Physics Informed Seismic Deblending Using a Self-Supervised Denoiser

Simultaneous sources aims at reducing the acquisition time of seismic surveys by either firing multiple sources at the same time, or closely after one another. This leads to entangled seismic data that has to be untangled in order to be meaningful for processing and imaging purposes. Untangling the seismic data amounts to solving an underdetermined linear inverse problem. The blending operator introduces coherent overlapping seismic events in the source gathers, but in the receiver gathers the entangled data will appear as noise. To obtain meaningful seismic data, we have to add regularization to filter this noise. In this work we propose the use of a self-supervised denoiser that filters the blending noise, that is cleverly coupled with the blending operator. Our algorithm thereby combines the physics of the blending process through the blending operator with a highly effective self-supervised denoiser. The denoiser requires no ground-truth labels or pre-training on synthetic data, which is a huge benefit over other machine learning algorithms.