Prediction of geothermal heat flow in Antarctica based on deep neural network
Chu Dongfang
Fan Xiaopeng
Li Jing
Bai Lige
Wang Zhuo
Guo Jingxue
Abstract：The Antarctic ice sheet plays an increasingly significant regulating role in the global climate system,where geothermal heat flow(GHF)serves as a critical indicator of basal thermal conditions.Understanding its spa-tial distribution is essential for studying its dynamic evolution.Current GHF measurements in Antarctica remain limited to merely 52 data points due to extreme cold geological conditions and prohibitive drilling costs.Existing GHF estimates based on geological and geophysical data exhibit substantial uncertainties and fail to account for non-linear heat transfer processes in the bedrock.Deep learning algorithms demonstrate superior predictive capabilities for sparse data interpolation by capturing coupled relationships between GHF and multi-source data features.We develop a deep neural network(DNN)integrated with a high-dimensional uncertainty quantification framework to predict Antarctic GHF distribution.Utilizing over 4000 global GHF observations and 22 geological and geophysi-cal features(including crustal thickness,gravity/magnetic anomalies,and Moho depth)as training data,we validate the model against known GHF points in Australia and Antarctica.The uncertainty quantification method reorga-nizes the training dataset through correlation,sensitivity,and principal component analysis,combined with DNN model to determine optimal GHF distribution.Prediction results reveal the GHF range of 24-103 mW/m2 with a mean value of 60.1 mW/m2,showing a 10.05%error relative to measured data.The Gamburtsev Subglacial Moun-tains exhibit significant high GHF anomalies(50-74 mW/m2).Uncertainty quantification confirms high confid-ence levels for these GHF predictions.Our model successfully interprets spatial heterogeneity characteristics of Antarctic crustal thermal structure,and the prediction results can be further applied to model Antarctic-scale ice sheet dynamics or thermodynamics,provide reliable data support for studies on ice sheet motion and subglacial lake distribution.
Keywords：deep neural networkgeothermal heat flowAntarcticauncertainty quantification
Publication Date：2026-03-31
Online Publishing Date：2025-11-07(First online date of this platform, not the publication date of the document)
Pages：14( 177-190 )