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
When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates
New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences
What did they just say? Building a Rosetta stone for geoscience and machine learning
Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions
Geothermal resource favorability: select features and predictions for the western United States curated for DOI 10.1016/j.geothermics.2023.102662
Heat flow maps and supporting data for the Great Basin, USA
Science and Products
- Publications
When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates
Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of errAuthorsStanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth LindseyNew 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 iAuthorsJacob DeAngelo, Erick R. Burns, Emilie Gentry, Joseph F. Batir, Cary Ruth Lindsey, Stanley Paul MordenskyWhat 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(AuthorsStanley Paul Mordensky, John Lipor, Erick R. Burns, Cary Ruth LindseyPredicting 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 inAuthorsStanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey - Data
Geothermal resource favorability: select features and predictions for the western United States curated for DOI 10.1016/j.geothermics.2023.102662
The data contained herein are five input features (i.e., heat flow, distance to the nearest quaternary fault, distance to the nearest quaternary magma body, seismic event density, maximum horizontal stress) and labels (i.e., where known geothermal systems have been identified) from Williams and DeAngelo (2008) and nine favorability maps from Mordensky et al. (2023). The favorability maps are the uHeat flow maps and supporting data for the Great Basin, USA
Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of backgroun
*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