Passive seismic data primary estimation and noise removal via focal-denoising closed-loop surface-related multiple elimination based on 3D L1-norm sparse inversion

Abstract

Passive seismic has recently attracted a great deal of attention because non-artificial source is used in subsurface imaging. The utilization of passive source is low cost compared with artificial-source exploration. In general, constructing virtual shot gathers by using cross-correlation is a preliminary step in passive seismic data processing, which provides the basis for applying conventional seismic processing methods. However, the subsurface structure is not uniformly illuminated by passive sources, which leads to that the ray path of passive seismic does not fit the hyperbolic hypothesis. Thereby, travel time is incorrect in the virtual shot gathers. Besides, the cross-correlation results are contaminated by incoherent noise since the passive sources are always natural. Such noise is kinematically similar to seismic events and challenging to be attenuated, which will inevitably reduce the accuracy in the subsequent process. Although primary estimation for transient-source seismic data has already been proposed, it is not feasible to noise-source seismic data due to the incoherent noise. To overcome the above problems, we proposed to combine focal transform and local similarity into a highly integrated operator and then added it into the closed-loop surface-related multiple elimination based on the 3D L1-norm sparse inversion framework. Results proved that the method was capable of reliably estimating noise-free primaries and correcting travel time at far offsets for a foresaid virtual shot gathers in a simultaneous closed-loop inversion manner.