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Central Energy Resources Science Center

The United States Geological Survey (USGS), Central Energy Resources Science Center (CERSC) addresses national and global energy geoscience issues and conducts interdisciplinary research on energy systems.   You can explore the projects that are based here using the 'Science' option to the left.



Bipartisan Infrastructure Law helps fund new USGS facility at Colorado School of Mines, focused on energy and minerals research


MEDIA ADVISORY: Principal Deputy Assistant Secretary Brain, Director Applegate to Attend Groundbreaking for USGS Energy and Minerals Research Facility


EMRF Public Meeting Notice & Public Comment Period for Draft Environmental Assessment


Comparison of δ13C analyses of individual foraminifer (Orbulina universa) shells by secondary ion mass spectrometry and gas source mass spectrometry

Rationale: The use of secondary ion mass spectrometry (SIMS) to perform micrometer-scale in situ carbon isotope (δ13C) analyses of shells of marine microfossils called planktic foraminifers holds promise to explore calcification and ecological processes. The potential of this technique, however, cannot be realized without comparison to traditional whole-shell δ13C values measured by gas source mas
Jody Brae Wycech, Daniel Clay Kelly, Reinhard Kozdon, Akizumi Ishida, Kouki Kitajima, Howard J. Spero, John W. Valley

Evaluation of breeding distribution and chronology of North American scoters

North America's scoter species are poorly monitored relative to other waterfowl. Black Melanitta americana, surf M. perspicillata, and white-winged M. deglandi scoter abundance and trend estimates are thus uncertain in many parts of these species' ranges. The most extensive source of waterfowl abundance and distribution data in North America is the Waterfowl breeding population and habitat survey
Kristin Bianchini, Scott G. Gilliland, Alicia Berlin, Timothy D. Bowman, W. Sean Boyd, Susan E. W. De La Cruz, Daniel Esler, Joseph R. Evenson, Paul L. Flint, Christine Lepage, Scott R. McWilliams, Dustin E. Meattey, Jason E. Osenkowski, Matthew Perry, Jean-François Poulin, Eric T. Reed, Christian Roy, Jean-Pierre L. Savard, Lucas Savoy, Jason L Schamber, Caleb S. Spiegel, John Takekawa, David H. Ward, Mark L. Mallory

Machine learning application to assess occurrence and saturations of methane hydrate in marine deposits offshore India

Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume.
Leebyn Chong, Timothy Collett, C. Gabriel Creason, Yongkoo Seol, E.M. Myshakin