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S68. Multiscale geophysical characterization of mineral and geothermal systems in the Great Basin

We seek to develop 3D geophysical models of the Great Basin and apply novel methods to characterize relationships between near-surface hydrothermal and mineral systems and deep structures, heat sources, and mineral-bearing fluids. The opportunity will explore strategies to translate geophysical knowledge into resource assessment methodologies and to improve process models for resource formation.

Research Opportunity Description

Background: The Great Basin, within the Basin and Range (B&R) province, is fertile ground for undiscovered geothermal and mineral resources critical to national goals of energy independence, reduced import reliance on critical minerals, and reduction in greenhouse gas emissions. The Great Basin hosts a wealth of critical-mineral resources including lithium, manganese, tungsten, and vanadium as well as potential for numerous other commodities. The basin further hosts vast geothermal resources with the largest concentration of active geothermal operations in the U.S., including some of the most favorable areas for undiscovered systems. Both mineral and energy resources result from deep-crustal fluid flow and mantle fluxes through the extended crust of the B&R province.  Previous studies have shown the location and distribution of resources to be controlled by both top-down (e.g., folding and faulting) and bottom-up (thermal and rheologic structure at mid- to lower-crustal levels) processes (e.g., Peacock and Siler, 2021; Kirkby et al. 2022). 

The USGS has collected and is collecting various multi-scale geophysical data across the Great Basin, including airborne magnetic, electromagnetic, and hyperspectral data as well as gravity, magnetotelluric, and seismic data, resulting in large datasets that have the potential to characterize known resources, to identify areas of interest for further investigation, and to inform quantitative resource assessments.  Recent and ongoing efforts to collect detailed data over vast swaths of the B&R (e.g., EarthMRI and GeoDAWN) focus on top-down information, providing an unprecedented quantity of untapped information. This is a ‘big data’ problem that requires the development of novel methods to mine, model, and understand these rich datasets. Perhaps equally important, such approaches can benefit from the systematic integration of deeper-sensing bottom-up datasets (e.g., magnetotelluric and passive-seismic data).

A high-level goal of this opportunity is to blend several promising strategies to do better than each strategy is capable of alone.  For example, performing joint or cooperative inversion of top-down and bottom-up geophysical data to which anomaly detection machine learning (ML) strategies are applied using explainable machine learning (XML). Each component has a unique value to the process: 1) joint or cooperative inversion incorporates multiple lines of evidence to improve predictive skill; 2) because mineral deposits and hydrothermal systems are few and small compared with the size of the Great Basin, ML strategies such as anomaly detection designed to find small but significant features are postulated to be a good approach; and 3) an emphasis on XML is anticipated to result in sequential improvement of predictions as the most important features are identified and refined. 

Research Opportunity: The objectives of this opportunity are to develop multiscale 3D geophysical models of the Great Basin and to develop novel interpretive methods using statistics, machine learning, or other approaches to characterize and inform assessment of critical-mineral and geothermal resources. The opportunity will be built upon new and existing ground and airborne geophysical data and seeks to: (1) develop, validate, and improve process models for resource formation and (2) explore strategies and approaches to translate geophysical knowledge into resource assessment methodologies.

Candidates are invited, but not limited, to explore geophysical methods with dynamic spatial resolution like magnetotellurics and passive-seismic methods in order to develop multiscale 3D geophysical models. The candidate is encouraged to explore various modeling approaches including: 

  • Joint or cooperative inversion using Bayesian or deterministic approaches.
  • Physical property or structural similarity approaches to linking diverse geophysical data sand models within a joint inversion framework.
  • Multiscale inversion modeling using structured or adaptive meshes.

Successful applicants are expected to use the developed 3D geophysical models to explore one or more of the following research questions using a wide range of strategies and tools:  

  • How is bottom-up control of extensional tectonics related to top-down control of near-surface resources? Can this relationship be exploited to identify areas of interest using statistical analysis or explained machine learning? 
  • Can anomaly detection strategies find sparse features distinct from the surrounding area?  Recognizing that features of interest (e.g., mineral deposits, hydrothermal systems) are sparse and unique, analyses can be optimized for this sparse outlier problem.
  • Can adaptive methods that explore the models identify where to spend future resources most cost effectively?
  • Can development of image processing strategies (e.g., convolutional neural networks) identify ‘interesting’ patterns associated with structurally controlled conduits (e.g., mineral emplacement, hydrothermal energy, hydrogen fluxes, etc.)?

Proposals should be accompanied by a work plan, a general work schedule, and a budget covering needed travel (included to conferences), equipment, computers, deployment and operation of instruments, and/or field work. Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.



Kirkby, A., Czarnota, K., Huston, D.L., Champion, D.C., Doublier, M.P., Bedrosian, P.A., Duan, J. and Heinson, G. (2022) Lithospheric conductors reveal source regions of convergent margin mineral systems. Scientific Reports, 12, 8190,

Peacock, J. R., & Siler, D. L. (2021). Bottom-up and top-down control on hydrothermal resources in the Great Basin: An example from Gabbs Valley, Nevada. Geophysical Research Letters, 48, e2021GL095009,


Proposed Duty Station(s)

Lakewood, Colorado; Moffett Field, California


Areas of PhD 

Geophysics, earth science, physics, geology, statistics, computer science or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).



Applicants must meet the qualifications for:  Research Geophysicist.

(This type of research is performed by those who have backgrounds for the occupations stated above.  However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)