Lissa and Cy Wagner Professor
Edith Kinney Gaylord Presidential Professorship
Associate Professor of Geosciences
PhD, 2008 Earth and Planetary Sciences – Northwestern University Evanston, IL
M.S., 2005 Geological Sciences - Northwestern University Evanston, IL
B.S., 1999 Physics – Wake Forest University – Winston-Salem, NC
Office: SEC 746
Email: hbedle@ou.edu
Data Science and Machine learning, Advanced seismic reflection methods, Geothermal and Carbon capture reservoir characterization, Archean craton tectonics
My research interests focus primarily on combining a range of techniques across the disciplines of geosciences, data science, and environmental sciences to further improve our understanding of the earth's crust, and the socio-dynamics of our interactions with the environment. My research works to refine and employ a wide range of interpretation tools and workflows based in data science and a large variety of machine learning methods to improve our workflows in multi-attribute seismic analysis, and seismic geomorphology.. I am currently working on a variety of projects including improving the seismic identification of gas hydrate zones in the subsurface, as well as techniques to improve reservoir characterization and prediction on the sub-seismic scale for carbon capture and geothermal energy, as well as new methods to understand the dynamics between the environment and society in the presence of climate challenges.
GPHY 3423: Introduction to Petroleum Geology & Geophysics
GEOL 4233: Subsurface Methods
GPHY 4874: Seismic Exploration
GPHY 5513: 3D Seismic Interpretation
GPHY 5533: Quantitative Seismic Interpretation (AVO focus)
GPHY 5970: Designing Dynamic Presentations
GPHY 6790: Advanced Workflows in 3D Seismic (Attributes and SOM focus)
GPHY 6970: Machine Learning for Geoscientists
GPHY 6970: Advanced Workflows in 3D Seismic (ML focus)
GPHY 6970: Python for Geoscientists
GPHY 6970: Multidisciplinary Exploration
Bedle, H., Lou, X., and S. van der Lee. High-resolution imaging of continental tectonics in the mantle beneath the United States, through the combination of USArray data sets, Geochemisty, Geophysics, Geosystems, 2021, doi.org/10.1029/2021GC009674
Bedle, H., Cooper, C., and C. Frost, Nature versus Nurture: Preservation and Destruction of Archean Cratons, Tectonics, e2021TC006714, 2021 doi: 10.1029/2021TC006714
Salazar Florez, D., and H. Bedle, Study on the parameterization response of probabilistic neural Networks for Seismic Facies Classification in the Gulf of Mexico, Interpretations,Vol. 10, Iss 1 (2022) DOI: 10.1190/INT-2020-0218.1
Lubo-Robles, D., D. Devegowda, V. Jayaram, H. Bedle, K., Marfurt, M. Pranter, Quantifying the sensitivity of seismic facies classification to seismic attribute selection: An explainable machine learning study, Interpretations, 2022
La Marca, K., and H. Bedle. Deepwater seismic facies and architectural element interpretation aided with unsupervised machine learning techniques: Taranaki basin, New Zealand. Marine and Petroleum Geology, 2022. doi.org/10.1016/j.marpetgeo.2021.105427
Buist, C., Bedle, H, Rine, M., and J. Pigott. Enhancing Paleoreef Reservoir Characterization through Machine Learning and Multi-Attribute Seismic Analysis: Silurian Reef Examples from the Michigan Basin, Geosciences 11(3), 142, 2021 doi: 10.3390/geosciences11030142
Chenin, J., Bedle, H. Multi-attribute machine learning analysis for weak BSR detection in the Pegasus Basin, Offshore New Zealand. Mar Geophys Res 41, 21 (2020). doi:10.1007/s11001-020-09421-x
