Jacob is a Cartographer with the Geology, Minerals, Energy, and Geophysics Science Center
Science and Products
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
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
Play fairway analysis in geothermal exploration: The Snake River plain volcanic province
The Snake River volcanic province (SRP) has long been considered a target for geothermal development. It overlies a thermal anomaly that extends deep into the mantle and represents one of the highest heat flow provinces in North America, but systematic exploration been hindered by lack of a conceptual model. Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that inte
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): 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): 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
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 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
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
Science and Products
- Publications
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 inMachine 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 proPlay fairway analysis in geothermal exploration: The Snake River plain volcanic province
The Snake River volcanic province (SRP) has long been considered a target for geothermal development. It overlies a thermal anomaly that extends deep into the mantle and represents one of the highest heat flow provinces in North America, but systematic exploration been hindered by lack of a conceptual model. Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that inte - Data
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 backgrounGeophysical 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 modelUSGS 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 UniversitUSGS 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 NevadUSGS 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 aUSGS 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. TDigital 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 representatSnake 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 datSnake 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