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.

海底水热系统发出了影响海水化学的热，矿物质和微生物富集的液体，从气候变化到生物医学科学的一切都产生了影响。有史以来最详细，最全面的羽流调查之一将是东南太平洋上升的。这项研究巡游将表征沿中山脊长300多公里的弥漫性和聚焦的热液排放。基于声纳和光摩西数据解析功能，可井的大小以下，机器学习方法将检测并表征自动驾驶水下汽车声纳映射的热液通风孔。这将建立一个全面的通风烟囱和热液位点的清单，以及自大约24年前的最初研究以来，该系统的检查会发生变化。自动检测子米接地声纳数据可能自动检测可能的氢烟囱的工作流将增强所有深脂映射声纳用户的基础设施，因为寻找水热通风孔是这些仪器的常见用途。该研究将实施前端图形用户界面，该界面将为使用地理信息系统中的基本技能的任何人提供。为了使更广泛的社区提供海底映射的体验，将生产一个虚拟野外轨道，“狩猎深海水热通风孔”，并将学生和公众暴露于对地质和生物多样性的鲜为人知但非常关键的环境。 The project also will tr​​ain a PhD student contributing to the next generation of marine geoscientists.The central hypothesis of the research is that hydrothermal chimney distributions correlate with observeable 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.该项目计划在东部太平洋南部的热液排气场上收集声纳数据和光摩乳，从〜15°-18°S上升。这些数据将用于开发一种机器学习方法，以检测和表征来自子米尺度声纳图的热液通风孔。机器学习以找到水热通风孔的开发和应用将有助于在近24年的火山岩性形态以及山脊上的热液通风口田中检测变化，在该山脊上，岩浆复发间隔被认为约为7年。在Python实施的机器学习算法的用户友好界面将使非专家绘制来自测深数据的候选候选水热嵌合片，以帮助进行更有效的潜水计划和/或确定目标，以进一步调查，以进一步研究NSF的法定任务和审查范围，以评估良好的范围，这是通过评估的范围来弥补的。

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