This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.3389/feart.2022.981899. This is version 1 of this Preprint.
A geothermal heat flow model of Africa based on Random Forest Regression
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Abstract
We generate a geothermal heat flow model over Africa using random forest regression based
on sixteen different geophysical and geological quantities (among them are Moho depth, Curie
temperature depth, gravity anomalies, topography, and seismic wave velocities). The training of the random forest is based on direct heat flow measurements collected in the compilation of Lucazeau (2019). The final model reveals structures that are consistent with existing regional geothermal heat flow information. It is interpreted with respect to the tectonic setup of Africa, and the influence of
the selection of training data and target observables is illustrated in the supplementary material
DOI
https://doi.org/10.31223/X5QW8X
Subjects
Physical Sciences and Mathematics
Keywords
geothermal heat flow, Random Forest Regression, machine learning, African continent
Dates
Published: 2022-07-06 21:06
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None
Data Availability (Reason not available):
Yes
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