About

About the sponsor

The U.S. Department of Energy’s (DOE) Office of Geothermal (OG) works to reduce costs and risks associated with geothermal development by supporting innovative technologies that address key exploration and operational challenges.

About the data

The U.S. Geological Survey (USGS) and the Department of Energy (DOE) have collaborated to acquire high-resolution airborne magnetic and radiometric data over northern and western Nevada and eastern California to support geologic and geophysical mapping and modeling that will assist geothermal and critical mineral studies. The surveys, referred to as GeoDAWN (Geoscience Data Acquisition for Western Nevada), span areas of major resource potential associated with the Walker Lane and western Great Basin.

They were conducted under the USGS’s Earth Mapping Resources Initiative (EarthMRI), with support from the DOE’s Office of Geothermal (OG), and involved acquisition of aeroradiometric and aeromagnetic data that provide key information on surface geology and soil composition and subsurface structure and geology, respectively. Coordinated with this effort was the collection of airborne lidar (light detection and ranging) data (conducted through the USGS 3D Elevation Program (3DEP)) that yielded detailed surface topographic models of the terrain over a similar extent spanned by the geophysical surveys.

About the task

A geological fault is a fracture or discontinuity in Earth’s crust where two blocks of rock have undergone measurable displacement, whether vertical, horizontal, or a combination of both. These fractures can range from tiny features to vast systems spanning hundreds of miles, such as plate boundary faults that generate major earthquakes. The plane along which the displacement occurs is called the fault plane, and where this plane intersects the surface is known as the fault trace. When multiple related fractures exist in close proximity, geologists refer to this as a fault zone.

Geologists detect and delineate faults using a combination of traditional field observations, geophysical surveys, and advanced remote sensing and imaging techniques. In the field, they search for surface expressions such as fault scarps, offset rock layers, and polished slickensides. Subsurface methods like seismic reflection profiling can visualize hidden fault planes, especially where dense geophysical contrasts exist. Gravity and magnetic surveys also help infer the presence of faults through density or magnetic anomalies. From above, remote sensing data and elevation data reveal linear features and subtle ground movements indicative of fault activity. Furthermore, modern computational methods including edge detection, Hough transforms, and deep learning on seismic or topographic datasets further enhance automated fault mapping.

Detecting faults accurately is vital across multiple domains. In earthquake science and hazard assessment, recognizing active faults—those that have moved within the past ~11,700 years—is crucial for predicting seismic risks and enforcing safe land-use planning near fault lines. In engineering and construction, identifying fault zones guides the placement of infrastructure, ensuring stability and mitigating ground rupture hazards. Faults also play key roles in natural resource processes: they can act as conduits for fluids, concentrating minerals, hydrocarbons, geothermal energy, and groundwater, thus guiding exploration and extraction strategies.

Additional information

• Mattéo, L., Manighetti, I., Tarabalka, Y., Gaucel, J.-M., van den Ende, M., Mercier, A., et al. (2021). Automatic fault mapping in remote optical images and topographic data with deep learning. Journal of Geophysical Research: Solid Earth, 126, e2020JB021269. https://doi.org/10.1029/2020JB021269
• Hermant, B., Kiersnowski, L., & Bellanger, M. (2025, February). Using deep learning to map Quaternary faults in Western USA. In Proceedings of the 50th Workshop on Geothermal Reservoir Engineering (Stanford, CA). Stanford University. https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2025/Hermant.pdf