## 1 Introduction  
Oil and gas resources are not only the cornerstone of social and economic development but also the guarantee of national security. With the construction of the national digital detection network, the observation methods and technologies of the new Internet of Things are gradually being applied, which basically realizes the digitization, networking, and integration of seismic monitoring and exploration, and the ability to carry out seismic monitoring and exploration has been greatly improved. The application of digital technology provides rich first-hand data for seismic scientific research and lays a good foundation for explanations and predictions of seismic exploration. Noise attenuation is crucial to obtain high-quality data from the seismic signals that are collected with geophone sensing equipment and networks. But these are usually badly interfered with and distorted by noises in seismic exploration. This has an important impact on oil and gas exploration. Therefore, this paper aimed to obtain high-quality seismic data to delicately portray the subsurface geological structure and make a more reliable geological interpretation. While acquiring seismic data, the reflected seismic signals are recorded together with incoherent noise by geophones. These noises seriously interfere with real seismic data and produce distortion in seismic exploration. Therefore, noise suppression remains a pivotal step for obtaining high-quality seismic signals that can reliably inform oil and gas exploration decisions. Currently, many algorithms for seismic noise suppression, e.g., the first seismic denoising approach [1], denoising approaches based on the transform domain [2; 3; 4] and sparse transform [5; 6; 7; 8], learning-based approaches [9; 10], and other types of methods [11; 12; 13; 14], have been developed. In fact, random noise, which is the most common seismic noise, can penetrate the whole time domain, severely distorting and interfering with real seismic data. Since the seminal random noise suppression method introduced by [PERSON] [1], multiple refinements and alternative techniques have emerged, including empirical mode decomposition (EMD)-based approaches, sparse transform-based approaches, variational mode decomposition techniques, and fast dictionary learning-based approaches [10]. [PERSON] and [PERSON] [4] proposed an f-x empirical mode decomposition-based algorithm, where the random noise is removed by using predictive filtering. [PERSON] and [PERSON] [6] presented a new EMD-seislet transform-based algorithm for removing the random noise from seismic data. [PERSON] et al. [7] reported a variational mode decomposition approach to suppress seismic random noise. In fact, a widely used and effective method of seismic random noise suppression algorithms is based on the multi-scale geometric analysis sparse transform. [PERSON] et al. [2] built a wavelet transform-based denoising algorithm with an improved seismic data resolution. [PERSON] et al. reported a curvelet transform-based random noise attenuation method [3], and various subsequent variants [8; 12] also achieved satisfactory results. [PERSON] [14] suggested a seismic random noise suppression method based on the decomposition performed by three-dimensional (3D) steerable pyramids. Notably, [PERSON] et al. [15] extended the potential of wavelet analysis in seismic applications by employing circular harmonic wavelets for the generation and analysis of non-stationary ground motions. With the increasing use of current social intelligence, more and more fields are introducing artificial intelligence and deep learning (DL) algorithms [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] to solve related problems, including seismic denoising. [PERSON] et al. [27] implemented the automatic identification and localization of geological faults based on machine learning and deep learning methods, respectively. [PERSON] et al.