An Adaptive Time-Frequency Denoising Method for Suppressing Source-Related Seismic Strong Noise

The presence of source-related noise, characterized by exceptionally large amplitudes, poses a significant challenge in seismic data processing, impeding the accurate recovery of the effective signal within overlapping regions. In response, we introduce an adaptive time-frequency denoising method tailored to mitigate this issue. First, the seismic data is horizontally sorted, and exponential function fittings are applied to exploit amplitude difference information embedded in the dataset. Then, the curvatures of the exponential functions are computed, and their maximal values are chosen to establish a dynamic threshold curve, offering an estimate for the range of abnormal traces from shallow to deep regions. Next, further refinement of the dynamic threshold curve involves iterative adjustments based on the standard deviation of the curvature functions, resulting in a more precise separation between signal and noise traces. Finally, leveraging the optimized dynamic threshold curve, adaptive threshold value computations are performed at each frequency slice and subsequently applied through soft thresholding to effectively reduce the impact of strong amplitude noise. Numerical examples involving synthetic and field data demonstrate the effectiveness of the proposed method in attenuating strong source-related noise, surpassing the performance of traditional f-k filter, nonconvex (NC) threshold, and static threshold methods.