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.
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Where do I Sample? Building Scientific Situational Awareness for Hydrothermal Plume Investigations with an Autonomous Underwater Vehicle
Deep-sea hydrothermal vents inject dissolved and particulate metals, dissolved gasses, and biological matter into the water column, creating plumes several hundred meters above the seafloor that can be traced thousands of kilometers. To understand the impact of these plumes, rosettes equipped with sample bottles and in situ instruments, e.g., for turbidity, oxidation-reduction potential, and temperature, have been key tools for collecting water column fluid for informative ex situ analysis. However, deploying rosettes strategically in distal (>1km) plume-derived fluids is difficult when plume material is entrained rapidly with background water and transported by complicated bathymetric, internal, and/or tidal currents. This problem is exacerbated when the controlling dynamics are also poorly constrained (e.g., no persistent monitoring, few historical data) and data collected while in the field to estimate or compensate for these dynamics are only available to be analyzed hours or days following an asset deployment. Autonomous underwater vehicles (AUVs) equipped with equivalent in situ instruments to rosettes excel at exploration missions and creating highly-resolved maps at different spatial scales. Utilization of AUVs for hydrothermal plume charting and strategic sampling with rosettes is at a techno-scientific frontier that requires new data transmission and visualization interfaces for supporting real-time evidence-based operational decisions made at sea. We formulated a method for monitoring in situ water properties while an AUV is underway that (1) builds situational awareness of deep fluid mass distributions, (2) allows scientists-in-the-loop to rapidly identify fluid distribution patterns that inform adaptations to AUV missions or deployments of other assets, like rosettes, for targeted sample collection, and (3) supports robust formulation of working hypotheses of plume dynamics for in-field investigation. We will present a description of the method with preliminary results from cruise AT50-15 (Juan de Fuca Ridge, 2023) using AUV Sentry and discuss how supervised autonomy will improve ocean robotics for future science missions.
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- Award ID(s):
- 1851007
- PAR ID:
- 10508945
- Publisher / Repository:
- Ocean Sciences
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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