Cary Lindsey, PhD (Former Employee)
Science and Products
Maps of elevation trend and detrended elevation for the Great Basin, USA Maps of elevation trend and detrended elevation for the Great Basin, USA
Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. Specifically, local relative topography gives information about locations of faults and fault intersections relative to mountains, valleys, or at the transitions between. As part of U.S. Geological...
Heat flow maps and supporting data for the Great Basin, USA Heat 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
Exploration for blind geothermal systems in the eastern Great Basin of Utah: An update on the “Lund North” INGENIOUS detailed study site Exploration for blind geothermal systems in the eastern Great Basin of Utah: An update on the “Lund North” INGENIOUS detailed study site
Existing geothermal production in Utah is commonly collocated with surficial expressions of geothermal heat including active hot springs and hot spring deposits. However, geothermal potential across the Great Basin region is thought to be much higher for hidden or blind geothermal systems. Accordingly, exploration techniques that can locate geothermal resources that lack surface thermal...
Authors
Christian L. Hardwick, Eugene Szymanski, Nicole R. Hart-Wagoner, S. Ashton, N. Christensen, Tait E. Earney, James E. Faulds, Jonathan M.G. Glen, A.I. Hiscock, Stefan Kirby, T. Knudsen, S. Kobe, Cary R. Lindsey, Benjamin Lyter Morbeck, Jared R. Peacock, Grant Harold Rea-Downing, William D. Schermerhorn, K. Smith
Structural setting and geothermal potential of northeastern Reese River Valley, north-central Nevada: Highly prospective detailed study site for the INGENIOUS project Structural setting and geothermal potential of northeastern Reese River Valley, north-central Nevada: Highly prospective detailed study site for the INGENIOUS project
The northeastern part of the Reese River basin situated ~15 km southeast of Battle Mountain, Nevada, scored highly in the Nevada geothermal play fairway analysis (PFA) for hosting potential hidden geothermal systems. This site (also referred to as Argenta Rise) was therefore chosen for detailed study in the INGENIOUS project (INnovative Geothermal Exploration through Novel Investigations...
Authors
James Faulds, Tait E. Earney, Jonathan M.G. Glen, John Queen, Jared R. Peacock, Nicole R. Hart-Wagoner, Kurt Kraal, Cary R. Lindsey, Quentin Burgess, Mary Hannah Giddens
Geophysical modeling of a possible blind geothermal system near Battle Mountain, NV Geophysical modeling of a possible blind geothermal system near Battle Mountain, NV
The northeastern portion of the Reese River basin in north-central Nevada is the focus of detailed geophysical and geological studies as part of the INGENIOUS project, which aims to identify new, commercially viable hidden geothermal systems in the Great Basin region of the western U.S. This location, herein referred to as Argenta Rise, occupies a broad (~15km wide) left-step between...
Authors
Tait E. Earney, Jonathan M.G. Glen, Jared R. Peacock, James Faulds, William D. Schermerhorn, Grant Harold Rea-Downing, Jacob Elliott Anderson, Cary R. Lindsey, Maria Richards
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 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...
Authors
Pascal D. Caraccioli, Stanley Paul Mordensky, Cary R. Lindsey, Jacob DeAngelo, Erick R. Burns, John Lipor
Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability
Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. The Nevada Machine Learning Project (NVML) tested the use of a digital elevation map (DEM) of topography as an input feature to predict geothermal system favorability. A recent study re-examines the...
Authors
Jacob DeAngelo, Erick R. Burns, Stanley Paul Mordensky, Cary Ruth Lindsey
When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates 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...
Authors
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey
New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences 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...
Authors
Jacob DeAngelo, Erick R. Burns, Emilie Gentry, Joseph F. Batir, Cary Ruth Lindsey, Stanley Paul Mordensky
Exploring declustering methodology for addressing geothermal exploration bias Exploring declustering methodology for addressing geothermal exploration bias
Geothermal resources assessments use data that are unevenly distributed in space, with more data collected in areas with known thermal features. To meet the assumptions for geostatistical modeling (e.g., variography and kriging) such as having a random sample representative of the population, declustering may be needed to correct for spatial sample bias. Several declustering methods...
Authors
Cary Ruth Lindsey, Adam N. Price, Erick R. Burns
What did they just say? Building a Rosetta stone for geoscience and machine learning 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...
Authors
Stanley Paul Mordensky, John Lipor, Erick R. Burns, Cary Ruth Lindsey
Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions 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...
Authors
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey
Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis
We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new...
Authors
James Faulds, Stephen C. Brown, Mark F. Coolbaugh, John H. Queen, Sven Treitel, Michael Fehler, Eli Mlawsky, Jonathan M.G. Glen, Cary Lindsey, Erick R. Burns, Connor M. Smith, Chen Gu, Bridget F. Ayling
Science and Products
Maps of elevation trend and detrended elevation for the Great Basin, USA Maps of elevation trend and detrended elevation for the Great Basin, USA
Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. Specifically, local relative topography gives information about locations of faults and fault intersections relative to mountains, valleys, or at the transitions between. As part of U.S. Geological...
