I gave this presentation at the
Department of Geology and Geophysics
TGIF Seminar series shortly after arriving at
the University of Hawaii
for my postdoc to work on GMT.
The last interesting results that I'd had were from my PhD thesis
so I thought I'd present that, though heavily edited in the interest of time
(my thesis presentation was about 1h 20min).

I gave a few live demos of Fatiando a Terra using Jupyter
notebooks during the talk:

The inner density distribution of the Earth can be inferred from disturbances
in its gravitational field. However, accomplishing this is never easy. There
are many possible parameterizations for the mathematical model, which is often
non-linear. To make matters worse, gravity data alone do not contain enough
information to obtain a unique and stable solution. One must add independent
information to constrain the solution space, often in the form of
regularization. Many different methods for performing this inference have been
developed and research in this field is still active. Investigating new
methodologies implies developing complex software, which often must be able to
deal with sparse matrices and parallelism. I’ll present the open-source Python
library Fatiando a Terra. It implements many of the
components required for developing inversion methods, such as forward modeling,
data processing and I/O, and regularization. I’ll also show how I used this
library to develop a computationally efficient method for estimating the Moho
depth from gravity data using a spherical approximation of the
Earth.

Comments?
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let me know on Twitter
@leouieda.

Found a typo/mistake?
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and I'll happily merge it
(plus you'll feel great because you helped someone).
All you need is an account and 5 minutes!