Abstract:
Desert seismic data have the characteristics of low signal-to-noise ratio (SNR) and low-frequency, which pose a major challenge to noise attenuation. In this letter, we p...View moreMetadata
Abstract:
Desert seismic data have the characteristics of low signal-to-noise ratio (SNR) and low-frequency, which pose a major challenge to noise attenuation. In this letter, we propose a denoising method for desert seismic data that combines the Gaussian conditional random field (GCRF) and the sparsity measurement. The sparsity measurement method is designed to replace the noise sampling method in the posterior frame. To calculate the block sparsity, first the seismic data blocks are divided into three groups: high sparsity blocks, medium sparsity blocks, and low sparsity blocks. Then different denoising parameters are determined according to the sparsity of seismic signal and the nonsparsity of desert low-frequency noise. Consequently, this targeted parameter setting achieves a more thorough suppression of noise and less attenuation of seismic signals. Both the synthetic and real data experiments prove the effectiveness of the method in this letter.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 18, Issue: 10, October 2021)