Desert Seismic Noise Attenuation Based on Bayesian Mathematical Morphology Filtering
S. Wang, Y. Li and H. Lin
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Full Waveform Inversion C
Publication date: 03 June 2019
Info: Extended abstract, PDF ( 1.59Mb )
Price: € 20
Suppressing random noise in seismic data to acquire high-quality data is a major concern in seismic prospecting. However, in desert seismic data processing, we are unable to achieve an outstanding noise attenuation result by conventional noise attenuation methods due to the low fundamental frequency of the random noise. In this paper, we propose a novel noise suppression system called Bayesian Mathematical Morphological Filtering (BMMF). The basic idea of the proposed algorithm is to extract the signal density hidden in the seismic data as a priori condition for mathematical morphology filtering. In order to prove the feasibility of the algorithm, we use the complex synthetic data to test the performance of the new algorithm, and apply the algorithm to the real desert seismic data processing. Finally we make a comparison among our algorithm and other two popular algorithms: F-K filtering and EMD. The experiment results indicate that we can achieve an excellent noise attenuation using BMMF, and it prevails over other two algorithms in both noise suppression and signal retention.