Publication Date:
2022-10-20
Description:
© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Vrolijk, P., Summa, L., Ayton, B., Nomikou, P., Huepers, A., Kinnaman, F., Sylva, S., Valentine, D., & Camilli, R. Using a Ladder of Seeps with computer decision processes to explore for and evaluate cold seeps on the Costa Rica active margin. Frontiers in Earth Science, 9, (2021): 601019, https://doi.org/10.3389/feart.2021.601019.
Description:
Natural seeps occur at the seafloor as loci of fluid flow where the flux of chemical compounds into the ocean supports unique biologic communities and provides access to proxy samples of deep subsurface processes. Cold seeps accomplish this with minimal heat flux. While individual expertize is applied to locate seeps, such knowledge is nowhere consolidated in the literature, nor are there explicit approaches for identifying specific seep types to address discrete scientific questions. Moreover, autonomous exploration for seeps lacks any clear framework for efficient seep identification and classification. To address these shortcomings, we developed a Ladder of Seeps applied within new decision-assistance algorithms (Spock) to assist in seep exploration on the Costa Rica margin during the R/V Falkor 181210 cruise in December, 2018. This Ladder of Seeps [derived from analogous astrobiology criteria proposed by Neveu et al. (2018)] was used to help guide human and computer decision processes for ROV mission planning. The Ladder of Seeps provides a methodical query structure to identify what information is required to confirm a seep either: 1) supports seafloor life under extreme conditions, 2) supports that community with active seepage (possible fluid sample), or 3) taps fluids that reflect deep, subsurface geologic processes, but the top rung may be modified to address other scientific questions. Moreover, this framework allows us to identify higher likelihood seep targets based on existing incomplete or easily acquired data, including MBES (Multi-beam echo sounder) water column data. The Ladder of Seeps framework is based on information about the instruments used to collect seep information (e.g., are seeps detectable by the instrument with little chance of false positives?) and contextual criteria about the environment in which the data are collected (e.g., temporal variability of seep flux). Finally, the assembled data are considered in light of a Last-Resort interpretation, which is only satisfied once all other plausible data interpretations are excluded by observation. When coupled with decision-making algorithms that incorporate expert opinion with data acquired during the Costa Rica experiment, the Ladder of Seeps proved useful for identifying seeps with deep-sourced fluids, as evidenced by results of geochemistry analyses performed following the expedition.
Description:
Support for this research was provided through NASA PSTAR Grant #NNX16AL08G and National Science Foundation Navigating the New Arctic grant #1839063. Use of the R/V Falkor and ROV SuBastian were provided through a grant from the Schmidt Ocean Institute. The AUG Nemesis and the Aurora in-situ mass spectrometer was provided through in-kind support from Teledyne Webb Research and Navistry Corp, respectively.
Keywords:
Seep
;
Autonomous exploration
;
Costa Rica
;
Geochemistry
;
Water column data
;
Temporal variability
;
Decision-making algorithm
Repository Name:
Woods Hole Open Access Server
Type:
Article
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