Seismic Interpolation Transformer for Consecutively Missing Data: A Case Study in DAS-VSP Data

Distributed optical fiber acoustic sensing (DAS) is a rapidly developed seismic acquisition technology with the advantages of low cost, high resolution, high sensitivity, small interval, etc. Nonetheless, consecutively missing cases often appear in real seismic data acquired by the DAS system due to some factors, including optical fiber damage and inferior coupling between cable and well. Recently, some deep-learning (DL) seismic interpolation methods based on convolutional neural networks (CNN) have shown impressive performance in regular and random missing cases but still remain the consecutively missing case a challenging task. The main reason is that the weight sharing makes it difficult for CNN to capture enough comprehensive features. In this article, we propose a transformer-based interpolation method, called seismic interpolation transformer (SIT), to deal with the consecutively missing case. This proposed SIT is an encoder-decoder structure connected by some U-shaped swin-transformer (UST) blocks. In the encoder and decoder part, the multihead self-attention (MSA) mechanism is used to capture global features which is essential for the reconstruction of consecutively missing traces. The UST blocks are utilized to perform feature extraction operations on feature maps with different resolutions. Moreover, we introduce the SSIM loss to optimize the training process of SIT. In experiments, this proposed SIT outperforms A-Net and swin-transformer (ST). Moreover, ablation studies also demonstrate the advantages of new network architecture and loss function.