Applying Machine Learning to Identify Super-Emitters from a Large-Scale Field Campaign in the Marcellus Shale

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ADS bibcode: 2017AGUFM.A41F2350C
year: 2017
Listed Authors: Caulton, D.; Lu, J. M.; Lane, H. M.; Buchholz, B.; Fitts, J. P.; Golston, L.; Guo, X.; Li, Q.; McSpiritt, J.; Pan, D.; Wendt, L. P.; Bou-Zeid, E.; Zondlo, M. A.
Listed Institutions: Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; National Metrology Institute of Germany, Braunschweig, Germany; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Columbia University of New York, Palisades, NY, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States

Applying Machine Learning to Identify Super-Emitters from a Large-Scale Field Campaign in the Marcellus Shale

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