Jacob DeAngelo
Jacob DeAngelo is a Geologist with the GMEG Science Center in Moffett Field, CA. He received an M.S. degree in Geosciences from San Francisco State University and a B.S. in Geography and Environmental Science from the University of Oregon.
Since joining the USGS in 2008 as a Geologist, Jacob has devoted his career to studying geothermal resources and specializes in feature engineering with spatial data and machine learning based predictive modeling. Presently, his research focuses on producing geothermal assessments and products used to create them. is a member of the Geothermal Resource Investigations Project (GRIP) at USGS. Jacob is a core member of GRIP’s small machine learning team, seeking to apply well founded modeling approaches in predictive models used to characterize geothermal resource potential in the US. Jacob specializes in work with spatial data, using GIS and programming to engineer features for our predictive models from geologic and geophysical data.
Professional Experience
2021 – Present, Geologist, Geology Minerals Energy and Geophysics Science Center, Moffett Field, CA
2008 - 2021, Cartographer, Geology Minerals Energy and Geophysics Science Center, Moffett Field, CA.
Education and Certifications
M.S. Geosciences, San Francisco State University, 2019
B.S. Physical Geography and Environmental Science, Biology minor, University of Oregon, 2006
Science and Products
Three-dimensional temperature model of the Great Basin, USA
As part of the periodic update of the geothermal energy assessments for the USA (e.g., last update by Williams and others, 2008), a new three-dimensional temperature map has been constructed for the Great Basin, USA. Williams and DeAngelo (2011) identified uncertainty in estimates of conductive heat flow near land surface as the largest contributor to uncertainty in previously published temperatur
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 Survey effor
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 u
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 backgroun
Geophysical characterization of geothermal resources on the Umatilla Indian Reservation in northeast Oregon
During the summers of 2017 and 2020, our team collected gravity, ground magnetic (ATV and hiked traverses), paleomagnetic and rock property (density and susceptibility) data on the Umatilla Indian Reservation (UIR) in northeast Oregon to aid in characterizing subsurface stratigraphy using 2D and 3D modeling methods. This data was integrated with conductance surfaces from a 3D magnetotelluic model
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics Data
This package contains gravity and magnetics data and products generated for the Nevada Machine Learning (NVML) project (DE-FOA-0001956). Data products contained in this release consist of grids and vector data. Grids include: primary anomaly maps (isostatic and PSG), match-filtered maps, horizontal gradient (HG) maps, confidence maps, and maps showing density of specific key structural features. T
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Heat Flow Data
This package contains a map surface that depicts the estimated spatial variation of conductive heat flow (mW/m?) in a portion of northern Nevada, the extent of the ?Nevada Machine Learning Project? (DE-EE0008762). It was generated using well locations that had an estimated heat flow value from a measured thermal gradient and thermal conductivity, mainly using data from Southern Methodist Universit
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Slip and Dilation Tendency Data
This package contains data in a portion of northern Nevada, the extent of the ?Nevada Machine Learning Project? (DE-EE0008762). Slip tendency (TS) and dilation tendency (TD) were calculated for the all the faults in the Nevada ML study area. TS is the ratio between the shear components of the stress tensor and the normal components of the stress tensor acting on a fault plane. TD is the ratio of a
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics, Heat Flow, Slip and Dilation Tendency
This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project (DE-FOA-0001956), with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geophysics, heat flow, and fault dilation and slip tendencies) that cover a large portion of northern Nevad
Digital data from USGS OFR 83-250: Selected data for low-temperature (less than 90 degrees C) geothermal systems in the United States; reference data for U.S. Geological Survey Circular 892
The data in the csv and text files provided in this release are an update to the data tables originally published in USGS Open-File Report (OFR) 83-250 (https://doi.org/10.3133/cir892). Those data were published as paper tables and have until now only been available as pdf image documents that were not machine readable. USGS OFR 83-250 presented data for 2071 geothermal sites which are representat
Snake River Plain Play Fairway Analysis Phase 2 Favorability Model (DE EE0006733)
This data release contains all digital geographic data used and produced by the Snake River Plain Play Fairway Analysis (DE EE0006733) for Phase 2 (ArcGIS shapefiles and raster files) as well as the model processing script, tables, and documentation used to generate data outputs. Phase 2 examines two subset areas of the Phase 1 study area, Mountain Home and Camas Prairie. Brief descriptions of dat
