Using a Ladder of Seeps With Computer Decision Processes to Explore for and Evaluate Cold Seeps on the Costa Rica Active Margin

Abstract

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.

Towards Automated Sample Collection and Return in Extreme Underwater Environments

“…The sites, which are known to host oases of chemosynthetic communities associated with hydrothermal and seafloor hydrocarbon seeps, were chosen as NASA TRL-6 demonstration locations for analog astrobiology exploration missions. These campaigns utilized a sequentially nested survey method with a coordinated team of heterogeneous robotic platforms that relied on automated planing tools to rapidly synthesize vehicle missions in response to newly acquired information (Vrolijk et al, 2021;Ayton et al, 2019). To better approximate an analog space flight mission scenario, surface ships operated as orbiters, conducting multibeam sonar bathymetric mapping of the Pacific (Vrolijk et al, 2019) and Aegean (Nomikou et al, 2019) campaign sites, with coverage areas of 2000 km 2 at 30 m resolution and 48 km 2 at 10 m resolution, respectively.…”

“…To better approximate an analog space flight mission scenario, surface ships operated as orbiters, conducting multibeam sonar bathymetric mapping of the Pacific (Vrolijk et al, 2019) and Aegean (Nomikou et al, 2019) campaign sites, with coverage areas of 2000 km 2 at 30 m resolution and 48 km 2 at 10 m resolution, respectively. These maps informed the mission planning for autonomous underwater gliders (AUG), which acted as long-range in-situ reconnaissance drones, conceptually similar to NASA's Ingenuity and Dragonfly vehicles, conducting reconnaissance missions of between 1 and 500 km in length at standoff distances to within 15 m of ocean floor obstacles in order to identify potential areas of scientific interest (Vrolijk et al, 2021;Duguid and Camilli, 2020). Automated AUG mission planning considered resource (e.g., time and power) and risk constraints (Timmons et al, 2016(Timmons et al, , 2019, and adaptively replanned missions based on inferred sites of scientific interest that correlated with the presence of active ocean floor hydrocarbon cold seeps and hydrothermal vents.…”

“…Automated AUG mission planning considered resource (e.g., time and power) and risk constraints (Timmons et al, 2016(Timmons et al, , 2019, and adaptively replanned missions based on inferred sites of scientific interest that correlated with the presence of active ocean floor hydrocarbon cold seeps and hydrothermal vents. Using information gained by the surface ship sonar and AUG missions, the automated planning process then generated viable mission sequences that the ROV used to investigate areas of highest estimated information gain (Vrolijk et al, 2021). During these missions, the ROV acted as a lander, outfitted with a manipulator for automated sample collection and return.…”

Query-driven adaptive sampling

“…Characterization of Natural Hydrocarbon Seeps As described by the example scenario earlier in this chapter, characterization of natural hydrocarbon seeps required adaptive sampling to be performed in a domain with extremely limited prior knowledge, but incorporating expert knowledge given by geologists. Adaptive sampling and environment modeling approaches described in this thesis were used to search for hydrocarbon seeps in the Costa Rica active margin in December 2018 [144], supported by the Schmidt Ocean Institute, NASA PSTAR, and Navistry Corp. A structured model was constructed to capture geologists qualitative understanding of the relationships between bathymetric features, acoustic backscatter signals, water column acoustic anomalies, and the presence of seepage. The quantitative strengths of these signals was then learned from data under the asserted qualitative model.…”

“…Examples include confirming high temperature measurements [10], observing sources of hydrocarbon seepage [144], and observing algal blooms [35]. The choice of whether to estimate a variable accurately or seek high value observations depends on the intentions of the mission designer, and we will allow both within query-driven adaptive sampling.…”