Heat flow maps and supporting data for the Great Basin, USA Heat 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
Exploration for blind geothermal systems in the eastern Great Basin of Utah: An update on the “Lund North” INGENIOUS detailed study site Exploration for blind geothermal systems in the eastern Great Basin of Utah: An update on the “Lund North” INGENIOUS detailed study site
Existing geothermal production in Utah is commonly collocated with surficial expressions of geothermal heat including active hot springs and hot spring deposits. However, geothermal potential across the Great Basin region is thought to be much higher for hidden or blind geothermal systems. Accordingly, exploration techniques that can locate geothermal resources that lack surface thermal...
Authors
Christian L. Hardwick, Eugene Szymanski, Nicole R. Hart-Wagoner, S. Ashton, N. Christensen, Tait E. Earney, James E. Faulds, Jonathan M.G. Glen, A.I. Hiscock, Stefan Kirby, T. Knudsen, S. Kobe, Cary R. Lindsey, Benjamin Lyter Morbeck, Jared R. Peacock, Grant Harold Rea-Downing, William D. Schermerhorn, K. Smith
Structural setting and geothermal potential of northeastern Reese River Valley, north-central Nevada: Highly prospective detailed study site for the INGENIOUS project Structural setting and geothermal potential of northeastern Reese River Valley, north-central Nevada: Highly prospective detailed study site for the INGENIOUS project
The northeastern part of the Reese River basin situated ~15 km southeast of Battle Mountain, Nevada, scored highly in the Nevada geothermal play fairway analysis (PFA) for hosting potential hidden geothermal systems. This site (also referred to as Argenta Rise) was therefore chosen for detailed study in the INGENIOUS project (INnovative Geothermal Exploration through Novel Investigations...
Authors
James Faulds, Tait E. Earney, Jonathan M.G. Glen, John Queen, Jared R. Peacock, Nicole R. Hart-Wagoner, Kurt Kraal, Cary R. Lindsey, Quentin Burgess, Mary Hannah Giddens
Geophysical modeling of a possible blind geothermal system near Battle Mountain, NV Geophysical modeling of a possible blind geothermal system near Battle Mountain, NV
The northeastern portion of the Reese River basin in north-central Nevada is the focus of detailed geophysical and geological studies as part of the INGENIOUS project, which aims to identify new, commercially viable hidden geothermal systems in the Great Basin region of the western U.S. This location, herein referred to as Argenta Rise, occupies a broad (~15km wide) left-step between...
Authors
Tait E. Earney, Jonathan M.G. Glen, Jared R. Peacock, James Faulds, William D. Schermerhorn, Grant Harold Rea-Downing, Jacob Elliott Anderson, Cary R. Lindsey, Maria Richards
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 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...
Authors
Pascal D. Caraccioli, Stanley Paul Mordensky, Cary R. Lindsey, Jacob DeAngelo, Erick R. Burns, John Lipor
Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability
Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. The Nevada Machine Learning Project (NVML) tested the use of a digital elevation map (DEM) of topography as an input feature to predict geothermal system favorability. A recent study re-examines the...
Authors
Jacob DeAngelo, Erick R. Burns, Stanley Paul Mordensky, Cary Ruth Lindsey
When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates 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...
Authors
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey
New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences 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...
Authors
Jacob DeAngelo, Erick R. Burns, Emilie Gentry, Joseph F. Batir, Cary Ruth Lindsey, Stanley Paul Mordensky
Exploring declustering methodology for addressing geothermal exploration bias Exploring declustering methodology for addressing geothermal exploration bias
Geothermal resources assessments use data that are unevenly distributed in space, with more data collected in areas with known thermal features. To meet the assumptions for geostatistical modeling (e.g., variography and kriging) such as having a random sample representative of the population, declustering may be needed to correct for spatial sample bias. Several declustering methods...
Authors
Cary Ruth Lindsey, Adam N. Price, Erick R. Burns
What did they just say? Building a Rosetta stone for geoscience and machine learning 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...
Authors
Stanley Paul Mordensky, John Lipor, Erick R. Burns, Cary Ruth Lindsey
Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions 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...
Authors
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey
Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis
We are applying machine learning (ML) techniques, including training set augmentation and artificial neural networks, to mitigate key challenges in the Nevada play fairway project. The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new...
Authors
James Faulds, Stephen C. Brown, Mark F. Coolbaugh, John H. Queen, Sven Treitel, Michael Fehler, Eli Mlawsky, Jonathan M.G. Glen, Cary Lindsey, Erick R. Burns, Connor M. Smith, Chen Gu, Bridget F. Ayling
*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