Characterizing the subglacial environment with stochastic methods by Mickey MacKie

Mickey MacKie

Stanford | Rhoda Goldman Memorial

My research includes developing advanced geostatistical simulation methods and integrating geostatistics and other machine learning techniques with geophysical knowledge to characterize subglacial conditions. I am also interested in the role of topographic and geologic controls on ice sheet dynamics and evolution. My goal is to advance the use of machine learning methods for studying glaciers and make these tools accessible to the broader cryosphere community.


I am a geophysical glaciologist who works at the intersection of observational geophysics, glaciology, and machine learning to study subsurface ice sheet conditions in order to investigate ice sheet history, ice sheet dynamics, and sea level rise. My research focus is using radar observations and machine learning techniques to develop novel methods for estimating the topographic, geologic, and hydrological conditions beneath ice sheets. In previous projects, I have used stochastic simulation to produce the first ensemble realizations of subglacial topography. By using stochastically modeled topography in subglacial hydrological modeling, I have shown that subglacial water drainage patterns have much greater uncertainty and spatial variability than previously recognized. My recent work has focused on Thwaites Glacier, which is experiencing significant ice loss, threatening the stability of the West Antarctic Ice Sheet. In this project, I am producing topographic and geologic maps for Thwaites Glacier. Our results reveal complex topographic and geologic conditions that will prove critical for investigating topographic and geologic controls on ice sheet stability.


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