2019
DOI: 10.48550/arxiv.1905.12795
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Towards automatically building starting models for full-waveform inversion using global optimization methods: A PSO approach via DEAP + Devito

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“…Global search methods, while more rigorous than local search methods, come at a significant computational expense, especially when the forward problem is complex and the optimization involves a large number of unknowns, both of which are true for FWI. Yet, despite these complicating factors, some researchers have applied global search techniques to the FWI problem, such as simulated annealing (Datta and Sen, 2016;Tran and Hiltunen, 2012), particle swarm intelligence (Mojica and Kukreja, 2019), and genetic algorithms (Sajeva et al, 2016). However, researchers have more commonly used local search methods which, as their name implies, explore potential solutions located in the vicinity of a starting model in order to minimize the solution's misfit (Nocedal and Wright, 2006).…”
Section: Introductionmentioning
confidence: 99%
“…Global search methods, while more rigorous than local search methods, come at a significant computational expense, especially when the forward problem is complex and the optimization involves a large number of unknowns, both of which are true for FWI. Yet, despite these complicating factors, some researchers have applied global search techniques to the FWI problem, such as simulated annealing (Datta and Sen, 2016;Tran and Hiltunen, 2012), particle swarm intelligence (Mojica and Kukreja, 2019), and genetic algorithms (Sajeva et al, 2016). However, researchers have more commonly used local search methods which, as their name implies, explore potential solutions located in the vicinity of a starting model in order to minimize the solution's misfit (Nocedal and Wright, 2006).…”
Section: Introductionmentioning
confidence: 99%