Improving the signal-to-noise ratio (SNR) and resolution of seismic data are crucial for high-precision seismic exploration. Currently, many denoising methods and deconvolution approaches are presented. However, there has always been a technical problem that improving the SNR and improving the resolution are mutual influence and restriction. To solve the problem, we present a novel seismic data restoration approach based on alternating direction method and total variation theory in this paper, which can effectively recover seismic data from convolution and noisy observation. It simultaneously conducts denoising and deconvolution for seismic data. To the best of our knowledge, this is the first exploration in the geophysical fields using such a mathematical theory. We apply the proposed algorithm to the synthetic data and real seismic data. The comparison shows that our proposed algorithm can recover the seismic data better and have a faster convergence.
Presentation Date: Wednesday, October 19, 2016
Start Time: 10:20:00 AM
Location: 148
Presentation Type: ORAL
