Open AccessApplication of an improved particle filter for random seismic noise suppression
Author(s) -
Jingquan Zhang,
Wang Dian,
Peng Li,
Shiyu Liu,
Han Yu,
Yuxin Xu,
Ming Teng
Publication year - 2021
Publication title -
journal of geophysics and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.623
H-Index - 38
eISSN - 1742-2140
pISSN - 1742-2132
DOI - 10.1093/jge/gxab064
Subject(s) - noise (video) , particle filter , computer science , seismic noise , resampling , particle (ecology) , firefly algorithm , filter (signal processing) , process (computing) , algorithm , acoustics , geology , seismology , physics , artificial intelligence , computer vision , oceanography , particle swarm optimization , image (mathematics) , operating system
Random noise is inevitable during seismic prospecting. Seismic signals, which are variable in time and space, are damaged by conventional random noise suppression methods, and this limits the accuracy in seismic data imaging. In this paper, an improved particle filtering strategy based on the firefly algorithm is proposed to suppress seismic noise. To address particle degradation problems during the particle filter resampling process, this method introduces a firefly algorithm that moves the particles distributed at the tail of the probability to the high-likelihood area, thereby improving the particle quality and performance of the algorithm. Finally, this method allows the particles to carry adequate seismic information, thereby enhancing the accuracy of the estimation. Synthetic and field experiments indicate that this method can effectively suppress random seismic noise.