Whitney Trainor-Guitton is a quantitative geoscientist with a demonstrated history of working in the subsurface and remote-sensing research and development and higher education. Whitney is experienced in blending data science, spatial statistics and geophysical inversion, and specializes in geostatistics, geophysics, geothermal exploration, carbon dioxide sequestration monitoring, inverse problems, and deep learning. She earned her doctorate focused on earth, energy and environmental sciences from Stanford University. She has also served as a staff scientist at Lawrence Livermore National Laboratory and assistant professor at the Colorado School of Mines doing research and development in statistical learning applied to distributed acoustic sensing, angle versus offset seismic data and screen out pressure data. Currently, Whitney is advancing play fairways analysis for discovery of hidden geothermal systems, using value of information and spatial statistics.

Research Interests

Value of information methodologies

Stochastic techniques and geostatistical modeling of geophysical data

Distributed acoustic sensing

Integration of data of diverse scales

Uncertainty quantification

Subsurface flow modeling

Carbon sequestration monitoring

Geothermal exploration

Education

Ph.D., Earth, Energy & Environmental Science, Stanford University

M.S., Geophysics, Stanford University

B.S., Geophysical Engineering, Colorado School of Mines

Professional Experience

Affiliate Faculty, Colorado School of Mines (2023–present)

Geoscience Researcher, NREL (2022–present)

Team Member, INGENIOUS Project (2021–present)

Senior Decision Scientist, Zanskar Geothermal & Minerals (2021–2022)

Geophysicist, SeaOwl Energy (2020–2021)

Assistant Professor, Colorado School of Mines (2015–2019)

Postdoctoral Research Fellow, Lawrence Livermore National Laboratory (2010–2014)

Staff Scientist, Lawrence Livermore National Laboratory (2010–2011)

Risk Analyst, Risk Management Solutions (2010- 2011)

Featured Work

Distributed Sensing and Machine Learning Hone Seismic Listening, Eos. (2022)

Development of a Geostatistical Thermal Model in the Great Basin Region, Western USA: A Pilot Study in Western Nevada, World Geothermal Congress (2020)

Python Earth Engine API As a New Open-Source Ecosphere for Characterizing Offshore Hydrocarbon Seeps and Spills, The Leading Edge (2021)

3D Imaging of Geothermal Faults from a Vertical DAS Fiber at Brady Hot Spring, NV USA, Energies (2019)

Uncertainty and Risk Evaluation During the Exploration Stage of Geothermal Development: A Review, Geothermics (2019)

Value of MT Inversions for Geothermal Exploration: Accounting for Multiple Interpretations of Field Data & Determining New Drilling Locations, Geothermics (2017)

Awards and Honors

Honorable Mention,  The Leading Edge January 2021 Article Python Earth Engine API As a New Open-Source Ecosphere for Characterizing Offshore Hydrocarbon Seeps and Spills (2021)

R&D 100 Award, National Risk Assessment Program  [vKT1] Phase 1  (2017)

WiSTEM2D Scholars Finalist, Johnson & Johnson (2017)

Top 39 Papers,  Society of Exploration Geophysicists 2017: Statistical Imaging of Faults In 3D Seismic Volumes Using A Machine (2017)

Best Presentation, Geothermal Resources Council Annual Meeting (2013)

Best Poster Atmosphere, Energy and Earth Division, Lawrence Livermore National Laboratory Postdoc Poster Symposium (2012)