Heather Bedle

Lissa and Cy Wagner Professor

Edith Kinney Gaylord Presidential Professorship

hbedle@ou.edu
Sarkeys Energy Center,

About

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

Education

Ph.D., 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
B.S., 1986, University of California, Los Angeles

Research Interests

Data Science and Machine learning
Advanced Seismic Reflection Methods
Geothermal and Carbon Capture Reservoir Characterization
Archean Craton Tectonics

Courses Taught

Introduction to Petroleum Geology & Geophysics
Subsurface Methods
Seismic Exploration
3D Seismic Interpretation
Quantitative Seismic Interpretation (AVO focus)
Designing Dynamic Presentations
Advanced Workflows in 3D Seismic (Attributes and SOM focus)
Machine Learning for Geoscientists
Advanced Workflows in 3D Seismic (ML focus)
Python for Geoscientists
Multidisciplinary Exploration

Selected Publications

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

Patents

Zheng and Hu, Nonlinear signal comparison and high-resolution measurement of seismic or acoustic wave dispersion”. One commercial license has been authorized by an industry company.
Zheng and Hu, Surface wave prediction and removal from seismic data
Liu and Hu, "Seismic prestack migration imaging method”, CN102944894B.

School of Geosciences
Sarkeys Energy Center
100 East Boyd Street, RM 710
Norman, Oklahoma 73019
Phone: (405) 325-3253

Updated 11/20/2024 by School of Geosciences: geology@ou.edu