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Stanley P Mordensky

Stanley is a Research Geologist at the Geology, Minerals, Energy, and Geophysics Science Center. Since joining the USGS, Stanley has devoted his career to developing machine learning methods for analysis of geothermal systems, groundwater, and subsurface heat flow and specializes in experimental rock mechanics, volcano processes, fluid transport, and hydrothermal alteration.

Stanley joined the USGS as a Mendenhall Research Fellow with the Geology, Minerals, Energy, and Geophysics Science Center in February 2021. His research interests vary from machine learning, geothermal systems, and volcano monitoring to rock mechanics, geochemistry, and geohazards. Stan spent several seasons mapping lithology, geotechnical units, geothermal hazards, and volcanic hazards across several geophysical provinces and has taught these subjects to university students. He also served as a volunteer for the Hawaiian Volcano Observatory and Yellowstone National Park.

Professional Experience

  • 2020: Guest Scientist, Yellowstone National Park, Mammoth Hot Springs, WY

  • 2017: Guest Scientist, Yellowstone National Park, Mammoth Hot Springs, WY

  • 2014 - 2015: Research Fellow, National Energy and Technology Laboratory. Albany, OR

  • 2010: National Association of Geoscience Teachers Fellow, Reston, VA

Education and Certifications

  • Ph.D., Engineering Geology, University of Canterbury, New Zealand, 2019

  • MSc, Geological Sciences, University of Oregon, USA, 2012

  • BSc, Economics, George Washington University, USA 2009

  • BA, Geological Sciences, George Washington University, USA 2009

Affiliations and Memberships*

  • Geological Society of America

Science and Products

New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences

Geothermal well data from Southern Methodist University and the U.S. Geological Survey (USGS) were used to create maps of estimated background conductive heat flow across the Great Basin region of the western United States. These heat flow maps were generated as part of the USGS hydrothermal and Enhanced Geothermal Systems resource assessment process, and the creation process seeks to remove the i
By
Geology, Minerals, Energy, and Geophysics Science Center

What did they just say? Building a Rosetta stone for geoscience and machine learning

Modern advancements in science and engineering are built upon multidisciplinary projects that bring experts together from different fields. Within their respective disciplines, researchers rely on precise terminology for specific ideas, principles, methods, and theories. Hence, the potential for miscommunication is substantial, especially when common words have been adopted by one (or both) group(
By
Geology, Minerals, Energy, and Geophysics Science Center

Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized weight-of-evidence and logistic regression methods to estimate resource favorability, but these analyses relied upon some expert decisions. While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also in
By
Geology, Minerals, Energy, and Geophysics Science Center

Science and Products

*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government