Our research focuses on the creation and application of new methods for
geophysical modeling and data processing, mostly in the fields of gravimetry
Our ultimate goal as geophysicists is to understand the inner structure and
dynamics of the Earth from surface observations. This is a tough mathematical
and computational problem: an ill-posed inverse problem, to which a
solution might not exist or be non-unique and unstable.
We develop methods for solving different kinds of inverse problems using
several types of constraints to overcome these challenges.
Maps of the depth to the crust-mantle boundary under South America (left) and
differences between gravity- and seismologically-derived depths (right).
A key component for solving an inverse problem is first solve the "forward
One of the main research problems on which we work is developing methods for
forward modeling gravitational fields caused by a tesseroid (a spherical
This allows modelling and inversion at continental and global scales.
A tesseroid discretized using our adaptive algorithm.
There is no turning back from the machine learning frenzy that has taken over
the world. Geoscientists have been doing similar things for decades, for
example the "equivalent layer technique" in gravity and magnetics. Given the
many similarities, we are applying other machine learning techniques to these
Spatial data has uncertainties which need to be handled properly. There are different
ways to use uncertainties as data weights for processing.