Finding hydrothermal chimneys along the southern East Pacific Rise with machine learning approaches to AUV-based sonar data

利用基于 AUV 的声纳数据的机器学习方法寻找东太平洋海隆南部沿线的热液烟囱

• 批准号: 2006265
• 负责人: Gene Yogodzinski
• 金额: $ 21.44万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006265&HistoricalAwards=false
• 关键词: Finding; hydrothermal; chimneys; along; southern

Seafloor hydrothermal systems emit hot, mineral- and microbial-rich fluids that impact seawater chemistry, with ramifications for everything from climate change to biomedical sciences. One of the most detailed and comprehensive plume surveys ever will be conducted on the South-East Pacific Rise. This research cruise will characterize both diffuse and focused hydrothermal discharge along a 300+ km length of the mid-ocean ridge. Based on sonar and photomosaic data resolving features to well under a meter in size, a machine learning method will detect and characterize hydrothermal vents from autonomous underwater vehicle sonar mapping. This will establish a comprehensive inventory of vent chimneys and hydrothermal sites, as well as examine changes in this system since its initial study about 24 years ago. Workflows for automated detection of possible hydrothermal chimneys from sub-meter gridded sonar data would enhance the infrastructure for all users of deep-submergence mapping sonars since searching for hydrothermal vents is a common use of these instruments. The study will implement a front-end Graphic User Interface that will be made available to anyone with basic skills in Geographic Information Systems to use. In order to make the experience of seafloor mapping available to an even broader community, a virtual fieldtrip, “Hunting Deep-Sea Hydrothermal Vents”, will be produced and expose students and the public to a rarely observed yet very critical environment for both geology and biodiversity. The project also will train a PhD student contributing to the next generation of marine geoscientists.The central hypothesis of the research is that hydrothermal chimney distributions correlate with observable geologic features, and that those correlations will provide important insights into the causes of variations in hydrothermal flux, longevity, and chimney distribution from the ridge segment to global scales. This project plans to collect sonar data and photomosaics over hydrothermal vent fields along the Southern East Pacific Rise from ~15°-18°S. These data would be used to develop a machine-learning method to detect and characterize hydrothermal vents from sub-meter scale sonar maps. The development and application of machine learning to find hydrothermal vents will assist in detecting changes after almost 24 years in both in volcanic-tectonic morphology as well as hydrothermal vent fields at a ridge where the magmatic recurrence interval is thought to be approximately 7 years long. A user-friendly interface to the machine learning algorithm, implemented in Python, would enable non-experts to map candidate hydrothermal chimneys from bathymetric data to assist in more efficient dive planning and/or identification of targets for further investigation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

海底热液系统释放出富含矿物质和微生物的热流体，影响海水化学，对从气候变化到生物医学科学的一切都有影响。将对东南太平洋海隆进行有史以来最详细和最全面的羽流调查。这次考察航行将描述沿沿着300多公里长的洋中脊扩散和集中热液排放的特征。基于声纳和摄影测量数据将特征解析到一米以下，机器学习方法将通过自主水下航行器声纳映射来检测和表征热液喷口。这将建立一个喷口烟囱和热液地点的全面清单，并审查该系统自大约24年前开始研究以来的变化。根据亚米级网格声纳数据自动探测可能的热液烟囱的工作流程将加强深潜测绘声纳所有用户的基础设施，因为寻找热液喷口是这些仪器的常见用途。这项研究将实施一个前端图形用户界面，将提供给任何人在地理信息系统的基本技能使用。为了向更广泛的群体提供海底测绘经验，将制作一次虚拟实地考察“寻找深海热液喷口”，让学生和公众了解一个很少观察到但对地质学和生物多样性都非常重要的环境。该项目还将培养一名博士生，为下一代海洋地球科学家做出贡献。该研究的中心假设是，热液烟囱分布与可观察到的地质特征相关，这些相关性将为了解从海脊段到全球尺度的热液流量、寿命和烟囱分布变化的原因提供重要见解。本项目计划收集东太平洋海隆南部沿着约15°-18 ° S热液喷口区的声纳数据和照片镶嵌图。这些数据将用于开发一种机器学习方法，以便从亚米级声纳图中探测和确定热液喷口的特征。开发和应用机器学习来寻找热液喷口，将有助于检测火山构造形态和海脊热液喷口区近24年后的变化，据认为海脊的岩浆复发间隔约为7年。机器学习算法的用户友好界面，在Python中实现，将使非专家能够根据测深数据绘制候选热液烟囱，以帮助更有效的潜水规划和/或确定进一步调查的目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Chimney Identification Tool for Automated Detection of Hydrothermal Chimneys from High-Resolution Bathymetry Using Machine Learning

烟囱识别工具，用于利用机器学习通过高分辨率测深自动检测热液烟囱

• DOI: 10.3390/geosciences12040176
• 发表时间: 2022
• 期刊: Geosciences
• 影响因子: 2.7
• 作者: Keohane, Isaac;White, Scott
• 通讯作者: White, Scott