Research interests in a nutshell
My research encompasses various aspects of solid-Earth geophysics.
We infer the properties of our planet from physical measurements. To
investigate the mostly inaccessible Earth we need to use indirect
measurements in most situations. Thus, we need methods to
estimate the unknowns 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
Current research regards the study of a combined geophysical and geostatistical inversion method in the framework of a probabilistic approach (Markov chain Monte Carlo) for reservoir modeling and related risk analysis (IMGP group - Prof. K. Mosegaard). This involves the development of a Monte Carlo method that samples the posterior distribution of reservoir models according to their 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, which is "learned" by the algorithm by scanning through prototype models provided in the form of training images.
Seismic ambient noise
Study of seismic ambient noise helps to gain further insights about the interior of the Earth in addition to more classical techniques. Research focus on measuring surface-wave phase velocity from station to station cross-correlation of ambient signal. The next step is to perform tomographic model-ling using surface-wave dispersion curves as well as studying the origin and location of noise sources. Moreover, we investigate the relationship between microseisms and sea wave heights in the Ligurian Sea.
Markov Chain Monte Carlo inverse methods applied for thermo-compositional mapping of Earth's mantle
In collaboration with Amir Khan (ETH Zurich) and James Connolly (ETH Zurich) we set up a probabilistic inversion of seismic surface-wave data, directly targeting the thermo-compositional mapping of Earth's mantle below the North American Continent. We employ a non-linear Monte Carlo approach in a bayesian framework that combines a self-consistent thermodynamic modeling of mineral phase equilibria with a stochastic-based nonlinear inverse technique, targeting directly the thermo-compositional mapping of Earth's mantle.
Inversion of magnetic anomaly data
This work regarded the development of inverse methods for exploration geophysics using potential fields, in particular magnetic anomalies. We studied iterative deconvolution and semi-blind deconvolution methods targeting magnetic archaeological prospecting, testing several different regularization approaches.