ALBERT

Description: Submitted in partial fulfillment of the requirements for the degree of Master of Science in Aeronautics and Astronautics at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.

Description: In the environmental and earth sciences, hypotheses about transient phenomena have been universally investigated by collecting physical sample materials and performing ex situ analysis. Although the gold standard, logistical challenges limit the overall efficacy: the number of samples are limited to what can be stored and transported, human experts must be able to safely access or directly observe the target site, and time in the field and subsequently the laboratory, increases overall campaign expense. As a result, the temporal detail and spatial diversity in the samples may fail to capture insightful structure of the phenomenon of interest. The development of in situ instrumentation allows for near real-time analysis of physical phenomenon through observational strategies (e.g., optical), and in combination with unmanned mobile platforms, has considerably impacted field operations in the sciences. In practice, mobile platforms are either remotely operated or perform guided, supervised autonomous missions specified as navigation between humanselected waypoints. Missions like these are useful for gaining insight about a particular target site, but can be sample-sparse in scientifically valuable regions, particularly in complex or transient distributions. A skilled human expert and pilot can dynamically adjust mission trajectories based on sensor information. Encoding their insight onto a vehicle to enable adaptive sampling behaviors can broadly increase the utility of mobile platforms in the sciences. This thesis presents three field campaigns conducted with a human-piloted marine surface vehicle, the ChemYak, to study the greenhouse gases methane (CH4) and carbon dioxide (CO2) in estuaries, rivers, and the open ocean. These studies illustrate the utility of mobile surface platforms for environmental research, and highlight key challenges of studying transient phenomenon. This thesis then formalizes the maximum seek-and-sample (MSS) adaptive sampling problem, which requires a mobile vehicle to efficiently find and densely sample from the most scientifically valuable region in an a priori unknown, dynamic environment. The PLUMES algorithm — Plume Localization under Uncertainty using Maximum-ValuE information and Search—is subsequently presented, which addresses the MSS problem and overcomes key technical challenges with planning in natural environments. Theoretical performance guarantees are derived for PLUMES, and empirical performance is demonstrated against canonical uniform search and state-of-the-art baselines in simulation and field trials. Ultimately, this thesis examines the challenges of autonomous informative sampling in the environmental and earth sciences. In order to create useful systems that perform diverse scientific objectives in natural environments, approaches from robotics planning, field design, Bayesian optimization, machine learning, and the sciences must be drawn together. PLUMES captures the breadth and depth required to solve a specific objective within adaptive sampling, and this work as a whole highlights the potential for mobile technologies to perform intelligent autonomous science in the future.

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

Description: The Hunting Bubbles Cruise took place in August-September 2018 on the R/V Falkor (cruise ID 180824). Ship time was provided by the Schmidt Ocean Institute. This cruise investigated transport of methane from seeps located on the Cascadia Margin. Data archived at the WHOAS repository supplements additional data from this cruise available at the R2R rolling deck to repository and at MGDS: Marine Geoscience Data System.

Description: Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 47 (2020): e2020GL087669, doi:10.1029/2020GL087669.

Description: We present a year‐round time series of dissolved methane (CH4), along with targeted observations during ice melt of CH4 and carbon dioxide (CO2) in a river and estuary adjacent to Cambridge Bay, Nunavut, Canada. During the freshet, CH4 concentrations in the river and ice‐covered estuary were up to 240,000% saturation and 19,000% saturation, respectively, but quickly dropped by 〉100‐fold following ice melt. Observations with a robotic kayak revealed that river‐derived CH4 and CO2 were transported to the estuary and rapidly ventilated to the atmosphere once ice cover retreated. We estimate that river discharge accounts for 〉95% of annual CH4 sea‐to‐air emissions from the estuary. These results demonstrate the importance of resolving seasonal dynamics in order to estimate greenhouse gas emissions from polar systems.

