  In this work, we illustrate an example of estimating the macro-model of
velocities in the subsurface through the use of global optimization methods
(GOMs). The optimization problem is solved using DEAP (Distributed Evolutionary
Algorithms in Python) and Devito, python frameworks for evolutionary and
automated finite difference computations, respectively. We implement a Particle
swarm optimization (PSO) with an "elitism strategy" on top of DEAP, leveraging
its transparent, simple and coherent environment for implementing of
evolutionary algorithms (EAs). The high computational effort, due to the huge
number of cost function evaluations (each one demanding a forward modeling
step) required by PSO, is alleviated through the use of Devito as well as
through parallelization with Dask. The combined use of these frameworks yields
not only an efficient way of providing acoustic macro models of the P-wave
velocity field (Vp), but also significantly reduces the amount of human effort
in fulfilling this task.
