# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 1 Introduction  
Seismic signals are important for studying the Earth's internal structure, geological exploration, and resource development. By analyzing the propagation paths, velocities, and waveforms of seismic signals, researchers can infer the characteristics of the Earth's interior, including the properties and boundaries of the mantle, crust, and core [1, 2]. In the field of geological exploration, seismic signals provide a reliable means of geophysical detection for the exploration and development of resources such as oil and natural gas [3, 4]. By analyzing the propagation velocities, reflections, refractions, and other characteristics of seismic signals, explorers can deduce the types, thicknesses, and structures of subsurface rock layers, thereby determining the potential locations and scales of oil and gas reservoirs [5, 6]. However, seismic data are often contaminated by various types of noise, such as surface noise (e.g., wind and traffic), instrument noise (e.g., noise from the sensors themselves), and other man-made interference (e.g., electromagnetic interference and human activities). A significant amount of signal data is rendered unusable for further study due to noise interference, and signals containing noise can also introduce biases into research results [7]. For a long time, there has been a persistent need to extract true seismic signals from noisy data. Traditional seismic signal denoising methods, such as filtering, wavelet decomposition [8], S-transform [9], and empirical mode decomposition [10], aim to extract useful information and achieve denoising by transforming or decomposing the signal. These methods have the advantages of being fast and simple, but they also have many shortcomings. For example, Fourier-based filtering cannot remove noise with the same frequency asthe signal; wavelet transforms and S-transforms require manually selecting appropriate parameters, introducing subjectivity and uncertainty; empirical mode decomposition may suffer from mode mixing, boundary effects, and difficulty in determining the number of signal modes to decompose. Therefore, in the field of seismic signal denoising, it is crucial to seek a denoising method that does not rely on the experience of technicians, achieves superior denoising performance, and has a relatively fast processing speed. In recent years, with the continuous development of artificial intelligence technology, deep learning has become a research hotspot due to its unique advantages. Time domain deep learning methods for seismic signal denoising have experienced rapid development. [PERSON] et al. [11] improved upon DnCNN by adding downsampling and upscaling operations, reducing the training time and memory requirements. [PERSON] et al. [12] proposed a multi-scale feature extraction DnCNN model with a hierarchical structure capable of extracting features at different scales and utilizing the fused features to more effectively extract seismic data information. [PERSON] et al. [13] proposed a multiscale CNN based on U-Net. They adjusted the shape of the convolution kernel to fit the shape of the seismic data and used dilated convolutions to extract multiscale features. [PERSON] et al. [14] added an attention module after the convolutional layers in the residual blocks and added channel attention to the skip connections to remove interference. Time-frequency domain-based seismic signal denoising methods are scarce, and there remains room for improvement. DeepDenoiser [15] obtains the time-frequency spectra of seismic signals via short-time Fourier transform and employs a U-Net-based model for denoising in the time-frequency domain. DnRDB [16] improves upon DeepDenoiser by introducing residual dense block (RDB), further enhancing the SNR. ARDU [17] combines dilated convolutions with RDB, and the SNR improved by 2.9 dB compared to DnRDB. Addressing the issue of existing methods underperforming in denoising low SNR signals, this paper proposes an innovative seismic signal denoising method based on generative adversarial networks.