Discrete Curvelet Transform (DCT) introduce minimal overlapping between coefficients representing signal and noise in the curvelet domain, hence being well-suited for data denoising. However, appropriate and optimal weighting of these coefficients remain challenging and the most important stage of curvelet-based noise attenuation. Setting one threshold level for all coefficients may be insufficient for optimal noise attenuation, hence we focus on more complex, scale- and angle-adaptive approach to thresholding. We find empirically that adjusting threshold levels according to certain frequency bands and dips gives results superior to global thresholding. We demonstrate our noise attenuation approach by applying DCT to 3D post-stack seismic data and conditioning input data for 2D full-waveform inversion.


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