Seismic random noise suppression method by adaptive dynamic filtering network
Due to the complex geological and environmental conditions,the signal-to-noise ratio of seismic data is relatively low,which has a negative impact on subsequent research.Therefore,the suppression of random noise in seismic data processing has been of great concern.The existing algorithms are unable to effectively sup-press noise and preserve the effective signal.Therefore,this paper combines traditional methods with deep learning and puts forward a method based on an adaptive dynamic filtering network to suppress random noise in seismic data.The network is based on an encoder-decoder architecture.Firstly,the idea of channel attention mechanism(AM)is introduced to realize the feature integration of multi-scale data formed by dilated convolu-tion through channel AM,providing accurate and rich feature representation for the network.Then,dynamic convolution is introduced to achieve the learning of high-frequency features of seismic data with low computa-tional complexity,so as to preserve more detailed information.The experimental results of both synthetic data and actual data show that the adaptive dynamic filtering network can effectively suppress random noise in seis-mic data while retaining richer details of seismic data,and the signal-to-noise ratio of seismic data after proces-sing is significantly improved.
deep learningchannel attention mechanismdynamic convolutionresidual learningsignal-to-noise ratio(SNR)
国家科技期刊平台
NSTL
万方数据
