Applying Curvelet Denoising Directly to Real-World Seismic Data

Curvelet denoising is a signal processing method based on curvelet transform that has gained widespread application in seismic data processing in recent years. Compared to traditional wavelet transform, curvelet transform demonstrates superior capability in capturing curved features within seismic signals, offering unique advantages when processing seismic data. From an implementation perspective, curvelet transform algorithms typically involve multi-scale directional decomposition through frequency partitioning and angular segmentation.

In practical seismic data, noise contamination remains a significant challenge for exploration professionals. Conventional linear filtering techniques often struggle to distinguish between useful signals and noise, whereas curvelet denoising employs adaptive thresholding and sparse representation techniques to effectively separate noise while preserving seismic waveform characteristics. The core algorithm involves decomposing seismic data into different scales and directions using curvelet transform, applying thresholding to suppress noise components, and finally reconstructing denoised seismic data through inverse transform. Key implementation steps include: 1) Forward curvelet transform using digital curvelet transform algorithms, 2) Threshold application using hard or soft thresholding functions, 3) Inverse transform reconstruction.

Curvelet denoising proves particularly effective for seismic data containing complex geological structures such as faults, fractures, or dipping strata. The method not only improves the signal-to-noise ratio of seismic profiles but also better preserves detailed characteristics of useful signals, facilitating subsequent seismic interpretation and inversion work. Code implementation often requires careful handling of directional sensitivity parameters to maintain structural integrity.

In practical applications, curvelet denoising typically requires parameter optimization including scale selection for curvelet transform, number of directional subdivisions, and threshold settings. Proper parameter adjustment can significantly enhance denoising performance, while automated or semi-automated parameter optimization methods have become a research focus. Implementation frameworks often incorporate optimization algorithms like cross-validation or Bayesian optimization for parameter tuning, with computational efficiency being a crucial consideration for large-scale seismic datasets.