深度特征融合的双卷积自编码器VSP波场分离方法OA

The separation of upgoing and downgoing waves in vertical seismic profiling(VSP)data is a key tech-nique for suppressing interference in seismic signals and improving the imaging accuracy of subsurface struc-tures.Existing VSP wavefield separation methods often rely on high-accuracy velocity models or manually la-beled training datasets,resulting in limited applicability and insufficient wavefield separation accuracy.To ad-dress these problems,a VSP wavefield separation method based on a dual convolutional autoencoder(DCAE)with deep feature fusion is proposed.First,F-K filtering is applied to the original VSP data for wavefield separa-tion to obtain initial information on the upgoing and downgoing wavefields.Subsequently,the features of the preliminarily separated wavefields are deeply fused with those of the original VSP data.A shared convolutional autoencoder architecture is adopted,and an attention mechanism is introduced to enhance the model's ability to adaptively extract key wavefield features.Finally,the downgoing and upgoing waves are separately fed into their respective autoencoders and processed through feature fusion and the attention mechanism,after which the decoders produce accurate wavefield separation results.By constructing a loss function constrained by the conti-nuity of upgoing and downgoing seismic events,the method implements an unsupervised learning strategy that does not require manually labeled datasets.Experimental results obtained using both synthetic and field VSP data demonstrate that the proposed method can accurately and effectively separate upgoing and downgoing wavefields in VSP data and has promising application potential in seismic exploration imaging.