Our research themes
We focus on the creation and application of new methods for geophysical modeling and data processing, mostly in the fields of gravimetry and magnetometry.
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 to overcome these challenges and solve different kinds of inverse problems that arise in geophysics.
The planting method for solving the inverse problem of estimating density from observed gravity disturbances.
A key component for solving an inverse problem is first solve the forward problem (predicting observed data from a known model of the subsurface). One of our main research themes is the development methods for forward modeling gravitational fields caused by a tesseroid (a segment of a sphere). This is a surprisingly difficult task but is crucial to model geology at continental and global scales.
A tesseroid (spherical prism) 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 geophysical problems.
Spatial data has uncertainties which need to be handled properly. There are different ways to use uncertainties as data weights for processing.