Broadband Reconstruction of Seismic Data with Generative Recurrent
Adversarial Network
- wei song ,
- Yaying Song
Abstract
Wei et al. describe a new method to reconstruct broadband of seismic
data by GRAN deep learning. The deep learning neural network is based on
an improved network architecture of GAN and the discriminator is to
build the loss function by introducing Wasserstein distance to ensure no
gradient vanishing at any time. Although synthetic dataset was used in
training, the method still gave satisfactory results in the real data
processing study and it also showed the importance of multi disciplinary
collaboration and data cleaning in deep learning.