Combining thermochronological data with 3D probabilistic kinematic modeling of the Bavarian Subalpine Molasse for uncertainty estimation
- 1Department of Environment and Biodiversity, Paris-Lodron University Salzburg, Salzburg, Austria (brisson@cgre.rwth-aachen.de)
- 2Computational Geoscience, Geothermal, and Reservoir Geophysics, RWTH Aachen University, Germany
Thermokinematic modeling often relies on prescribed geometric and kinematic models at depth without considering their uncertainty. This does not allow for the proper quantification of the relative contributions of different drivers to the exhumation signal. Considering uncertainty of structural data in thermokinematic models would help understanding how much shortening associated with the observed cooling signal occurred.
The aim of this work is to combine probabilistic structural modeling with thermokinematic forward simulations to investigate the related uncertainties. For this purpose, the Bavarian Subalpine Molasse is particularly suited as a test case, as it connects the Alpine orogen with its foreland, and should shed light on the strain distributions during the latest stages of Alpine mountain building. 3D implicit geological modeling of the Bavarian Subalpine Molasse triangle zone was carried out and combined with a systematic random sampling approach to automatically generate an ensemble of geometric models in the range of assigned uncertainties. In addition, a probabilistic 3D kinematic forward model is constructed. A link can then be obtained between kinematic model parameters and present-day geometry in comparison with field observations at the surface and also in comparison to the range of geometric uncertainties in the 3D geological model. Results show that the uncertainty (quantified as information entropy) is distributed as a function of structural complexity, depth, and data density throughout the geometric model, and additionally where fault slip ranges are large in the kinematic model.
In a next step, these models are combined with a thermokinematic forward model to integrate thermochronological measurements from previous campaigns, and eventually own measurements, to obtain an integrated picture of foreland evolution and associated uncertainties over space and time.
How to cite: Brisson, S., Ziegler, J., Chudalla, N., Wellmann, F., and von Hagke, C.: Combining thermochronological data with 3D probabilistic kinematic modeling of the Bavarian Subalpine Molasse for uncertainty estimation, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13340, https://doi.org/10.5194/egusphere-egu23-13340, 2023.
