Computer-Oriented Geoscience Lab

Our research

We focus our efforts on creating and applying new methods for 3D modeling and data processing, mostly for gravimetry and magnetometry.

Magnetic microscopy

The magnetization locked in minerals at the time of their formation is a gateway to the Earth’s distant past. So far, researchers have only been able to make bulk measurements from each sample. Magnetic microscopy technology is now allowing us to distinguish the magnetic fields of the individual minerals that make up the rock sample. Our group is working with experts in paleomagnetism to develop new methods that are capable of unlocking the huge potential of these new data.

Red and blue map showing dipolar anomalies at a scale of 20 micrometers.
Example magnetic microscopy data showing tiny magnetic anomalies on the order of 20µm in size.

Antarctic geothermal heat flow

Heat flow from the Earth’s interior is an important parameter for how ice sheets flow and how the Earth’s crust rebounds upwards once ice mass is displaced, influencing sea-level rise. Magnetic anomaly data is one of the few ways we have to determine heat flow. Our group is working to improve the way airborne and satellite magnetic data are merged and modelled to produce heat flow estimates.

Map of Antarctica overlaid by red, white, and blue points representing magnetic measurements. The data coverage has a lot of gaps.
The ADMAP2 compilation of open-access airborne magnetic anomaly data for Antarctica.

Machine learning & data processing

It’s undeniable that a machine learning frenzy has taken over the world. Geoscientists have been doing similar things for decades, for example the equivalent sources technique in gravity and magnetics is basically a supervised-learning method. Given the many similarities, we are applying and adapting machine learning techniques and best-practices to geophysical problems.

3 maps of California with colored points representing different types of uncertainty calculations with each being slightly different from the observed data uncertainties.
Spatial data has uncertainties which need to be handled properly. There are different ways to use uncertainties as data weights for processing.

Geophysical inversion and imaging

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.

3D rendering of white blocks with topography and some red blocks spread in a thin vain representing the estimated iron ore body.
Modelled density anomalies associated with iron-ore formations derived from observed gravity disturbances.

Forward modeling

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.

Drawing of a curved prism sitting on top of a curved Earth. The prism is cut by white lines that represent the discretization.
A tesseroid (spherical prism) discretized using our adaptive algorithm.