# Towards automatically building starting models for full-waveform inversion using global optimization methods: A PSO approach via DEAP + Devito

arXiv: https://arxiv.org/abs/1905.12795 · HTML: https://mlsanity.com/paper/1905.12795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 1 of 1)

- [Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?](https://mlsanity.com/paper/2509.14919/) (2025)

## Abstract (arXiv)

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.