Snake River Plain Play Fairway Analysis Phase 1 Favorability Model (DE EE0006733)
This data release contains all digital geographic data used and produced by the Snake River Plain Play Fairway Analysis (DE EE0006733) for Phase 1 (ArcGIS shapefiles and raster files) as well as the model processing script, tables, and documentation used to generate data outputs. Brief descriptions of data layers are in the metadata of GIS files, greater detail is available in the Larger Work, the
Filter Total Items: 13
Updated three-dimensional temperature maps for the Great Basin, USA
As part of the periodic update of the geothermal energy assessments for the USA (e.g., last update by Williams and others, 2008), a new three-dimensional temperature map has been constructed for the Great Basin, USA. Williams and DeAngelo (2011) identified uncertainty in estimates of conductive heat flow near land surface as the largest contributor to uncertainty in previously published temperatur
Authors
Erick Burns, Jacob DeAngelo, Colin F. Williams
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
Authors
Stanley Paul Mordensky, Erick Burns, Jacob DeAngelo, John Lipor
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
Authors
Stanley Paul Mordensky, Erick Burns, John Lipor, Jacob DeAngelo
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
Authors
Pascal D. Caraccioli, Stanley Paul Mordensky, Cary R. Lindsey, Jacob DeAngelo, Erick Burns, John Lipor
Geothermal Play Fairway Analysis, Part 2: GIS methodology
Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal re
Authors
Jacob DeAngelo, John W. Shervais, Jonathan M.G. Glen, Dennis Nielson, Sabodh Garg, Patrick Dobson, Erika Gasperikova, Eric Sonnenthal, Lee M. Liberty, Drew Lorenz Siler, James P. Evans
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 NVML data,
Authors
Jacob DeAngelo, Erick Burns, Stanley Paul Mordensky, Cary Ruth Lindsey
Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho
The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal re
Authors
John W. Shervais, Jacob DeAngelo, Jonathan M.G. Glen, Dennis L. Nielson, Sabodh Garg, Patrick Dobson, Erika Gasperikova, Eric Sonnenthal, Lee M. Liberty, Dennis L. Newell, Drew Lorenz Siler, James P. Evans
Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis
Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that prod
Authors
Connor M. Smith, James E. Faulds, Stephen C. Brown, Mark Coolbaugh, Jacob DeAngelo, Jonathan M.G. Glen, Erick Burns, Drew Lorenz Siler, Sven Treitel, Eli Mlawsky, Michael Fehler, Chen Gu, Bridget F. Ayling
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 err
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
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
Authors
Jacob DeAngelo, Erick R. Burns, Emilie Gentry, Joseph F. Batir, Cary Ruth Lindsey, Stanley Paul Mordensky
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
Authors
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey
Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada
A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a pro
Authors
Stephen C. Brown, Mark F. Coolbaugh, Jacob DeAngelo, James E. Faulds, Michael Fehler, Chen Gu, John H. Queen, Sven Treitel, Connor M. Smith, Eli Mlawsky
Science and Products
Three-dimensional temperature model of the Great Basin, USA
As part of the periodic update of the geothermal energy assessments for the USA (e.g., last update by Williams and others, 2008), a new three-dimensional temperature map has been constructed for the Great Basin, USA. Williams and DeAngelo (2011) identified uncertainty in estimates of conductive heat flow near land surface as the largest contributor to uncertainty in previously published temperatur
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 Survey effor
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 u
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 backgroun
Geophysical characterization of geothermal resources on the Umatilla Indian Reservation in northeast Oregon
During the summers of 2017 and 2020, our team collected gravity, ground magnetic (ATV and hiked traverses), paleomagnetic and rock property (density and susceptibility) data on the Umatilla Indian Reservation (UIR) in northeast Oregon to aid in characterizing subsurface stratigraphy using 2D and 3D modeling methods. This data was integrated with conductance surfaces from a 3D magnetotelluic model
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics Data
This package contains gravity and magnetics data and products generated for the Nevada Machine Learning (NVML) project (DE-FOA-0001956). Data products contained in this release consist of grids and vector data. Grids include: primary anomaly maps (isostatic and PSG), match-filtered maps, horizontal gradient (HG) maps, confidence maps, and maps showing density of specific key structural features. T
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Heat Flow Data
This package contains a map surface that depicts the estimated spatial variation of conductive heat flow (mW/m?) in a portion of northern Nevada, the extent of the ?Nevada Machine Learning Project? (DE-EE0008762). It was generated using well locations that had an estimated heat flow value from a measured thermal gradient and thermal conductivity, mainly using data from Southern Methodist Universit
