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
Maps of elevation trend and detrended elevation for the Great Basin, USA
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
Predicting large hydrothermal systems
We train five models using two machine learning (ML) regression algorithms (i.e., linear regression and XGBoost) to predict hydrothermal upflow in the Great Basin. Feature data are extracted from datasets supporting the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS). The label data (the reported convective signals) are extracted from meas
Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models
Recent advances in machine learning (ML) identifying areas favorable to hydrothermal systems indicate that the resolution of feature data remains a subject of necessary improvement before ML can reliably produce better models. Herein, we consider the value of adding new features or replacing other, low-value features with new input features in existing ML pipelines. Our previous work identified st
Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions
Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify area
Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability
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
Science and Products
Maps of elevation trend and detrended elevation for the Great Basin, USA
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
Predicting large hydrothermal systems
We train five models using two machine learning (ML) regression algorithms (i.e., linear regression and XGBoost) to predict hydrothermal upflow in the Great Basin. Feature data are extracted from datasets supporting the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems project (INGENIOUS). The label data (the reported convective signals) are extracted from meas
Cursed? Why one does not simply add new data sets to supervised geothermal machine learning models
Recent advances in machine learning (ML) identifying areas favorable to hydrothermal systems indicate that the resolution of feature data remains a subject of necessary improvement before ML can reliably produce better models. Herein, we consider the value of adding new features or replacing other, low-value features with new input features in existing ML pipelines. Our previous work identified st
Don’t Let Negatives Hold You Back: Accounting for Underlying Physics and Natural Distributions of Hydrothermal Systems When Selecting Negative Training Sites Leads to Better Machine Learning Predictions
Selecting negative training sites is an important challenge to resolve when utilizing machine learning (ML) for predicting hydrothermal resource favorability because ideal models would discriminate between hydrothermal systems (positives) and all types of locations without hydrothermal systems (negatives). The Nevada Machine Learning project (NVML) fit an artificial neural network to identify area
Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability
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
*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