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Gradient boosting regression algorithm for prediction of geothermal heat flow (in Antarctica)

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MachineLearning-GHF-Antarctica

Gradient boosting regression algorithm for prediction of geothermal heat flow in Antarctica -- Still in progress

RESULTS

Publication: Lösing, M.; Ebbing, J. (2021): Predicting Geothermal Heat Flow in Antarctica With a Machine Learning Approach. Journal of Geophysical Research: Solid Earth, 126(6), https://doi.org/10.1029/2020JB021499

Resulting predicted heat flow for Antarctica with minimum, maximum, and maximum absolute difference values are available in file 'HF_Min_Max_MaxAbs.csv'.

HEAT FLOW data is from:

FEATURES are from:

Global Moho: Szwillus, W., Afonso, J. C. C., Ebbing, J., & Mooney, W. D. (2019). Global crustal thickness and velocity structure from geostatistical analysis of seismic data. Journal of Geophysical Research: Solid Earth, 124, 1626– 1652. https://doi.org/10.1029/2018JB016593

Antarctic Mohos and LABs:

Pappa, F., Ebbing, J., Ferraccioli, F., & van der Wal, W. (2019). Modeling satellite gravity gradient data to derive density, temperature, and viscosity structure of the antarctic lithosphere. Journal of Geophysical Research: Solid Earth, 124, 12053– 12076. https://doi.org/10.1029/2019JB017997

An, M., Wiens, D. A., Zhao, Y., Feng, M., Nyblade, A., Kanao, M., Li, Y., Maggi, A., and Lévêque, J.-J. (2015), Temperature, lithosphere-asthenosphere boundary, and heat flux beneath the Antarctic Plate inferred from seismic velocities, J. Geophys. Res. Solid Earth, 120, 8720– 8742, doi:10.1002/2015JB011917

Shen, W., Wiens, D. A., Anandakrishnan, S., Aster, R. C., Gerstoft, P., Bromirski, P. D., et al. (2018). The crust and upper mantle structure of central and West Antarctica from Bayesian inversion of Rayleigh wave and receiver functions. Journal of Geophysical Research: Solid Earth, 123, 7824– 7849. https://doi.org/10.1029/2017JB015346 Australian Moho: Kennett & Chopping (2018)

African Moho: M. Youssof, H. Thybo, I.M. Artemieva, A. Levander, (2013). Moho depth and crustal composition in Southern Africa. Tectonophysics, 609, 267-287, https://doi.org/10.1016/j.tecto.2013.09.001

Global LAB: Juan Carlos Afonso, Farshad Salajegheh, Wolfgang Szwillus, Jorg Ebbing, Carmen Gaina, A global reference model of the lithosphere and upper mantle from joint inversion and analysis of multiple data sets, Geophysical Journal International, Volume 217, Issue 3, June 2019, Pages 1602–1628, https://doi.org/10.1093/gji/ggz094

Topography:

Christian Hirt, Moritz Rexer, (2015). Earth2014: 1 arc-min shape, topography, bedrock and ice-sheet models – Available as gridded data and degree-10,800 spherical harmonics. International Journal of Applied Earth Observation and Geoinformation, 39, 103-112, https://doi.org/10.1016/j.jag.2015.03.001

Morlighem, M., Rignot, E., Binder, T. et al. Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet. Nat. Geosci. 13, 132–137 (2020). https://doi.org/10.1038/s41561-019-0510-8

Susceptibility: Hemant, K., and Maus, S. (2005), Geological modeling of the new CHAMP magnetic anomaly maps using a geographical information system technique, J. Geophys. Res., 110, B12103, doi:10.1029/2005JB003837

Tectonic units: Schaeffer A.J., Lebedev S. (2015) Global Heterogeneity of the Lithosphere and Underlying Mantle: A Seismological Appraisal Based on Multimode Surface-Wave Dispersion Analysis, Shear-Velocity Tomography, and Tectonic Regionalization. In: Khan A., Deschamps F. (eds) The Earth's Heterogeneous Mantle. Springer Geophysics. Springer, Cham. https://doi.org/10.1007/978-3-319-15627-9_1

Mean curvature from grav. gradients: Ebbing, J., Haas, P., Ferraccioli, F., Pappa, F., Szwillus, W., & Bouman, J. (2018). Earth tectonics as seen by GOCE-Enhanced satellite gravity gradient imaging. Scientific reports, 8(1), 1-9.

Vertical magnetic component: Ebbing, J., Dilixiati, Y., Haas, P. et al. East Antarctica magnetically linked to its ancient neighbours in Gondwana. Sci Rep 11, 5513 (2021). https://doi.org/10.1038/s41598-021-84834-1

Distance to ridges, trenches, and transform faults: Coffin, Millard F., Lisa M. Gahagan, and Lawrence A. Lawver. Present-day plate boundary digital data compilation. Institute for Geophysics, 1997.

Distance to young rifts: Sengör, AM Celal, and Boris A. Natal'in. "Rifts of the world." Geological Society of America Special Papers 352 (2001): 389-482. Digitized by B. Goutorbe, J. Poort, F. Lucazeau, S. Raillard, Global heat flow trends resolved from multiple geological and geophysical proxies, Geophysical Journal International, Volume 187, Issue 3, December 2011, Pages 1405–1419, https://doi.org/10.1111/j.1365-246X.2011.05228.x

Distance to volcanoes:

Global Volcanism Program, 2013. Volcanoes of the World, v. 4.9.1 (17 Sep 2020). Venzke, E (ed.). Smithsonian Institution. Downloaded 04 Jun 2020. https://doi.org/10.5479/si.GVP.VOTW4-2013

Maximillian van Wyk de Vries, Robert G. Bingham and Andrew S. Hein. Geological Society, London, Special Publications, 461, 231-248, 29 May 2017, https://doi.org/10.1144/SP461.7

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Gradient boosting regression algorithm for prediction of geothermal heat flow (in Antarctica)

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