Research

My main topic of research is the development of methods to solve inverse problems in geophysics. For example, estimating density anomalies in the subsurface from measured disturbances in the Earth's gravity field. These so called "inversion methods" are the main tools used by geoscientists to understand the inside of the Earth and other planets. Most methods that I develop are related to gravity and magnetic field data but I'm also interested in seismology and geodesy. Find out more about my research at the Computer-Oriented Geoscience Lab.

Fatiando a Terra is the main open-source software project on which I work. We develop Python tools for geophysics: modelling, inversion, data processing, and more.

Open-source Scientific Software

Programming is a requirement for method development. By definition, there is no existing software that implements your new method. I program mostly in Python but I'm also proficient in C. All of my software contributions are open-source and hosted on GitHub.

Map of the mean gravity disturbance and point density data in 0.1° blocks for all of Australia. The volume of data in Geophysics has been increasing rapidily and we need to develop data processing methods that scale to these data volumes.

Geophysical data processing and machine learning

There is no turning back from the machine learning frenzy that has taken over the world. Geoscientists have been doing similar things for decades but with different names and objectives. One of these things is called the "equivalent layer technique" in gravity and magnetics. Similar methods in different fields have many different names, for example radial basis functions or Green's functions interpolation. All of these methods are linear regressions in which we fit a linear model to some data and then use the model to predict new data. The difference with standard machine learning is that the linear model we use has physical meaning. For gravity data, the model is the gravitational attraction of point sources, whereas for GPS data, the model is the elastic deformation of medium. Given the many similarities, I have been very interested in applying other machine learning techniques to these geophysical problems.

Estimated depth to the crust-mantle interface (Moho) from satellite measurements of gravity disturbances. Dotted lines represent the boundaries between major geologic provinces.Solid orange lines mark the limits of the main lithospheric plates. The solid light grey line is the 35 km Moho depth contour.

Inverse problems in Geophysics

As a geophysicist, my ultimate goal is to infer the physical properties of the inner Earth and its processes from surface observations. This is an ill-posed inverse problem, to which a solution might not exist or be non-unique and unstable. I develop methods for solving different kinds of inverse problems using several sets of constraints to overcome the instability of the solution.

Research groups

Earth Sciences

Research grants

Towards individual-grain paleomagnetism: Translating regional-scale geophysics to the nascent field of magnetic microscopy

ROYAL SOCIETY

March 2022 - March 2025

Research collaborations

Ricardo I. F. Trindade

Adapting large-scale magnetomery methods to magnetic microscopy

Universidade de São Paulo, Brazil

I am the co-supervisor of PhD student Gelson Ferreira de Souza Junior from Professor Trindade's research group who is working on this theme.

Santiago R. Soler

Open-source software for processing and modelling gravity

University of British Columbia, Canada

I was the co-supervisor for Santiago's PhD project and continue to collaborate on different projects related to processing and modelling gravity data. We have been working together closely on the Fatiando a Terra (https://www.fatiando.org) project since 2015.