Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1699-2021
https://doi.org/10.5194/gmd-14-1699-2021
Development and technical paper
 | 
26 Mar 2021
Development and technical paper |  | 26 Mar 2021

Effects of transient processes for thermal simulations of the Central European Basin

Denise Degen and Mauro Cacace

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Cited articles

Alger, B., Andrš, D., Carlsen, R. W., Gaston, D. R., Kong, F., Lindsay, A. D., Miller, J. M., Permann, C. J., Peterson, J. W., Slaughter, A. E., and Stogner, R.: MOOSE Web page, available at: https://mooseframework.org, last access: 20 June 2020. a, b
Baş, D. and Boyacı, I. H.: Modeling and optimization I: Usability of response surface methodology, J. Food Eng., 78, 836–845, 2007. a, b
Bayer, U., Scheck, M., and Koehler, M.: Modeling of the 3D thermal field in the northeast German basin, Geol. Rundsch., 86, 241–251, 1997. a
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., and Escaleira, L. A.: Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta, 76, 965–977, 2008. a, b
Clauser, C.: A climatic correction on temperature gradients using surface-temperature series of various periods, Tectonophysics, 103, 33–46, 1984. a
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In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
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