## 1 Introduction  
In the field of geophysical exploration, seismic profile signals carry critical information about subsurface medium structures and physical properties, serving as the core basis for characterizing geological structures, identifying oil-gas reservoirs, and assessing disaster risks [1]. However, in practical exploration, seismic trace data loss is prevalent due to complex field topography, observation equipment layout constraints, and economic considerations. Industry statistics show that decimation ratios in conventional seismic acquisition often reach 10-50%, compromising the continuity of event horizons in seismic signalsand attenuating effective wave energy [2]. This leads to issues such as blurred seismic imaging and increased reservoir parameter inversion errors. Seismic signal reconstruction technology aims to recover complete, high-fidelity seismic signals from sparse and noisy observation data, with the core goal of maximizing the restoration of true subsurface geological structure information, providing precise data support for energy exploration and geological safety assessment. Thus, it has become an indispensable key link in seismic data processing workflows [3]. Traditional seismic signal reconstruction methods primarily rely on signal processing theories and mathematical optimization models, such as wavelet transform, curvelet transform, and sparse inversion. These methods reconstruct missing data by constructing sparse representation models of signals and imposing regularization constraints. They demonstrate effectiveness in scenarios characterized by simple geological structures and low noise levels, where the underlying signal sparsity assumptions align well with the data characteristics. However, when facing complex and variable geological structures in practical exploration, as well as issues like strong random noise and multiple-wave interference, the limitations of traditional methods become increasingly prominent. Relying on manually designed transform basis functions, they struggle to adapt to the non-stationary nature and multi-scale feature coupling characteristics of seismic signals, often leading to phenomena such as event horizon disconnection and weak signal loss in reconstruction results [4; 5; 6]. Furthermore, as seismic exploration advances into deep and complex regions, data scale grows exponentially, making traditional methods computationally intensive and inefficient, failing to meet the needs of modern seismic exploration for rapid processing of massive data. In recent years, deep learning has demonstrated great potential in seismic signal processing due to its powerful nonlinear feature learning capabilities. Convolutional neural networks (CNNs) can automatically extract local features of seismic signals through multi-layer convolution and pooling operations, attaining remarkable achievements in tasks such as seismic denoising and first-break picking. U-Net and its derivative architectures use an encoder-decoder symmetric structure with skip connections to effectively fuse shallow and deep features, achieving high precision in seismic signal reconstruction. In particular, Transformer-based models like Swin-Unet, with their hierarchical window self-attention mechanism, can capture long-range dependencies between seismic traces, providing a new approach for restoring event horizon continuity and being widely applied in seismic data processing [7; 8; 9]. Despite the progress made by deep learning in seismic signal reconstruction, existing methods still have limitations. On one hand, seismic signals contain multi-scale information ranging from high-frequency waveform details (corresponding to thin-layer reflections) to low-frequency structural trends (corresponding to large-scale geological interfaces), while traditional convolutional networks with fixed receptive fields struggle to efficiently extract features at different scales simultaneously.