Geology topics

John H. Queen

Publications and source records attributed to John H. Queen.

3 recordsLinked to original sources

USGS2021

Three-dimensional geologic map of the Brady geothermal area, Nevada

The three-dimensional (3D) geologic map characterizes the subsurface in the Brady geothermal area in the northern Hot Springs Mountains of northwestern Nevada. We built the 3D map by integrating the results from detailed geologic mapping, seismic-reflection, potential-field-geophysical, and lithologic well-logging investigations completed in the study area. This effort was undertaken to investigate the geologic structure in the geothermal field and geologic controls on hydrothermal circulation. This characterization of the controls on hydrothermal circulation is applicable to the assessment, exploration, and development of analogous geothermal resources. The 3D map area is 4 kilometers (km) wide along the west-northwest-to-east-southeast axis and 6 km wide along the north-northeast-to-south-southwest axis and extends to 1.0 km below sea level, approximately 2.5 km below the land surface. We describe the geologic units and structures in the map area, discuss the methods used to integrate the geologic and geophysical information into the 3D geologic interpretation, and calculate several geologic factors that may aid in our understanding of hydrothermal circulation. Map sheet 1 provides horizontal and vertical section views and oblique perspective views from several angles of the 3D geologic map. Map sheet 2 provides views of derivative calculations based on the 3D geologic data, 3D density of faults, 3D density of fault intersections and terminations, slip tendency on 3D faults, and dilation tendency on 3D faults. We provide digital data for all elements of the map, such as individual 3D fault and stratigraphic surfaces, 3D fault density, 3D fault intersection density, 3D slip tendency on fault surfaces, and 3D dilation tendency on fault surfaces. A brief movie displaying the 3D map is available at https://doi.org/10.3133/sim3469 .

Nevada↗

USGS2020

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 geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. Finally, ML will be applied to define new workflows using the enhanced and additional datasets. An algorithmic approach that empirically learns to estimate weights of influence for diverse parameters can potentially scale and perform better than the play fairway analysis. Initial work on this project has involved 1) evaluating potential positive and negative training sites, 2) transformation of datasets into formats suitable for ML, and 3) initial development and testing of ML techniques.

Nevada↗

USGS2020

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 probability map. During the first year of this project we have encountered several issues particular to using geological and geophysical data sets with these tools. Through an illustrative example we develop a promising workflow for future use as more data become available and are analyzed.

Nevada↗