Fang, J., Zhou, H., Zhang, Q., Chen, H., Wang, N., Sun, P., & Wang, S. (2018). Effect of Surface-Related Rayleigh and Multiple Waves on Velocity Reconstruction with Time-Domain Elastic FWI. Journal of Applied Geophysics, 148, 33-43. https://doi.org/10.1016/j.jappgeo.2017.11.006

has been cited by the following article:

• TITLE: A Rayleigh Wave Globally Optimal Full Waveform Inversion Framework Based on GPU Parallel Computing

  AUTHORS: Zhao Le, Wei Zhang, Xin Rong, Yiming Wang, Wentao Jin, Zhengxuan Cao

  KEYWORDS: Full Waveform Inversion, Finite-Difference Method, Globally Optimal Framework, GPU Parallel Computing, Particle Swarm Optimization

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.11 No.3, March 31, 2023

  ABSTRACT: Conventional gradient-based full waveform inversion (FWI) is a local optimization, which is highly dependent on the initial model and prone to trapping in local minima. Globally optimal FWI that can overcome this limitation is particularly attractive, but is currently limited by the huge amount of calculation. In this paper, we propose a globally optimal FWI framework based on GPU parallel computing, which greatly improves the efficiency, and is expected to make globally optimal FWI more widely used. In this framework, we simplify and recombine the model parameters, and optimize the model iteratively. Each iteration contains hundreds of individuals, each individual is independent of the other, and each individual contains forward modeling and cost function calculation. The framework is suitable for a variety of globally optimal algorithms, and we test the framework with particle swarm optimization algorithm for example. Both the synthetic and field examples achieve good results, indicating the effectiveness of the framework.