Combined multi-branch selective kernel hybrid-pooling skip connection residual network for seismic random noise attenuation

Abstract

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.

A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors

“…In order to address the inevitable loss of data detail in downsampling, skip connections were integrated. These connections bridged the downsampling and upsampling stages, preserving crucial information and enhancing the model's ability to reconstruct and predict data more accurately [41].…”