A brief exploration of open-source gradient-based numerical optimization Python libraries for full-waveform inversion

Geoscientists favor Python for its user-friendly interface and scientific packages that support application implementation. Python's capabilities make it particularly useful for seismic full waveform inversion (FWI), which can see its implementation time reduced by making use of its extensive library collection. We compare four open-source gradient-based optimization Python packages - scipy.optimize, sotb-wrapper, NLopt, and PyROL - for solving the FWI optimization problem. The comparison is based on the packages' core features, documentation, and learning curves evaluated through the implementation of a 2D time-domain FWI application, built using the Devito modeling engine along with the aforementioned optimization packages. We detail how one can use a particular solver from each package for the solution of a bound-constrained optimization problem such as FWI. The open-source FWI template models used to obtain the numerical results are provided.