1. This paper proposes a method based on data augmentation to train a deep neural network for suppressing multiple seismic waves.
2. The designed deep neural network includes convolutional encoding and decoding processes, which are used to learn the primary wave features in the full wave field data and reconstruct the primary wave while suppressing multiple waves and random noise.
3. Three sets of simulated data examples and an application example of a set of seismic physical simulation data verify the effectiveness, stability, and good generalization of the proposed method.
This article is generally reliable and trustworthy as it provides evidence from three sets of simulated data examples as well as an application example of a set of seismic physical simulation data to support its claims. The article also presents both sides equally by providing an overview of existing methods for suppressing multiple seismic waves before introducing its own proposed method. Furthermore, potential risks are noted in the article, such as how multiples can mislead interpretation of seismic data if not removed properly.
However, there are some points that could be improved upon in terms of trustworthiness and reliability. For instance, there is no discussion on possible counterarguments or alternative approaches that could be taken when dealing with multiples in seismic data. Additionally, there is no mention of any limitations or drawbacks associated with the proposed method that readers should be aware of before implementing it in practice. Finally, there is no discussion on how this method compares to other existing methods for suppressing multiple seismic waves in terms of accuracy or efficiency.