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.

海底热液系统排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的各个领域产生影响，有史以来最详细、最全面的羽流调查之一将在东南太平洋隆起进行。这次研究巡航将根据声纳和马赛克数据解析大洋中脊 300 多公里长的扩散和集中热液排放特征，机器学习方法将通过自动水下航行器声纳测绘来检测和表征热液喷口，这将建立热液喷口和热液地点的全面清单，并检查该系统自大约 24 年前首次研究以来的变化。从亚米级网格声纳数据中检测可能的热液烟囱将增强所有深潜测绘声纳用户的基础设施，因为寻找热液喷口是这些仪器的常见用途。实施一个前端图形用户界面，任何具有地理信息系统基本技能的人都可以使用该界面，以便让更广泛的社区能够获得海底测绘的经验，虚拟实地考察“深海狩猎”。热液喷口”，将让学生和公众接触到一个罕见但对地质学和生物多样性来说非常关键的环境。该项目还将培训一名为下一代海洋地球科学家做出贡献的博士生。该研究的中心假设。是热液吗烟囱分布与可观测的地质特征相关，这些相关性将为了解从山脊部分到全球尺度的热液通量、寿命和烟囱分布的变化原因提供重要见解。这些数据将用于开发一种机器学习方法，以从亚米级声纳地图中检测和表征热液喷口。机器学习的开发和应用以寻找热液喷口将有助于检测近 24 年后火山构造形态以及山脊热液喷口场的变化，其中岩浆复发间隔被认为约为 7 年长。用 Python 实现的机器学习算法的用户友好界面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计划和/或识别目标该奖项反映了 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