Taylor R. Lee

United States Naval Research Laboratory — United States

Environmental Science · Physical Sciences

6h-index218citations38works0.12yr avg
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Research Topics

Methane Hydrates and Related Phenomena(17), Hydrocarbon exploration and reservoir analysis(8), Atmospheric and Environmental Gas Dynamics(7), Geochemistry and Geologic Mapping(6), Underwater Acoustics Research(5)

Publications38 total

The updated distribution of clay minerals in the World Ocean
Marine Geology·2025Open Access
Global Seafloor Grain-Size Prediction: A Data-Driven Approach
2025Open Access
Evidence for Arctic paleothermokarst and melt features controlled by sea-level rise in regional sub-bottom profile data
2025Open Access
Modeling gas, hydrates, and slope stability on the U.S. Atlantic margin during Pleistocene glacial cycles
Marine Geology·2025
Using Machine Learning and Fluid Flow Modeling to Predict the Response of Marine Sediments around the Cascadia Margin to Gas Formation and Hydrate Dissociation
2024Open Access
The updated distribution of clay minerals in the World Ocean
2024Open Access
XBTs Provide First‐Order Characterization of Seabed Physical Properties
Earth and Space Science·2024· 1 citedOpen Access
Comment on essd-2023-295
2023Open Access
The necessary optimization of the data lifecycle: Marine geosciences in the big data era
Frontiers in Earth Science·2023· 7 citedOpen Access
BRINGING MARINE GEOSCIENCE INTO THE BIG DATA ERA: A NECESSARY APPROACH WHEN ‘I DON’T KNOW’ ISN’T GOOD ENOUGH
Abstracts with programs - Geological Society of America·2023
Predicting Shallow Marine Gas Occurrence using Geospatial Machine Learning Combined with Mechanistic Subsurface Simulation.
2022Open Access
Global Estimates of Biogenic Methane Production in Marine Sediments Using Machine Learning and Deterministic Modeling
Global Biogeochemical Cycles·2022· 10 citedOpen Access
Forecasting Marine Sediment Properties with Geospatial Machine Learning
2021· 3 citedOpen Access
Global input datasets for use in constraints on global seafloor biogenic methane production from deterministic and machine learning modeling
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Global input datasets for use in constraints on global seafloor biogenic methane production from deterministic and machine learning modeling
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
Global Marine Isochore Estimates Using Machine Learning
Geophysical Research Letters·2020· 8 citedOpen Access
Machine Learning Augmented Time‐Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front
Geophysical Research Letters·2020· 9 citedOpen Access
Predictors and observed depth change data used for manuscript "Machine Learning as a Force Multiplier for Marine Geohazard Assessment"
Zenodo (CERN European Organization for Nuclear Research)·2020Open Access
Predictors and observed depth change data used for manuscript "Machine Learning as a Force Multiplier for Marine Geohazard Assessment"
Figshare·2020Open Access
Data files associated with the k-nearest neighbor global prediction of isopachs for present to middle Miocene
2020· 2 citedOpen Access
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Frequent Co-authors

Benjamin J. Phrampus(31), Warren T. Wood(26), Jeffrey Obelcz(14), Adam Skarke(7), Jennifer Frederick(4), Michael A. Nole(4), Warren W. Wood(4), Hugh Daigle(4), David Fukuyama(3), Olin Carty(3), Trilby Hill(3), Jordan H. Graw(3), William Eymold(2), M. A. L. Walton(2), Taylor Vander(2), Hongkyu Yoon(1), Ethan Conley(1), Matthew J. Hornbach(1), Andrei Abelev(1), Peter C. Herdic(1)