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Predicting the Geothermal Heat Flux in Greenland: A Machine Learning Approach
被引:58
作者:
Rezvanbehbahani, Soroush
[1
,2
]
Stearns, Leigh A.
[1
,2
]
Kadivar, Amir
[3
]
Walker, J. Doug
[1
]
van der Veen, C. J.
[4
]
机构:
[1] Univ Kansas, Dept Geol, Lawrence, KS 66045 USA
[2] Univ Kansas, Ctr Remote Sensing Ice Sheets, Lawrence, KS 66045 USA
[3] McGill Univ, Dept Math & Stat, Quebec City, PQ, Canada
[4] Univ Kansas, Dept Geog & Atmospher Sci, Lawrence, KS 66045 USA
关键词:
Greenland ice sheet;
geothermal heat flux;
machine learning;
BASAL MELT;
ICE;
MODEL;
FLOW;
TEMPERATURES;
LITHOSPHERE;
CRUST;
D O I:
10.1002/2017GL075661
中图分类号:
P [天文学、地球科学];
学科分类号:
07 ;
摘要:
Geothermal heat flux (GHF) is a crucial boundary condition for making accurate predictions of ice sheet mass loss, yet it is poorly known in Greenland due to inaccessibility of the bedrock. Here we use a machine learning algorithm on a large collection of relevant geologic features and global GHF measurements and produce a GHF map of Greenland that we argue is within similar to 15% accuracy. The main features of our predicted GHF map include a large region with high GHF in central-north Greenland surrounding the NorthGRIP ice core site, and hot spots in the Jakobshavn Isbraecatchment, upstream of Petermann Gletscher, and near the terminus of Nioghalvfjerdsfjorden glacier. Our model also captures the trajectory of Greenland movement over the Icelandic plume by predicting a stripe of elevated GHF in central-east Greenland. Finally, we show that our model can produce substantially more accurate predictions if additional measurements of GHF in Greenland are provided.
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页码:12271 / 12279
页数:9
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