Our research focuses on the creation and application of new methods for geophysical modeling and data processing, mostly in the fields of gravimetry and magnetometry.

Inverse problems

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).

Forward modeling

A key component for solving an inverse problem is first solve the "forward problem". One of the main research problems on which we work is developing methods for forward modeling gravitational fields caused by a tesseroid (a spherical prism). This allows modelling and inversion at continental and global scales.

A tesseroid discretized using our adaptive algorithm.

Data processing

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 geophysical problems.

Spatial data has uncertainties which need to be handled properly. There are different ways to use uncertainties as data weights for processing.