EGU2020-13606, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Bayesian approach for thermal history reconstruction in basin modeling

Andrea Licciardi1, Kerry Gallagher2, and Stephen Anthony Clark3
Andrea Licciardi et al.
  • 1Université Côte d'Azur, Geoazur, France (
  • 2Géosciences, Université de Rennes 1, France
  • 3Statoil Research, Trondheim, Norway

Vitrinite reflectance and apatite fission track) and borehole data (bottom hole temperature and porosi ty) for thermal history reconstruction in basin modeling.  The approach implements a trans-dimensional and hierarchical Bayesian formulation with a reversible jump Markov chain Monte  Carlo (rjMcMC) algorithm. The main objective of the inverse problem is to infer the heat flow history below a borehole given the data and a set of geological constraints (e.g. stratigraphy , burial histories and physical properties of the sediments).  The algorithm incorporat es an adaptive, data-driven parametrization of the heat flow history, and allows for automatic estimation of relative importance of each data type in the inversion and for robust quantification of parameter uncertainties and trade-offs. In addition, the algorithm deals with uncertainties on the imposed geological constraints in two ways. First, the amount of erosion and timing of an erosional event are explicitly treated as independent parameters to be inferred from the data. Second, uncertainties on compaction parameters and surface temperature histo ry are directly propagated 
into the final probabilistic solution.

How to cite: Licciardi, A., Gallagher, K., and Clark, S. A.: A Bayesian approach for thermal history reconstruction in basin modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13606,, 2020


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