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Abstract: Using Machine Learning Explainability Techniques to Examine Drivers of Ground Magnetic Field Localization (AGU25)

Using Machine Learning Explainability Techniques to Examine Drivers of Ground Magnetic Field Localization

Michael Coughlan1, Amy M Keesee2, Victor A Pinto3,4, Raman Mukundan5, Jose Paulo Marchezi6, Mayowa Adewuyi7, Joel Tibbetts7, Jeremiah W Johnson8, Hyunju KIM Connor9 and Donald L Hampton10, (1)University of New Hampshire Main Campus, Durham, NH, United States, (2)University of New Hampshire, Physics and Space Science Center, Durham, United States, (3)Universidad de Santiago de Chile, Physics, Santiago, Chile, (4)University of Santiago Chile, Santiago, Chile, (5)University of New Hampshire, Department of Physics and Astronomy, Durham, United States, (6)CRS/INPE - UFSM, Santa Maria, Brazil, (7)University of New Hampshire Main Campus, Durham, United States, (8)University of New Hampshire, Applied Engineering and Sciences, Manchester, United States, (9)NASA Goddard Space Flight Center, Greenbelt, United States, (10)University of Alaska Fairbanks, Fairbanks, AK, United States
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