Near-Surface-Related Nonstationary Coherent Noise Suppression Using a Physically Constrained Deep Neural Network

Surface waves, usually referred to as Rayleigh waves and also called ground roll, generated near the surface are a kind of strong coherent noise to disturbs the accuracy of subsurface images with the recorded data from a seismic survey. In the case of the further challenges posed by rugged topography and variable near-surface velocities, the surface waves and other near-surface-related noises in the field data exhibit nonstationary characteristics, and their amplitude, frequency, and velocity vary with recorded time. Currently, the conventional methods to suppress this type of surface wave noise are based on steady-state assumptions. This article develops a nonstationary surface wave suppression method utilizing a deep neural network constrained by physical information. This method leverages the physical characteristics of surface waves and data-driven labels to constrain neural network training jointly. It enables effective training under small-sample conditions and effectively suppresses nonstationary surface waves in each prestack seismic data gathered. It also effectively decreases near-surface-related nonstationary interferences such as single-frequency noise and linear noise. Examples for both numerical and field datasets demonstrate that this near-surface-related noise suppression technology can precisely eliminate nonstationary interference signals such as surface waves, reconstructing effective waves with high efficiency, robustness, and generalization capability. Comparisons between the processing results of field data and those obtained using some typical commercial software confirm that the proposed deep neural network method surpasses traditional label-constrained network training limitations, offering superior suppression of surface waves, single-frequency noise, and linear interference in field data efficiently and stably with much less cost.