \nDeXiang Huang 1,2, YongFu Sun 1*, Wei Gao 1, WeiKun Xu 1, Wei Wang 1,3, YiXin Zhang 1,3 and Lei Wang 2  \n1 Investigation Department, National Deep Sea Center (NDSC), Qingdao, China, 2College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, China, 3Qingdao Innovation Development Base, Harbin Engineering University, Qingdao, China  \nThe western Pacific seamount area is abundant in both biological and mineral resources, making it a crucial location for international investigation of regional seabed resources . An essential stage in comprehending and advancing seamounts is gaining knowledge about the distribution characteristics and laws governing the seabed substrate. Deep-sea geological sampling is challenging because of the intricate nature of the deep-sea environment, resulting in increased difficulty in identifying and evaluating substrates. This study addresses the aforementioned issues by utilizing in-situ video footage obtained from the “Jiaolong” manned deep submersible and shipborne deep-water multibeam data. This data is used as a foundation for constructing a Western Pacific seamount areas substrate classification point set. Additionally, the paper introduces the m RMR-XGBoost substrate classification model. Substrate categorization in deep sea and mountainous regions has been successfully accomplished, yielding a classification accuracy of 92 . 5% . The classification experiments and box sampling results demonstrate that the mRMR-XGBoost substrate classification model proposed in this paper can efficiently use acoustic and optical data to accurately divide the substrate types in seamount areas, with better classification accuracy, when compared with commonly used machine learning models. It has a significant application value and the best classification effect on the two types of substrates: nodules and gravel substrates.  \nKEYWORDS  \nCaiwei seamount, substrate classification, machine learning, feature selection, mRMR-XGBoost  \n1 Introduction  \nSeamounts, also known as seabed mountains, usually refer to seabed uplifts that are distributed in the deep sea below sea level and are greater than 1000m in height. They are morphologically divided into flat-topped seamounts and pointed-topped seamounts (Ganet al., 2021) . Seamounts not only contain rich polymetallic mineral resources, but also rich  \nFrontiers in Marine Science 01 [frontiersin.org](frontiersin.org)  \nbiological resources, and are a typical ecosystem. The distribution of organisms on seamounts varies vertically, forming a rich variety of habitat types. Its complex topographical and geological features provide a unique habitat environment for marine organisms (Mayer et al., 2018; Victorero et al., 2018) . S","cbCaihtBNmPR5Dig","https://ap.wps.com/l/cbCaihtBNmPR5Dig","pdf",8368346,5,1,16,"English","en",105,"# Introduction\n## Seamounts and substrate research background\n## Challenges of deep-sea substrate acquisition\n## Deep-sea detection technologies and data sources","[{\"question\":\"What data sources are used to classify seamount substrates?\",\"answer\":\"The method uses in-situ video footage from the Jiaolong manned deep submersible and shipborne deep-water multibeam acoustic data to construct the substrate classification point set.\"},{\"question\":\"What is the proposed classification model in this study?\",\"answer\":\"The paper introduces the mRMR-XGBoost substrate classification model, combining feature selection (mRMR) with XGBoost for substrate categorization.\"},{\"question\":\"How accurate is the proposed approach and how does it compare with other models?\",\"answer\":\"Experiments and box sampling achieve 92.5% classification accuracy, and results indicate better performance than commonly used machine learning models, especially for nodules and gravel substrates.\"}]","Research on seamount substrate classification method based on machine learning | 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data sources are used to classify seamount substrates?","Question",{"text":77,"@type":78},"The method uses in-situ video footage from the Jiaolong manned deep submersible and shipborne deep-water multibeam acoustic data to construct the substrate classification point set.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the proposed classification model in this study?",{"text":82,"@type":78},"The paper introduces the mRMR-XGBoost substrate classification model, combining feature selection (mRMR) with XGBoost for substrate categorization.",{"name":84,"@type":75,"acceptedAnswer":85},"How accurate is the proposed approach and how does it compare with other models?",{"text":86,"@type":78},"Experiments and box sampling achieve 92.5% classification accuracy, and results indicate better performance than commonly used machine learning models, especially for nodules and gravel 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