A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

Jinglai Li, Guang Lin, Xu Yang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.
Original languageEnglish
Pages (from-to)58-71
Number of pages14
JournalJournal of Computational Physics
Early online date6 May 2015
Publication statusPublished - 1 Sept 2015


  • Frozen Gaussian approximation
  • Full waveform inversion
  • High-frequency wave
  • Particle swarm optimization


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