Authors: Uieda, Leonardo, Observatorio Nacional; Oliveira Jr, Vanderlei C., Observatorio Nacional; Barbosa, V
Solid Earth geophysics is the science of using physical observations of the Earth to infer its inner structure. Generally, this is done with a variety of numerical modeling techniques and inverse problems. The development of new algorithms usually involves copy and pasting of code, which leads to errors and poor code reuse. Added to this is a modeling pipeline composed of various tools that don't communicate with each other (Fortran/C for computations, large complicated I/O files, Matlab/VTK for visualization, etc).
Fatiando a Terra is a Python library that aims to unify the modeling pipeline inside of the Python language. This allows users to replace the traditional shell scripting with more versatile and powerful Python scripting. Together with the new IPython notebook, Fatiando a Terra can integrate all stages of the geophysical modeling process, like data pre-processing, inversion, statistical analysis, and visualization. However, the library can also be used for quickly developing stand-alone programs that can be integrated into existing pipelines. Plus, because functions inside Fatiando a Terra use a common data and mesh format, existing algorithms can be combined and new ideas can build upon existing functionality. This flexibility facilitates reproducible computations, prototyping of new algorithms, and interactive teaching exercises.
Although the project has so far focused on potential field methods (gravity and magnetics), some numerical tools for other geophysical methods have been developed as well. The library already contains: fast implementations of forward modeling algorithms (using Numpy and Cython), generic inverse problem solvers, unified geometry classes (prism meshes, polygons, etc), functions to automate repetitive plotting tasks with Matplotlib (automatic griding, simple GUIs, picking, projections, etc) and Mayavi (automatic conversion of geometry classes to VTK, drawing continents, etc). In the future, we plan to continuously implement classic and state-of-the-art algorithms as well as sample problems to help teach geophysics.