Evaluating In-Situ Measurements of Hydrothermal Plume Tracers for Autonomous Exploration and Sampling. Branch, A., Preston, V., Lien, R., Xu, G., Burkitt-Gray, M., & German, C. R. In Ocean Sciences Meeting Abstract 2024, New Orleans, Louisiana, February, 2024. ![Evaluating In-Situ Measurements of Hydrothermal Plume Tracers for Autonomous Exploration and Sampling [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex The process of seeking, sampling, and characterizing deep hydrothermal systems is benefited by the use of autonomous underwater vehicles (AUVs) equipped with in situ sensors. Traditional AUV operations require multiple deployments with manual data analysis by ship-board scientists. Development of advanced autonomous methods that analyze in situ data in real-time and allow the vehicle itself to make decisions would improve the efficiency of operations and enable new frontiers in exploration at hydrothermal systems on Ocean Worlds. Adaptive robotic decision making is facilitated by computational models of hydrothermal systems and selected in situ sensors used to refine and validate these predictions. Improving autonomous missions requires better models, and thus an understanding of how different sensors respond to hydrothermally altered seawater. During cruise AT50-15 (Juan De Fuca Ridge, 2023), we performed surveys of the hydrothermal plumes at the Endeavour Segment with AUV Sentry to investigate the utility of in situ sensors measuring tracers such as oxidation-reduction potential, optical backscatter, methane abundance, conductivity, and temperature, for building working models of plume dynamics. We investigated length scales of under 1 km to 5 km with a focus on reoccupying locations over varying time scales. Persistent deep current data were available through the Ocean Networks Canada mooring array. Using these datasets, we investigate two questions: (1) how reliably and at what length scales can real-time current information be used to predict the location and source of a hydrothermal plume? (2) How does the relative age (hence, biogeochemical maturation) of the hydrothermal plume fluid affect the response of different in situ sensors? These results will be used to inform the development of autonomous plume detection algorithms that use real-time, in situ data with the purpose of improving AUV exploration of hydrothermal plumes on Earth and other Ocean Worlds.
@inproceedings{branch-osm2024,
address = {New Orleans, Louisiana},
author = {Andrew Branch and Victoria Preston and Rudi Lien and Guangyu Xu and Mary Burkitt-Gray and Christopher R. German},
booktitle = {Ocean Sciences Meeting Abstract 2024},
month = {February},
project = {ice\_covered\_oceans},
title = {Evaluating In-Situ Measurements of Hydrothermal Plume Tracers for Autonomous Exploration and Sampling},
year = {2024},
abstract = {The process of seeking, sampling, and characterizing deep hydrothermal systems is benefited by the use of autonomous underwater vehicles (AUVs) equipped with in situ sensors. Traditional AUV operations require multiple deployments with manual data analysis by ship-board scientists. Development of advanced autonomous methods that analyze in situ data in real-time and allow the vehicle itself to make decisions would improve the efficiency of operations and enable new frontiers in exploration at hydrothermal systems on Ocean Worlds. Adaptive robotic decision making is facilitated by computational models of hydrothermal systems and selected in situ sensors used to refine and validate these predictions. Improving autonomous missions requires better models, and thus an understanding of how different sensors respond to hydrothermally altered seawater. During cruise AT50-15 (Juan De Fuca Ridge, 2023), we performed surveys of the hydrothermal plumes at the Endeavour Segment with AUV Sentry to investigate the utility of in situ sensors measuring tracers such as oxidation-reduction potential, optical backscatter, methane abundance, conductivity, and temperature, for building working models of plume dynamics. We investigated length scales of under 1 km to 5 km with a focus on reoccupying locations over varying time scales. Persistent deep current data were available through the Ocean Networks Canada mooring array. Using these datasets, we investigate two questions: (1) how reliably and at what length scales can real-time current information be used to predict the location and source of a hydrothermal plume? (2) How does the relative age (hence, biogeochemical maturation) of the hydrothermal plume fluid affect the response of different in situ sensors? These results will be used to inform the development of autonomous plume detection algorithms that use real-time, in situ data with the purpose of improving AUV exploration of hydrothermal plumes on Earth and other Ocean Worlds.},
url = {https://ai.jpl.nasa.gov/public/documents/papers/branch-osm2024.pdf},
}
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Traditional AUV operations require multiple deployments with manual data analysis by ship-board scientists. Development of advanced autonomous methods that analyze in situ data in real-time and allow the vehicle itself to make decisions would improve the efficiency of operations and enable new frontiers in exploration at hydrothermal systems on Ocean Worlds. Adaptive robotic decision making is facilitated by computational models of hydrothermal systems and selected in situ sensors used to refine and validate these predictions. Improving autonomous missions requires better models, and thus an understanding of how different sensors respond to hydrothermally altered seawater. 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