ALBERT

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.

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

Description: To predict and mitigate anthropogenic impacts on the ocean, we must understand the underlying systems that govern the ocean’s response to inputs (e.g. carbon dioxide, pollutants). Analytical models can be used to generate predictions and simulate intervention strategies, but they must be grounded with empirical observations. Unfortunately, there exists a technological gap: in situ instrumentation is often lacking or nonexistent for key parameters influenced by anthropogenic inputs. While discrete bottle samples can be collected and analyzed for these parameters, their limited spatiotemporal resolution constrains scientific inquiry. To help fill the technological gap, this dissertation presents the development of instrumentation for the ocean inorganic carbon system and microplastics. The first few chapters present the development process of CSPEC, a deep-sea laser spectrometer designed to measure the ocean carbon system through alternating measurements of the partial pressure of carbon dioxide (pCO2) and dissolved inorganic carbon (DIC). CSPEC uses tunable diode laser absorption spectroscopy (TDLAS) to measure the CO2 content of dissolved gas extracted via a membrane inlet. Chapter 2 derives membrane equilibration dynamics from first principles, thus enabling informed design decisions. The analytical results showed that cross-sensitivity to other dissolved gases can be introduced by the equilibration method, regardless of the specificity of the gas-side instrumentation. A new method, hybrid equilibration, leverages the membrane equilibration dynamics to improve time response without incurring cross-sensitivity. Chapter 3 presents POCO, a surface pCO2 instrument that employs TDLAS and a depth-compatible membrane inlet. Through laboratory and field-testing, POCO demonstrated that hybrid equilibration overcame the gas flux limitation of deep-sea membrane inlets. Chapter 4 presents CSPEC, which successfully mapped the carbon system near different hydrothermal features at 2000 m in Guaymas Basin, becoming one of the first DIC instruments field-tested at depth. Chapter 5 introduces impedance spectroscopy for quantifying microplastics directly in water. Microplastics were successfully counted, sized, and differentiated from biology in the laboratory: a step toward in situ quantification. The analytical tools and measurement systems presented in this dissertation represent a significant step towards increasing the spatiotemporal resolution of carbon system and microplastic measurements, thus enabling broader scientific inquiry in the future.

Description: This research was supported by the following funding sources: NSF Grant # OCE-1454067 NSF Grant # OCE-184-2053 Link Foundation Ocean Engineering and Instrumentation Ph.D. Fellowship MITMartin Family Society of Fellows for Sustainability Richard Saltonstall Charitable Foundation National Academies Keck Future Initiative (NAFKI DBS13)