## 1 Introduction  
In the acquisition of seismic data, there is a risk of losing some of the data due to economic and environmental constraints, mechanical issues, and other such factors. In particular, the use of conventional streamer systems in marine seismic exploration, in particular, often leads to spatial aliasing problems because these systems tend to have close inline intervals and wide crossline intervals in order to maximize the system's survey area. This loss of data can have a negative effect on the subsequent processing and interpretation of data, and it may be necessary to conduct additional surveys in order to restore the data, which incurs additional time and financial costs [1]. To improve the resolution of 3D seismic data, trace interpolation in the crossline direction is required. With the advancement of machine learning, there has been an increase in research into various machine learning-based interpolation methods. [PERSON] et al. (2020) proposed a convolutional neural network model using U-Net architecture for the interpolation of 2D seismic data. The proposed model outperformed the FGFT (fast-generalized Fourier transform) interpolation method, and the synthetic and field data had higher signal-to-noise ratios [2,3]. [PERSON] et al. (2019) proposed a TF-CGAN (conditional generative adversarial networks in time and frequency domain) model using a cGAN model. The TF-CGAN model uses frequency domain information from Fourier transform in its generator and discriminator to increase the prediction accuracy of the model [4]. [PERSON] et al. (2022) proposed a CA-Unet (coordinate attention Unet) model based on a U-Net with a coordinate attention block. The CA-Unet model uses a coordinate attention block to optimize the use of feature maps and achieve high-accuracy predictions [5]. [PERSON] et al. (2022) proposed a CF-Unet (coarse-refine Unet) model using a coarse-refine U-Net with frequency wavenumber (FK) loss. The CF-Unet model solves the over-smoothing problem and accelerates network convergence with FK loss while mitigating the high-frequency loss problem [6]. [PERSON] et al. (2019) proposed a model consisting of two generators and one discriminator using the Cycle GAN structure. The proposed model showed an improved prediction speed and accuracy compared to the RNA method [7] in frequency spatial prediction filter interpolation [8]. [PERSON] et al. (2022) proposed a multi-resolution U-Net model that utilizes the correlation of 3D data for interpolating 3D seismic data. The model outperformed the traditional U-Net model as it was able to accurately predict weak events and utilize both horizontal and vertical correlations. The model also reduced computation costs by pre-setting initial CNN weights [9]. [PERSON] et al. (2022) proposed a deep tensor autoencoder (DTAE) model that is able to learn data-based, nonlinear, and high-dimensional mapping. The DTAE model accurately captures relationships between traces even when using data associated with complex geologic structures, resulting in accurate predictions [10]. [PERSON] et al. (2023) proposed a DS-U-Net model based on 3D U-Net, which uses depthwise separable convolution instead of traditional convolution. Depthwise separable convolution reduces the computational cost and the number of parameters by dividing traditional convolution into two parts: depthwise convolution and pointwise convolution [11]. Compared to other machine learning algorithms, convolutional neural network (CNN) based and generative adversarial network (GAN) based algorithms are widely used in the field of seismic data interpolation. [PERSON] and [PERSON] (2022) conducted an evaluation and comparison of the U-Net and conditional Wasserstein generative adversarial network (cWGAN) to determine an interpolation method that is able to accurately reconstruct 2D seismic data. After thorough experiments, they concluded that cWGAN achieved higher accuracy and excellent performance compared to U-Net [12].