Simultaneous source separation and noise reduction with structure-oriented space-varying alpha-trimmed mean filter.

Median filters are often used in simultaneous source data to remove sudden noise disturbances. Nonetheless, the median filter has a poor ability to process random noise, which can affect its performance in seismic data processing. This paper introduces a novel filtering method, a structure-oriented space-varying alpha-trimmed mean filter, which is an improvement of median filtering, and can deal with random noise better. The proposed filter's window length can be adaptively adjusted based on geological conditions, enabling the effective removal of spiky noise during simultaneous source acquisition and common random noise in geological exploration. This paper introduces the main differences between different types of median filtering methods and provides an overview of the principles and steps involved in our proposed approach. Our method's effectiveness is evaluated through its application to both simple synthetic examples and field data, alongside a comparative analysis against other existing approaches. The results of this research demonstrate that the approach we proposed here can efficiently make spike-like noise and random noise disappear while simultaneously maintaining vital signal characteristics. • The experiment separates simultaneous sources and suppress random noise. • The deblending effect of different methods based on filtering method is compared. • Deblending in the flatted dimension preserves most of the useful signal. [ABSTRACT FROM AUTHOR]