Research interests in a nutshell

My research encompasses various aspects of solid-Earth geophysics.   To investigate the inaccessible Earth we have to rely on indirect measurements, therefore we need methods to estimate the unknown parameters that are able to predict the observations. My interests concern the development of methodologies and implementation of algorithms to model these geophysical data, including processing, forward and inverse modeling.
  Research topics include mainly inverse problems in geophysics, seismology and computational geophysics, and touch other fields like applied geophysics and geochemistry. I am strongly interested in the probabilistic approach to inverse problems, in particular in Monte Carlo methods. These are able to tackle complex non-linear inverse problems and can combine together different geophysical data sets.

Combined inverse modeling and geostatistics in a probabilistic framework

seismic data reservoir models

Current research regards the study of a combined geophysical and geostatistical inversion method in the framework of a probabilistic approach targeting subsurface structure and related uncertainty analysis (Solid Earth Physics group - Prof. K. Mosegaard). This involves the development of a Monte Carlo (Markov chain Monte Carlo) method that samples the posterior distribution of reservoir models according to the degree of fit with observed seismic data and the a priori information. It involves algorithms drawn from geostatistics to generate random models according to possibly complex prior information, e.g., by patterns "learned" by the algorithm by scanning through prototype models provided in the form of training images.

Markov Chain Monte Carlo inverse methods applied to thermo-compositional mapping of Earth's mantle

seismic tomography

In collaboration with Amir Khan (ETH Zurich) we are investigating the thermo-chemical structure of Earth's mantle using a probabilistic methodology to invert seismic and other geophysical data. We employ a non-linear Monte Carlo approach in a bayesian framework that combines a self-consistent thermodynamic modeling of mineral phase equilibria with seismic forward modeling (surface waves dispersion curves or full waveform), targeting directly thermo-compositional mapping of Earth's mantle.