  Distributed optical fiber acoustic sensing (DAS) is a rapidly-developed
seismic acquisition technology with advantages of low cost, high resolution,
high sensitivity, and small interval, etc. Nonetheless, consecutively missing
cases often appear in real seismic data acquired by DAS system due to some
factors, including optical fiber damage and inferior coupling between cable and
well. Recently, some deep-learning seismic interpolation methods based on
convolutional neural network (CNN) have shown impressive performance in regular
and random missing cases but still remain the consecutively missing case as a
challenging task. The main reason is that the weight sharing makes it difficult
for CNN to capture enough comprehensive features. In this paper, 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
blocks. In encoder and decoder part, the multi-head self-attention (MSA)
mechanism is used to capture global features which is essential for the
reconstruction of consecutively missing traces. The U-shaped swin-transformer
blocks are utilized to perform feature extraction operations on feature maps
with different resolutions. Moreover, we combine the loss based on structural
similarity index (SSIM) and L1 norm to propose a novel loss function for SIT.
In experiments, this proposed SIT outperforms U-Net and swin-transformer.
Moreover, ablation studies also demonstrate the advantages of new network
architecture and loss function.
