Seismic profile denoising based on common-reflection-point gathers using convolution neural networks

Abstract

With the development of seismic surveys and the decline of shallow petroleum resources, high resolution and high signal-to-noise ratio have become more important in seismic processing. To improve the quality of seismic data, stationary phase migration based on dip-angle gathers can be used to separate the reflected waves and noise. However, this method is very computationally intensive and heavily dependent on expert experience. Neural networks currently have powerful adaptive capabilities and great potential to replace artificial processing. Certain applications of convolution neural networks (CNNs) on stack profiles lead to a loss of amplitude information. Therefore, we have developed CNNs for noise reduction based on Common-Reflection-Point (CRP) gathers. We used CRP gathers of stationary phase migration as labels and CRP gathers of conventional prestack time migration as inputs. In addition, we analyzed the seismic amplitude properties and demonstrated the neural network optimization process and results. The results showed that our methods can achieve fast and reliable denoising and produce high-quality stack profiles that contain true amplitude information. Furthermore, the predicted high-quality CRP gathers can be used for further processing steps, such as normal moveout correction and amplitude variation with offset.