Description: All data generated by the authors that were used in this article are available on PANGAEA (https://doi.org/10.1594/PANGAEA.907159) and model code for estimating CH4 transport is available on GitHub (https://doi.org/10.5281/zenodo.3785893). We acknowledge the use of imagery from the NASA Worldview application (https://worldview.earthdata.nasa.gov), part of the NASA Earth Observing System Data and Information System (EOSDIS), and data from Ocean Networks Canada, and Environment Canada. We thank everyone involved in the fieldwork including C. Amegainik, Y. Bernard, A. Cranch, F. Emingak, S. Marriott, and A. Pedersen. Laboratory analysis and experiments were performed by A. Cranch, R. McCulloch, A. Morrison, and Z. Zheng. We thank J. Brinckerhoff, the Arctic Research Foundation, and the staff of the Canadian High Arctic Research Station for support with field logistics. Funding for the work was provided by MEOPAR NCE funding to B. Else, a WHOI Interdisciplinary Award to A. Michel., D. Nicholson. and S. Wankel, and Canadian NSERC grants to P. Tortell. and B. Else. Authors received fellowships, scholarships, and travel grants including an NSERC postdoctoral fellowship to C. Manning, an NDSEG fellowship to V. Preston, NSERC PGS‐D and Izaak Walton Killam Pre‐Doctoral scholarships to S. Jones, and Northern Scientific Training Program funds (Polar Knowledge Canada, administered by the Arctic Institute of North America, University of Calgary) to S. Jones and P. Duke. We also thank Polar Knowledge Canada (POLAR) and Nunavut Arctic College for laboratory space and field logistics support.

Description: 2020-10-23

Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Michel, A. P. M., Preston, V. L., Fauria, K. E., & Nicholson, D. P. Observations of shallow methane bubble emissions from Cascadia Margin. Frontiers in Earth Science, 9, (2021): 613234, https://doi.org/10.3389/feart.2021.613234.

Description: Open questions exist about whether methane emitted from active seafloor seeps reaches the surface ocean to be subsequently ventilated to the atmosphere. Water depth variability, coupled with the transient nature of methane bubble plumes, adds complexity to examining these questions. Little data exist which trace methane transport from release at a seep into the water column. Here, we demonstrate a coupled technological approach for examining methane transport, combining multibeam sonar, a field-portable laser-based spectrometer, and the ChemYak, a robotic surface kayak, at two shallow (〈75 m depth) seep sites on the Cascadia Margin. We demonstrate the presence of elevated methane (above the methane equilibration concentration with the atmosphere) throughout the water column. We observe areas of elevated dissolved methane at the surface, suggesting that at these shallow seep sites, methane is reaching the air-sea interface and is being emitted to the atmosphere.

Description: Funding for VP was provided by an NDSEG Fellowship. Funding for KF was provided by a WHOI Postdoctoral Scholar Fellowship. Ship time on the R/V Falkor was provided by the Schmidt Ocean Institute (FK180824).

Description: Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Autonomous Systems at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2023.

Description: An improved understanding of our ocean would allow us to characterize the largest habitable biosphere on planet Earth, quantify the geochemical processes that control Earth’s climate, and develop responsible regulations for controlling the natural resources stored in its depths. Expeditionary science is the art of collecting in situ observations of an environment to build approximate models of underlying properties that move us towards this understanding. Robotic platforms are a critical technology for collecting observations of the ocean. Depth-capable autonomous underwater vehicles (AUVs) are commonly used to build static maps of the seafloor by executing pre-programmedsurveys. However, there is growing urgency to generate rich data products of spatiotemporal distributions that characterize the physics and chemistry of the deep ocean biogeosphere. In this thesis, the problem of charting dynamic deep sea hydrothermal plumes with depth-capable AUVs is investigated. Effectively collecting samples of geochemical plumes using the operationally preferred strategy of pre-specifying surveys requires access to a dynamics model of the advective currents, bathymetric updrafts, and turbulent mixing at a hydrothermal site. In practice, however, access to this information is unavailable, imperfect, or only partially known, and so a model of plume dynamics must be inferred from observations and subsequently leveraged to improve future sampling performance. As most in situ scientific instruments yield point-measurements, considerable uncertainty is placed over the form of the dynamics in purely data-driven solutions. Challenges related to planning under uncertainty for geochemical surveys in the deep ocean are addressed in this thesis by embedding scientific knowledge as a strong inductive prior for tractable model learning and decision-making. Algorithmic contributions of this thesis show how plumes can be perceived from field data, their fate predicted far into the future (e.g., multiple days), and informative fixed trajectories planned which place an AUV in the right place at the right time. Scientific assessment of observational data collected with AUV Sentry during field trials in the Guaymas Basin, Gulf of California are interwoven with algorithmic analyses, demonstrating how intelligent perception, prediction, and planning enables novel insights about hydrothermal plumes.

Description: Financial support for my research was provided by the National Defense Graduate Fellowship Program and the MIT Martin Family Society of Fellows for Sustainability. Research activities for the RR2107 cruise were funded by NSF OCE OTIC #1842053, a WHOI Innovation Technology Award, NOAA Ocean Exploration #NA18OAR0110354, and Schmidt Marine Technology Partners Award #G-21-62431.