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Post-stack seismic data denoising via MABCDT-Curvelet transform 认领 引用
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作者 Niu Chaoyi Huang Xuri +3 位作者 Yan Shengyuan Chen Xiaochun Wo Yukai Ma An 《Journal of Geophysical Prospecting》 2025年第3期286-297,共12页
Noise in post-stack seismic data can severely interfere with the identification and interpretation of reflection signals, making efficient denoising essential for improving the accuracy of seismic data interpretation.... Noise in post-stack seismic data can severely interfere with the identification and interpretation of reflection signals, making efficient denoising essential for improving the accuracy of seismic data interpretation. Owing to its superior multi-scale and multi-directional characteristics, curvelet transform has been widely adopted for post-stack data denoising. However, conventional curvelet transform tends to produce pseudo-Gibbs effects at boundaries, which leads to edge oscillations and spurious reflections. In addition, aggressive noise suppression often results in the loss of valid signals, which limits the practical applicability of these methods. To address these challenges, this paper proposes a curvelet transform approach incorporating multi-scale adaptive block curveletdomain thresholding(MABCDT). First, cycle spinning and MABCDT are introduced in the curvelet domain to enhance the preservation of weak signals and significantly mitigate pseudo-Gibbs phenomena. Then, a fast non-local mean filtering is applied to the data after inverse curvelet transform, which further retains valid signals while removing residual noise. Finally, a directional smoothing diffusion algorithm is introduced, which utilizes gradient direction information to perform directionally weighted smoothing and diffusion, thereby further suppressing noise and enhancing the continuity of valid signals. Both synthetic and field data tests demonstrate that the proposed method outperforms conventional post-stack denoising techniques in terms of signal-to-noise ratio enhancement and waveform fidelity. The method preserves the continuous structural features of seismic signals while suppressing noise and significantly reduces pseudo-Gibbs effects caused by high-frequency truncation. 展开更多
关键词 curvelet transform cycle spinning multi-scale adaptive block curvelet‑domain thresholding(MABCDT) fast non-local means filtering directional smoothing diffusion algorithm weak signal identification
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