| A machine learning (kNN) approach to predicting global seafloor total organic carbon TR Lee, WT Wood, BJ Phrampus Global Biogeochemical Cycles 33 (1), 37-46, 2019 | 112 | 2019 |
| A global probabilistic prediction of cold seeps and associated SEAfloor FLuid Expulsion Anomalies (SEAFLEAs) BJ Phrampus, TR Lee, WT Wood Geochemistry, Geophysics, Geosystems 21 (1), e2019GC008747, 2020 | 20 | 2020 |
| Global marine isochore estimates using machine learning TR Lee, BJ Phrampus, J Obelcz, WT Wood, A Skarke Geophysical Research Letters 47 (18), e2020GL088726, 2020 | 10 | 2020 |
| Machine learning augmented time‐lapse bathymetric surveys: A case study from the Mississippi river delta front J Obelcz, WT Wood, BJ Phrampus, TR Lee Geophysical Research Letters 47 (10), e2020GL087857, 2020 | 7 | 2020 |
| Practical quantification of uncertainty in seabed property prediction using geospatial KNN machine learning W Wood, T Lee, J Obelcz EGU General Assembly Conference Abstracts, 9760, 2018 | 6 | 2018 |
| Global estimates of biogenic methane production in marine sediments using machine learning and deterministic modeling TR Lee, BJ Phrampus, A Skarke, WT Wood Global Biogeochemical Cycles 36 (7), e2021GB007248, 2022 | 4 | 2022 |
| Forecasting marine sediment properties with geospatial machine learning JM Frederick, WK Eymold, MA Nole, BJ Phrampus, TR Lee, WT Wood, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | 3 | 2021 |
| The necessary optimization of the data lifecycle: Marine geosciences in the big data era TR Lee, BJ Phrampus, J Obelcz Frontiers in Earth Science 10, 1089112, 2023 | 1 | 2023 |
| Nowcasting submarine slope instability at local, margin, and global scales using machine learning J Obelcz, WT Wood, BJ Phrampus, TR Lee EarthArXiv, 2019 | 1 | 2019 |
| Development of a global predictive seabed model (GPSM) WT Wood, BJ Phrampus, TR Lee, J Obelcz AGU Fall Meeting Abstracts 2018, T31E-0369, 2018 | 1 | 2018 |
| Global Seafloor Prediction of Total Organic Carbon, Total Inorganic Carbon, and Mass Accumulation Rate Using Geospatial Machine Learning JY Peter, WT Wood, TR Lee, MAL Walton, J Graw, M Uwaifo AGU23, 2024 | | 2024 |
| Machine learning inputs to estimate Arctic marine sediments methane inventories for modern and last glacial maximum conditions TR Lee, BJ Phrampus, WT Wood AGU23, 2023 | | 2023 |
| Empirically determined global marine shear strength estimates via machine learning and sediment physics models TR Lee, J Obelcz, WT Wood AGU23, 2023 | | 2023 |
| Preliminary Results using a Physics-Informed Neural Network (PINN) to predict sub-surface sediment properties BJ Phrampus, TR Lee, J Graw, WT Wood AGU23, 2023 | | 2023 |
| Characterizing the heterogeneity of gas and gas hydrate accumulations in natural marine sediment. WT Wood, BJ Phrampus, TR Lee AGU23, 2023 | | 2023 |
| Correlating Satellite Derived Ocean Color with Benthic Sedimentation GA Restreppo, JH Graw, BJ Phrampus, TR Lee, WT Wood Fall Meeting 2022, 2022 | | 2022 |
| A Data Driven Approach to Quantifying Fraction Clay Mineralogy from Grain Size and Clay Species Analysis TA Hill, J Obelcz, T Vander, TR Lee AGU Fall Meeting Abstracts 2022, EP11A-01, 2022 | | 2022 |
| Biogenic methane production and gas hydrate distribution during the Last Glacial Maximum: estimates utilizing machine learning and model-based inputs BJ Phrampus, TR Lee, WT Wood AGU Fall Meeting Abstracts 2022, OS12C-0761, 2022 | | 2022 |
| New Constraints on Gas and Gas Hydrate Concentration Estimates on the Cascadia Margin from Long-Offset Multichannel Seismic Data. WT Wood, BJ Phrampus, TR Lee, A Douglass, S Abadi AGU Fall Meeting Abstracts 2022, OS12C-0762, 2022 | | 2022 |
| Inventory of Arctic carbon and methane in marine sediments using machine learning and deterministic models TR Lee, BJ Phrampus, WT Wood AGU Fall Meeting Abstracts 2022, OS12C-0764, 2022 | | 2022 |