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Slip and Dilation Tendency Data
This package contains data in a portion of northern Nevada, the extent of the ?Nevada Machine Learning Project? (DE-EE0008762). Slip tendency (TS) and dilation tendency (TD) were calculated for the all the faults in the Nevada ML study area. TS is the ratio between the shear components of the stress tensor and the normal components of the stress tensor acting on a fault plane. TD is the ratio of a
USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics, Heat Flow, Slip and Dilation Tendency
This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project (DE-FOA-0001956), with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geophysics, heat flow, and fault dilation and slip tendencies) that cover a large portion of northern Nevad
Digital data from USGS OFR 83-250: Selected data for low-temperature (less than 90 degrees C) geothermal systems in the United States; reference data for U.S. Geological Survey Circular 892
The data in the csv and text files provided in this release are an update to the data tables originally published in USGS Open-File Report (OFR) 83-250 (https://doi.org/10.3133/cir892). Those data were published as paper tables and have until now only been available as pdf image documents that were not machine readable. USGS OFR 83-250 presented data for 2071 geothermal sites which are representat
Snake River Plain Play Fairway Analysis Phase 2 Favorability Model (DE EE0006733)
This data release contains all digital geographic data used and produced by the Snake River Plain Play Fairway Analysis (DE EE0006733) for Phase 2 (ArcGIS shapefiles and raster files) as well as the model processing script, tables, and documentation used to generate data outputs. Phase 2 examines two subset areas of the Phase 1 study area, Mountain Home and Camas Prairie. Brief descriptions of dat
Snake River Plain Play Fairway Analysis Phase 1 Favorability Model (DE EE0006733)
This data release contains all digital geographic data used and produced by the Snake River Plain Play Fairway Analysis (DE EE0006733) for Phase 1 (ArcGIS shapefiles and raster files) as well as the model processing script, tables, and documentation used to generate data outputs. Brief descriptions of data layers are in the metadata of GIS files, greater detail is available in the Larger Work, the
Filter Total Items: 13
Updated three-dimensional temperature maps for the Great Basin, USA
As part of the periodic update of the geothermal energy assessments for the USA (e.g., last update by Williams and others, 2008), a new three-dimensional temperature map has been constructed for the Great Basin, USA. Williams and DeAngelo (2011) identified uncertainty in estimates of conductive heat flow near land surface as the largest contributor to uncertainty in previously published temperatur
Authors
Erick Burns, Jacob DeAngelo, Colin F. Williams
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
Authors
Stanley Paul Mordensky, Erick Burns, Jacob DeAngelo, John Lipor
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
Authors
Stanley Paul Mordensky, Erick Burns, John Lipor, Jacob DeAngelo
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
Authors
Pascal D. Caraccioli, Stanley Paul Mordensky, Cary R. Lindsey, Jacob DeAngelo, Erick Burns, John Lipor
Geothermal Play Fairway Analysis, Part 2: GIS methodology
Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal re
Authors
Jacob DeAngelo, John W. Shervais, Jonathan M.G. Glen, Dennis Nielson, Sabodh Garg, Patrick Dobson, Erika Gasperikova, Eric Sonnenthal, Lee M. Liberty, Drew Lorenz Siler, James P. Evans
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 NVML data,
Authors
Jacob DeAngelo, Erick Burns, Stanley Paul Mordensky, Cary Ruth Lindsey
Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho
The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal re
Authors
John W. Shervais, Jacob DeAngelo, Jonathan M.G. Glen, Dennis L. Nielson, Sabodh Garg, Patrick Dobson, Erika Gasperikova, Eric Sonnenthal, Lee M. Liberty, Dennis L. Newell, Drew Lorenz Siler, James P. Evans
Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis
Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that prod
Authors
Connor M. Smith, James E. Faulds, Stephen C. Brown, Mark Coolbaugh, Jacob DeAngelo, Jonathan M.G. Glen, Erick Burns, Drew Lorenz Siler, Sven Treitel, Eli Mlawsky, Michael Fehler, Chen Gu, Bridget F. Ayling
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 err
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
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
Authors
Jacob DeAngelo, Erick R. Burns, Emilie Gentry, Joseph F. Batir, Cary Ruth Lindsey, Stanley Paul Mordensky
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
Authors
Stanley Paul Mordensky, John Lipor, Jacob DeAngelo, Erick R. Burns, Cary Ruth Lindsey
Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada
A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a pro
Authors
Stephen C. Brown, Mark F. Coolbaugh, Jacob DeAngelo, James E. Faulds, Michael Fehler, Chen Gu, John H. Queen, Sven Treitel, Connor M. Smith, Eli Mlawsky