Swell noise, which is characterized by high-amplitude and low-frequency content, can mask the useful signals in seismic data and severely affect the fidelity of subsequent seismic processing. In this abstract, we presented a novel correntropy based robust sparsity-promoting method for swell noise suppression. Unlike traditional least-squares sparsity-promoting methods making use of the norm, which is extremely sensitive to swell noise, our method leveraged the superiority of correntropy induced metric (CIM) to adaptively assign weights for noisy data depending on the swell noise level. And also, the sparse prior is imposed on the CIM to learn robust and sparse representations. Using the Half-Quadratic optimization technique, the correntropy based optimization can be transformed into an -constrained quadratic problem, which can be efficiently solved by a standard optimization method. We applied our presented method to real marine seismic data which is largely contaminated by swell noise, and the results turns out that the swell noise is suppressed successfully and the useful signals are well preserved. 2 l 1 l
Presentation Date: Wednesday, October 14, 2020
Session Start Time: 9:20 AM
Presentation Time: 9:45 AM
Location: Poster Station 13
Presentation Type: Poster
