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

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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.