## 1 Introduction  
In recent years, the conventional onshore oil and gas reserves have been declining, leading to a shift in research focus from conventional oil and gas to deep complex strata oil and gas exploration [1]. Consequently, seismic exploration technology has emerged as an indispensable tool for deep oil and gas exploration. Nonetheless, the intricate surface conditions and the absorption and attenuation effects of deep layers have led to the escalating complexity of low-frequency intense ground roll features, accompanied by a simultaneous weakening of the energy carried by real reflected waves. This deleteriously impacts the signal-to-noise ratio of seismic records and impairs the precision of subsequent data processing and interpretation. Furthermore, due to the ground roll and reflected waves having similar frequency characteristics and temporal forms, and their overlap in both the frequency and spatiotemporal domains, this blurs the boundary between ground roll and real reflected waves, increasing the difficulty of separating ground roll from reflected waves. In order to meet the requirements of seismic exploration and improve the quality of deep pre-stack seismic records, experts and scholars have put forward various techniques for suppressing ground roll noise. These techniques can be classified into two main categories: model-based methods that suppress ground roll and deep learning algorithm-based methods that also target ground roll suppression. The first category, model-based ground roll suppression methods, mainly includes frequency domain bandpassfiltering [2; 3; 4; 5], F-K (Frequency-Wavenumber) fan filtering [6; 7], wavelet filtering [8; 9; 10; 11; 12; 13], curvelet filtering [7; 14; 15; 16; 17], the t-p transform (Travel-time and Slowness Transform) [18], singular value spectrum analysis [19], the hyperbolic interference method [20], morphological component analysis [21; 22], and other methods [23; 24; 25; 26]. The core idea is to utilize the differences in characteristics such as time, space, frequency, wave velocity, and morphology between ground roll and real reflected waves to design a ground roll prediction model and separate the ground roll from seismic records. However, these methods only consider the characteristics of ground roll in one aspect to establish mathematical models, which have limited expressive power and cannot describe the complex distribution of ground roll noise. This affects the accuracy of model assumptions and parameter settings, leading to the destruction of real reflected waves during the ground roll suppression process. The second category, which relies on deep learning [27; 28; 29] algorithms for ground roll suppression, primarily involves training deep neural networks with extensive paired seismic data. In recent years, deep learning has been widely applied in the processing of seismic data [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. However, its current focus primarily lies in suppressing random noise [42; 43], while the application of deep learning algorithms for mitigating ground roll [44; 45; 46] remains limited. These networks learn the distinct features of seismic data and automatically distinguish between ground roll and real reflected waves, thereby successfully suppressing ground roll noise. The ground roll suppression methods based on the GAN (Generative Adversarial Network) studied by [PERSON] [47] require the utilization of model-based ground roll suppression methods to suppress ground roll noise in actual seismic records as labeled data, followed by training deep learning networks to achieve ground roll suppression. However, in practical applications, it is impractical to obtain actual seismic data containing ground roll and corresponding denoised data. Due to the lack of high-quality training data, the above-mentioned two methods cannot fully achieve ground roll suppression.