Seismic signal-to-noise separation method for complex wavefields based on dip-constrained sparse representation

Denoising is a critical step in seismic data processing. Traditional dictionary learning methods lack constraints on the physical mechanism of signals, easily misidentifying coherent noise with linear or curvilinear features as effective signal components and incorporating them into dictionary atoms, which results in distortion of effective signals or residual noise during reconstruction. To address these problems, this paper proposes a hybrid noise suppression method based on sparse basis learning with physical constraints. Firstly, the plane-wave destruction (PWD) filter is used to estimate and update the local dip field, ensuring that the dip field matches the iteratively optimized signal. Then, the local dip field estimated by PWD is taken as a constraint to guide the sparse basis learning process, which combines the signal representation ability of sparse transform with the local dip characteristics of effective signals. This enables dictionary atoms to match only effective signals with corresponding dip characteristics, avoiding the mislearning of coherent noise atoms. Finally, effective signals can be accurately sparsely represented on the sparse basis matching their dip characteristics, while various noises are difficult to be matched by the sparse basis due to the absence of corresponding dip characteristics. Thereby, high-fidelity effective signal reconstruction and effective suppression of multi-type noises are achieved. Tests on both complex synthetic data and field data show that the proposed method has superior suppression performance and wide applicability in suppressing hybrid noises such as strong abnormal amplitude interference, surface waves, random noise, and coherent noise, compared with conventional methods and industrial standard denoising workflows.