Seismic data reconstruction by wavelet channel attention network
Missing traces reconstruction is a key step for seismic data processing.In recent years,various seis-mic data reconstruction methods based on deep learning theory have been proposed.However,normal convolu-tion operation can only capture local dependencies and make insufficient use of global information.Moreover,the operation of pooling also results in the loss of feature map information,which destroys detailed features of seismic reflections.Therefore,a seismic data reconstruction method based on wavelet channel attention net-work is proposed.The Haar wavelet transform effectively extracts multi-scale characteristics and avoids the loss of information during the up-sampling process.Efficient channel attention modules are introduced to model the correlations between feature maps of different channels,which can make full use of the global information.Experimental results on synthetic and field datasets illustrate that the wavelet channel attention network can pro-duce more accurate reconstruction results than some representative deep learning methods.
seismic data reconstructionrandom missingdeep learningHaar wavelet transformefficient chan-nel attention
万方数据
维普
