# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 1 Introduction  
In the quest to refine seismic signal denoising techniques, a variety of traditional approaches have been explored, including the Fourier Transform, Wavelet Transform (WT), and Empirical Mode Decomposition (EMD). The Fourier Transform, a foundational method in seismic signal analysis, facilitates the transition of signals from the time domain to the frequency domain, targeting noise elimination through frequency range differentiation [1]. Yet, this method falls short in capturing simultaneous time and frequency characteristics, hindering the comprehensive analysis of nonstationary seismic signals. Wavelet Transform, on the other hand, extends the analysis to a two-dimensional time-frequency domain, offering a more nuanced processing of nonstationary signals [2]. However, its efficacy is contingent upon the selection of an appropriate threshold and basis function [3]. EMD offers a decomposition technique that operates based on the signal's intrinsic time-scale attributes, yet it graphes with issues like mode aliasing and the challenge of selecting the right intrinsic mode functions [4, 5]. Advanced versions of EMD, such as EEMD, CEEMDAN, and ICEEMDAN, have been developed to address these limitations and provide more robust denoising capabilities [6, 7, 8].  
Since 2012, with the advent of AlexNet's victory at the ILSVRC, deep neural networks have revolutionized fields like image recognition and noise reduction [9], leveraging their superior feature extraction and nonlinear mapping capabilities, along with reduced human intervention and computational costs. This has prompted researchers to explore their application in seismic signal denoising. In 2018, [PERSON] and colleagues employed a multi-layer convolutional neural network for adaptive noise reduction [10], outperforming traditional methods. However, the effectiveness of such networks is constrained by their depth, with shallower networks failing to capture detailed signal features and deeper networks susceptible to gradient vanishing. [PERSON] and others validated the potential of multi-layer CNNs in seismic data, while [PERSON]'s team introduced an auto-encoder that leverages deep residual networks to mitigate gradient issues [11]. Despite these advances, challenges remain in distinguishing between seismic signals and noise, especially with nonstationary signals.  
In 2019, [PERSON] and colleagues addressed the scarcity of labeled data in seismic denoising by employing data augmentation and CNN techniques [12]. [PERSON] utilized sparse auto-encoders to reduce noise while preserving data details, and [PERSON] investigated a U-Net-based method, DeepDenoiser, for noise suppression. Yet, U-Net's reliance on shallow layer features can lead to the loss of significant signal information, impacting SNR improvement [13]. [PERSON] and [PERSON], in 2020, focused on regional noise suppression and predictive noise modeling through deep learning, respectively [14]. Despite the potential of deep learning to enhance denoising, issues such as low SNRs and signal distortion persist.  
To overcome these challenges, this study introduces a DnCNN-based seismic signal denoising model. The DnCNN network is a convolutional neural network that combines batch normalization and residual learning. It uses the DnCNN residual learning method to efficiently and quickly learn the noise in seismic signals and then remove the learned noise from the noisy seismic signals.The DnCNN model can use the residual learning method to reduce the noise of seismic signals with different SNR without manual intervention. This model is designed to enhance the SNR of noisy seismic signals and to minimize distortion of the waveform, offering a promising advancement in the field of seismic signal processing.