Publication Date:
2022-10-21
Description:
Seafloor hydrothermalism plays a critical role in fundamental interactions between geochemical and biological processes in the deep ocean. A significant number of hydrothermal vents are hypothesized to exist, but many of these remain undiscovered due in part to the difficulty of detecting hydrothermalism using standard sensors on rosettes towed in the water column or robotic platforms performing surveys. Here, we use in situ methane sensors to complement standard sensing technology for hydrothermalism discovery and compare sensing equipment on a towed rosette and autonomous underwater vehicle (AUV) during a 17 km long transect in the Northern Guaymas Basin. This transect spatially intersected with a known hydrothermally active venting site. These data show that methane signaled possible hydrothermal activity 1.5-3 km laterally (100-150m vertically) from a known vent. Methane as a signal for hydrothermalism performed similarly to standard turbidity sensors (plume detection 2.2-3.3 km from reference source), and more sensitively and clearly than temperature, salinity, and oxygen instruments which readily respond to physical mixing in background seawater. We additionally introduce change-point detection algorithms---streaming cross-correlation and regime identification---as a means of real-time hydrothermalism discovery and discuss related data monitoring technologies that could be used in planning, executing, and monitoring explorative surveys for hydrothermalism.
Description:
NSF OCE OTIC: #1842053
Woods Hole Oceanographic Institution: Innovative Technology Award
NOAA Ocean Exploration: #NA18OAR0110354
Schmidt Marine Technology Partners: #G-21-62431
NASA: #NNX17AB31G
NSF OCE: #0838107
Gordon and Betty Moore Foundation: #9208
NDSEG: Graduate Fellowship
MIT Martin Family Society of Fellows: Graduate Fellowship
Microsoft: Graduate Research Fellowship
DOE/National Nuclear Security Administration: #DE-NA000392
MIT EAPS: Houghton Fund
Keywords:
Methane
;
In situ instrumentation
;
Hydrothermalism
;
Deep sea exploration
;
Eater mass classification
;
Science-informed models
;
AUV SENTRY
;
Decision-making infrastructure
Repository Name:
Woods Hole Open Access Server
Type:
Dataset
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