Unveiling Antarctica's Heat: A Review of Geothermal Heat Flow Estimation and the Rise of Machine Learning

Abstract

Antarctica, characterized by its extreme environment and sparse data availability, presents a formidable challenge for estimating geothermal heat flow, a key parameter influencing its geological and glaciological processes. This review paper analyzes existing literature surveys on predicting geothermal heat flow in Antarctica, focusing on various methodologies employed, data sources utilized, and challenges encountered. Highlight the transition from traditional techniques reliant on sparse direct measurements to adopting machine learning (ML) approaches leveraging diverse datasets. The review synthesizes insights from studies utilizing geological, geophysical, and remote sensing data, alongside advancements in ML algorithms, to improve the spatial resolution and accuracy of heat flow predictions. Through a comprehensive examination of the literature, identify key trends, knowledge gaps, and future directions for research in this critical domain.