Curvelet Denoising Directly Applied to Real Seismic Data

Curvelet denoising is a signal processing technique commonly used in seismic exploration that effectively suppresses noise in seismic data. In practical seismic data processing, curvelet denoising has been widely adopted with demonstrated success. By documenting detailed processing workflows and parameter configurations - such as threshold selection methods, scale decomposition levels, and angular divisions in the curvelet transform - we can properly evaluate and validate the denoising results. The implementation typically involves applying multidimensional wavelet transforms across different scales and orientations, where noise components are identified and attenuated through thresholding operations. Furthermore, curvelet denoising can be integrated with other processing techniques like seismic inversion and velocity analysis to further enhance the quality and accuracy of seismic data processing. The algorithm's directional sensitivity makes it particularly effective for preserving geological features while removing random noise.