# A Strong Noise Reduction Network for Seismic Records
## 1 Introduction  
The quest for accurate seismic data is fundamental to the field of geophysics, underpinning a broad spectrum of applications from mineral exploration to the understanding of the Earth's tectonic activity [1, 2]. However, seismic records are often plagued by noise that obscures signals of interest, thereby complicating subsequent analyses. These noises, including geological activities, human interference, and equipment failures, significantly degrade the quality of seismic data and the accuracy of interpretations [3]. Consequently, the noise reduction of seismic records is essential for the subsequent analysis and processing. Traditional seismic data denoising methods predominantly utilize mathematical models and signal processing techniques, such as wavelet threshold denoising [4, 5, 6, 7, 8, 9], Fourier transform [10, 11, 12], and empirical mode decomposition (EMD) [13, 14, 15, 16, 17, 18]. Wavelet threshold denoising achieves denoising by processing noisy signals in the wavelet domain. Fourier transform improves the signal clarity and quality by filtering noise in the frequency domain. EMD can effectively handle nonlinear and non-stationary signals without the need for preset basis functions, and it can adaptively decompose different frequency components, making signal analysis more flexible and accurate. Despite their utility, these methods often yield suboptimal results and are time-consuming when applied to complex seismic data [19]. They are also susceptible to the subjective selection of feature functions andthreshold parameters, particularly when noise overwhelms the signal, resulting in a low SNR [20]. The advent of deep learning technology, with convolutional neural networks leading advancements in image and speech denoising, has demonstrated the power of feature learning and nonlinear mapping capabilities [21; 22; 23; 24; 25; 26; 27]. Although deep learning techniques require a large amount of data for models to be trained, they have significant advantages over traditional methods because the models are able to autonomously learn and differentiate the features of complex seismic signals and noise without the need for the process of human extraction of features [28; 29; 30; 31; 32; 33; 34; 35]. Consequently, an increasing number of researchers are employing deep learning for seismic data denoising [36; 37]. In 2018, [PERSON] et al. addressed seismic data denoising using deep residual networks to learn intricate data and noise features, achieving more effective denoising [38]. [PERSON] et al. 's DeepDenoiser in 2019 employed an encoder-decoder structure to learn a set of sparse features, effectively separating signal from noise [39]. However, the model's encoder down-sampling, achieved through convolution, faced challenges in extracting sufficient seismic signal features. In 2021, [PERSON] et al. introduced a seismic signal denoising model (DnRDB) that incorporated a residual dense block (RDB), deepening the model's structure with multiple RDB modules to extract more relevant feature information [20]. [PERSON] et al. 's SEDENOSS method in 2022 utilized a two-path recurrent neural network for effective seismic signal separation and denoising, thereby enhancing the signal quality and accuracy [40]. In 2023, [PERSON] et al. merged atrous convolution with RDB within the U-Net framework, creating the ARDU network, which improved the feature extraction capabilities without increasing the network parameters, reduced the waveform distortion, and preserved the effective signals [41]. Nonetheless, when noise and signal are in the same frequency band [42] or when excessive noise results in a signal-to-noise ratio below 0 dB, the denoising performance of these models is unsatisfactory. This is attributed to the excessively low signal-to-noise ratio and the overwhelming interference of noise, which hamper the model's signal feature extraction. In conclusion, this paper introduces a model tailored for denoising seismic signals with extremely low signal-to-noise ratios.