U-Net++ Based Subshallow Gas-Scattered Image Conditioning: Small-Scale Case Study of Seismic Data Acquired in the Korean East Sea

Data acquired through seismic surveys suffers from information loss for various reasons. Among these, shallow gas affects signals beneath it and causes distortion of seismic wave signals. These distorted signals often cause difficulties in interpreting seismic data. Various preceding research has been conducted to mitigate this issue, and this study presents an interpolation method using self-supervised machine learning. The dataset we use consists of small-scale marine seismic survey data acquired from the Youngil Bay near the eastern continental margin of the Korean Peninsula. The 2-D planes are randomly extracted from 3-D seismic data to generate training data of various sizes, which are then used to create an artificial neural network model. This study utilizes the U-Net++ architecture with an attention module to overcome the limitations associated with U-Net and to focus and emphasize repetitive patterns. In the training process, both the
$L1$
loss function and the structural similarity index (SSIM) loss function are used as hybrid loss functions. This enables the network to recognize both the overall patterns of the data and the key patterns to focus on. The neural network model created through this study shows the successful performance in detecting patterns in the original data and interpolating missing values of seismic data. The output data generated through the proposed network improves stratigraphic continuity by interpolating the missing values of the original data and restoring the lost structure.