**Summary **

An inverse problem is the task often occurring in many branches of the geosciences, where the values of some model parameters describing the Earth must be obtained given noisy observations made at the surface. For exemple, an earth model depicting variations of density in the crust can be obtained from gravity neasurements. In all applications of inversion, assumptions are made about the unknown earth parameters (e.g. number of layers, level of smoothing) and data noise characteristics. A well known issue is that results can significantly depend on those assumptions. These quantities are often manually `tuned' by means of subjective trial-and-error procedures, and this prevents to accurately quantify uncertainties in the recovered earth model. A Bayesian approach allows these assumptions to be relaxed by reformulating the problem in a probabilistic framework. Probabilistic sampling techniques such as transdimensional Markov chain Monte Carlo, allow sampling over complex posterior probability density functions, thus providing information on constraint, trade-offs and uncertainty in the reconstructed earth model. This presentation will present a review of transdimensional inference, and its application to different problems, ranging from paleoclimatology, geochemistry to solid earth geophysics.

**Brief biography**

I am a geophysist interested a in seismic data interpretation and inverse theory. I hold a degree of geophysics from the university of Strasbourg (2006), and a PhD from the Australian National University (2011). I was the recipient of a Miller postdoctoral fellowship at the university of California Berkeley (2012-2015). Since 2015, I serve as a CNRS researcher at the Laboratoire de Geologie de Lyon, in France, where I obtained an ERC starting grant to work on multiscale seismic imaging (2017-2023). In september 2024, I will be joining ICM with an ATRAE grant from the spanish governement. If I had to summarize the object of my research in one question, that would be: how can we quantify the state of knowledge we have about the Earth, given the measurements that we make at the surface? This includes solving an inverse problem and finding a model of the Earth that explains our observations. I have been mainly working on Bayesian (i.e. probabilistic) methods where the solution is a probability density function describing the information we have about the Earth. The goal is to fully take into account observational and theoretical errors, and to propagate them towards model uncertainties.