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

## TL;DR

This paper presents an automated approach to building initial models for full-waveform inversion by integrating PSO with DEAP and Devito, leveraging parallel computing to reduce computational costs.

## Contribution

It introduces a novel combination of PSO, DEAP, and Devito frameworks for efficient macro-model estimation in seismic inversion.

## Key findings

- Efficiently estimates subsurface velocity models using global optimization.
- Reduces computational effort through parallelization with Dask.
- Automates the initial model building process for FWI.

## Abstract

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.