A geothermal heat flow model of Africa based on random forest regression

A geothermal heat flow model of Africa based on random forest regression

Geothermal heat flow (GHF) data measured directly from boreholes are sparse. Purely physics-based models for geothermal heat flow prediction require various simplifications and are feasible only for few geophysical observables. Thus, data-driven multi-observable approaches need to be explored for co...

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Bibliographic Details
Main Authors: M. Al-Aghbary, M. Sobh , C. Gerhards 
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Earth Science
Subjects:
geothermal heat flow
random forest regression
machine learning
african continent
Multivariate analysis
Online Access:https://www.frontiersin.org/articles/10.3389/feart.2022.981899/full

Internet

https://www.frontiersin.org/articles/10.3389/feart.2022.981899/full

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