Nonrepeatable Noise Attenuation on Time-Lapse Prestack Data Using Fully Convolutional Neural Network and Masked Image-to-Image Translation Scheme

In 4-D seismic surveys performed for carbon capture and sequestration projects, it is essential to acquire consistent time-lapse data to track the behavior of carbon dioxide. However, in practice, seismic events affected by nonrepeatable effects (i.e., nonrepeatable noise) hinder the objective of these surveys. Cross-equalization (XEQ) is a task that aims to reduce differences between time-lapse data by alleviating the adverse effect of nonrepeatable noise. XEQ using a convolutional neural network was proposed and applied to poststack data. By utilizing masks derived from the eikonal equation, we design an XEQ method for prestack data, which could contribute to retrieving ancillary information impaired during stacking and migration. The inherent nature of prestack data poses challenges when changing the data domain. To address these challenges, we introduce three supplementary methods: Fourier loss, coordinate conditioning, and logarithmic rescaling. Numerical examples show that the proposed XEQ effectively suppresses nonrepeatable noise while preserving 4-D signals even for prestack data, with minimal impact on amplitude information representing the degree of change. In addition, the supplementary methods enhance the matching quality and training stability. Sensitivity analyses on several factors (i.e., seawater velocity, source characteristics, ambient noise, and inaccurate masks) demonstrate the robustness of the proposed XEQ in suppressing nonrepeatable noise.