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Heather Bedle

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Heather Bedle

Brett Carpenter

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 872

Heather Bedle CV (pdf)

Faculty Page

Areas of Interest

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.

Courses Taught

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 

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,

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.

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