Photo of Dr Leonardo Uieda

Dr Leonardo Uieda PhD • Pronouns: He/Him

Lecturer Earth, Ocean and Ecological Sciences

Research

Research Overview

"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.

I have an "open by default" policy for my research and teaching, meaning that pretty much everything I do is freely available online for others reuse and modify.

More information: www.compgeolab.org"

Open-source Scientific Software

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.
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.

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. I'm a core developer and maintainer of several open-source projects:

Fatiando a Terra
Tesseroids
Generic Mapping Tools.

Geophysical data processing and machine learning

Map of the mean gravity disturbance, observation height, 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.
Map of the mean gravity disturbance, observation height, 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.

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.

Inverse problems in Geophysics

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.
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.

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 Collaborations

Santiago R. Soler

Project: PhD co-supervisor
External: Universidad Nacional de San Juan, Argentina

I am the co-supervisor for Santiago's PhD project. We have been collaborating since 2015 and work together closely on the Fatiando a Terra project.