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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.981899/full |
| _version_ | 1811254799900868608 |
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| author | M. Al-Aghbary M. Al-Aghbary M. Sobh M. Sobh M. Sobh C. Gerhards |
| author_facet | M. Al-Aghbary M. Al-Aghbary M. Sobh M. Sobh M. Sobh C. Gerhards |
| author_sort | M. Al-Aghbary |
| collection | DOAJ |
| description | 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 continental-scale models. In this study, we generate a geothermal heat flow model over Africa using random forest regression, originally based on sixteen different geophysical and geological quantities. Due to an intrinsic importance ranking of the observables, the number of observables used for the final GHF model has been reduced to eleven (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 et al., Geochem. Geophys. Geosyst. 2019, 20, 4001–4024). 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 observables is discussed. |
| first_indexed | 2024-04-12T17:14:19Z |
| format | Article |
| id | doaj.art-72e26ed7edae4ecfba50f68928dd48e6 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-12T17:14:19Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-72e26ed7edae4ecfba50f68928dd48e62022-12-22T03:23:43ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-09-011010.3389/feart.2022.981899981899A geothermal heat flow model of Africa based on random forest regressionM. Al-Aghbary0M. Al-Aghbary1M. Sobh 2M. Sobh 3M. Sobh 4C. Gerhards 5Institute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Freiberg, GermanyGeophysical Laboratory, Centre d’Etudes et de Recherche de Djibouti, Djibouti, DjiboutiInstitute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Freiberg, GermanyNational Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Cairo, EgyptInstitute of Earth and Environmental Sciences, Albert-Ludwigs-Universität Freiburg, Breisgau, GermanyInstitute of Geophysics and Geoinformatics, TU Bergakademie Freiberg, Freiberg, GermanyGeothermal 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 continental-scale models. In this study, we generate a geothermal heat flow model over Africa using random forest regression, originally based on sixteen different geophysical and geological quantities. Due to an intrinsic importance ranking of the observables, the number of observables used for the final GHF model has been reduced to eleven (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 et al., Geochem. Geophys. Geosyst. 2019, 20, 4001–4024). 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 observables is discussed.https://www.frontiersin.org/articles/10.3389/feart.2022.981899/fullgeothermal heat flowrandom forest regressionmachine learningafrican continentMultivariate analysis |
| spellingShingle | M. Al-Aghbary M. Al-Aghbary M. Sobh M. Sobh M. Sobh C. Gerhards A geothermal heat flow model of Africa based on random forest regression Frontiers in Earth Science geothermal heat flow random forest regression machine learning african continent Multivariate analysis |
| title | A geothermal heat flow model of Africa based on random forest regression |
| title_full | A geothermal heat flow model of Africa based on random forest regression |
| title_fullStr | A geothermal heat flow model of Africa based on random forest regression |
| title_full_unstemmed | A geothermal heat flow model of Africa based on random forest regression |
| title_short | A geothermal heat flow model of Africa based on random forest regression |
| title_sort | geothermal heat flow model of africa based on random forest regression |
| topic | geothermal heat flow random forest regression machine learning african continent Multivariate analysis |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.981899/full |
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