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Robust Reconstruction of Seismic Data with Random and Erratic Noise
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 79th EAGE Conference and Exhibition 2017, Jun 2017, Volume 2017, p.1 - 5
Abstract
Due to the influence of variations of landform, seismic data usually suffers from missed traces. During the past few years, compressive sensing plays an important role in reconstruction of seismic data. However, the least-squares criterion in compressive sensing model is highly sensitive to non-Gaussian erratic noise. This paper develops a robust compressive sensing to achieve simultaneous random and erratic noise attenuation as well as seismic data reconstruction. The algorithm is based on a robust compressive sensing reconstruction model in which the least-squares criterion is replaced by a robust Huber function. The proposed algorithm is tested by using a subsampled seismic record with simulated random and erratic noise.