Conference Papers
Science Quality and Integrity
The USGS provides unbiased, objective, and impartial scientific information upon which our audiences, including resource managers, planners, and other entities, rely.
The USGS provides unbiased, objective, and impartial scientific information upon which our audiences, including resource managers, planners, and other entities, rely.
Browse almost 5,000 conference papers authored by our scientists and refine search by topic, location, year, and advanced search.
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SUAS and machine learning integration in waterfowl population surveys SUAS and machine learning integration in waterfowl population surveys
The rapid technological development of small Unmanned Aircraft Systems (sUAS) has led to an increase in capabilities of aerial image collection and analysis for monitoring a variety of wildlife species including waterfowl. Biologists mainly rely on conducting ocular surveys from fixed-wing aircraft or helicopters to estimate waterfowl abundance. sUAS provide an alternative that is safer...
Authors
Z. Tang, Y. Zhang, Y. Q. Wang, Y. Shang, R. Viegut, Elisabeth B. Webb, Andy Raedeke, J. Sartwell
Digital Twin Earth - Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators Digital Twin Earth - Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators
Developing fast and accurate surrogates for physics-based coastal and ocean mod- els is an urgent need due to the coastal flood risk under accelerating sea level rise, and the computational expense of deterministic numerical models. For this purpose, we develop the first digital twin of Earth coastlines with new physics-informed machine learning techniques extending the state-of-art...
Authors
P. Jiang, N. Meinert, H. Jordao, C. Weisser, S. Holgate, A. Lavin, B. Lutjens, D. Newman, H. Wainright, C. Walker, Patrick L. Barnard
Physics-guided machine learning from simulation data: An application in modeling lake and river systems Physics-guided machine learning from simulation data: An application in modeling lake and river systems
This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output...
Authors
Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison P. Appling, Samantha K. Oliver, Jordan Read
Data-driven prospectivity modelling of sediment-hosted mineral systems Data-driven prospectivity modelling of sediment-hosted mineral systems
Mississippi Valley-type (MVT) and clastic-dominated (CD) deposits are important sources for Zn, Pb, Ag, and Cd as well as the critical elements Ga, Ge, In, and Sb. However, mapping the drivers, sources, pathways, and traps of MVT and CD deposits within the much larger and mostly unmineralized sedimentary basins remain some of the least understood aspects of these mineral systems. Herein...
Authors
Christopher J.M. Lawley, Anne E. McCafferty, Garth E. Graham, Michael G. Gadd, David L. Huston, Karen D. Kelley, Karol Czarnota, Suzanne Paradis, Jan M. Peter, Nathan Hayward, Mike Barlow, Poul Emsbo, Joshua A. Coyan, Carma A. San Juan
Exploring basin-scale relations and unsupervised classification to quantify and automate the definition of assessment units in USGS continuous oil and gas resource assessments Exploring basin-scale relations and unsupervised classification to quantify and automate the definition of assessment units in USGS continuous oil and gas resource assessments
The U.S. Geological Survey (USGS) assesses potential for undiscovered, technically recoverable oil and gas resources in priority geologic provinces and quantifies resource volume estimates within subdivisions called assessment units (AUs). AU boundaries are defined by USGS geologists using quantitative and qualitative geologic information. Variables contained in IHS Markit’s well and...
Authors
Chilisa Marie Shorten, Scott A. Kinney, Katherine J. Whidden
Changes in liquefaction severity in the San Francisco Bay Area with sea-level rise Changes in liquefaction severity in the San Francisco Bay Area with sea-level rise
This paper studies the impacts of sea-level rise on liquefaction triggering and severity around the San Francisco Bay Area, California, for the M 7.0 “HayWired” earthquake scenario along the Hayward fault. This work emerged from stakeholder engagement for the US Geological Survey releases of the HayWired earthquake scenario and the Coastal Storm Modeling System projects, in which local...
Authors
Alex R. Grant, Anne Wein, Kevin M. Befus, Juliette Finzi-Hart, Mike Frame, Rachel Volentine, Patrick L. Barnard, Keith L. Knudsen