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To improve the generalization ability of the single pooling (average or maximum pooling) skip connection residual network (SSN) for seismic random noise attenuation, we present a hybrid-pooling skip connection residual network (HSN). In HSN, the hybrid pooling consists of average and maximum pooling and aims to simultaneously capture the local and global features well, ultimately improving the detail recovery capability of HSN. To further improve the network performance and denoising ability of HSN, we propose a combined multi-branch selective kernel (CSK) hybrid-pooling skip connection residual network, which is referred to as CHSN. In CHSN, CSK consists of a three-branch selective kernel (TSK) and our suggested four-branch selective kernel (FSK), and aims to adaptively capture feature maps for high-accuracy effective information recovery. The superior random noise attenuation ability of CHSN is demonstrated in both synthetic three- and actual two-dimensional seismic data.

The processing efficiency of the widely used time-space (t-x) domain vertical seismic profiling (VSP) wavefield separation methods (such as median and singular value decomposition filtering) via one-dimensional discrete Fourier transform (DFT) depends on the wavefield separation method (or algorithm with processing parameter) and the total number of samples in the input VSP data. Once the wavefield separation method is determined, its processing efficiency is set and cannot be optimized. Although the popular frequency-wavenumber (f-k) domain VSP wavefield separation method via two-dimensional DFT has higher processing efficiency than the t-x domain VSP wavefield separation methods, its processing precision is susceptible to the spatial alias and Gibbs effects. For efficiency and precision improvements, we introduced the discrete cosine transform (DCT) operation into VSP wavefield separation for the first time, and proposed a high-efficiency and precision frequency-space (f-x) domain VSP wavefield separation method via DFT and DCT, in which the wavefield separation efficiency and precision can be optimized using the effective bandwidth cutoff frequency of the input VSP data. Based on the relationship between DFT and DCT, we combined their operations in the proposed method into one step (referred to as DCT–DFT) for further efficiency enhancement, thereby designing a high-efficiency and precision f-x domain VSP wavefield separation method via DCT–DFT. Theory analysis and synthetic and field VSP data examples show that the proposed method is highly efficient and precise, and can be widely used for three-dimensional (3D)-VSP data wavefield separation, especially for large distributed acoustic sensing (DAS)-VSP data.

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Deep learning-based automatic horizon tracking has achieved promising results but still faces serious cross-horizon phenomena. Therefore, based on multitask learning, seismic data characteristics, and the seismic horizon sequence (or position) relationship, we have developed a sequence-constrained multitask horizon tracking (SMHT) method for high-precision automatic horizon tracking. SMHT contains the horizon label automatic enrichment, the multitask horizon tracking network (MHTN), and the horizon sequence-constrained loss function. Horizon label automatic enrichment aims to automatically generate the upper and lower auxiliary horizon labels of the target horizon label, and then the horizon region labels corresponding to the auxiliary and target horizon labels for MHTN. MHTN contains the shared layer, the auxiliary task, the main task, and the horizon sequence-constrained horizon correction. In MHTN, the shared layer generates multiscale feature maps of the input seismic data. The auxiliary task uses the concept of object detection to extract the horizon region (or probability), and the main task extracts the high-precision horizon within the extracted horizon region. The horizon sequence-constrained horizon correction with the horizon sequence-constrained loss function aims to avoid the cross-horizon phenomenon and finally obtain precise horizon tracking results. Application of MHTN to two field 3D seismic data sets finds that SMHT performs well in automatic horizon tracking.