# U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
## 1 Introduction  
Seismic or acoustic wave signals are inevitably contaminated by noise from various sources in different environments, such as land and marine, affecting the quality of imaging, inversion, and interpretation [1, 2, 3, 4]. Hence, denoising is one of the most critical processing steps in oil and gas exploration or engineering survey, and it can significantly enhance the signal-to-noise ratio (SNR) and resolution of real data. Ground roll traffic noise in land surveys and swell noise in marine acquisition are very important ambient noises which have a high amplitude and mixed models. These noises are mixed with valid signals, making seismic records more chaotic, and the energy of noise is high, often covering an effective signal, reducing the quality of seismic data. Attenuating the noise in seismic records and improving the signal-to-noise ratio can make more effective use of seismic data and facilitate subsequent inversion, imaging, and other operations. In this article, we analyze the characteristics of road traffic noise and study how to attenuate traffic noise in seismic records to obtain effective signals. When sensors are deployed near a highway, the acquired data will be seriously polluted by traffic noise, which is the mixture of harmonic noise and ground roll from passing vehicles. Traffic noise belongs to a high-amplitude noise, and its characteristic is that its absolute amplitudes are more significant than most signals. Many kinds of literature have shown different ideas for suppressing high-amplitude noise effectively. [PERSON] and [PERSON] presented two techniques to solvethe coherent and non-coherent high-amplitude noise attenuation [5]. [PERSON] et al. proposed an iterative F-X prediction filtering approach to attenuate swell noise [6]. [PERSON] and [PERSON] proposed an automatic threshold-determination technique for detecting high-amplitude noise using statistical modeling [7]. As a kind of high-amplitude noise, road traffic noises are rarely mentioned in the literature, but they are very common in urban seismic or acoustic acquisition. The frequency band and amplitude level of traffic noise are comparable to data and are coupled with effective signals, so it is difficult to remove them. The F-X prediction method or the simple trace editing can suppress them in land data, but the methods are difficult for satisfying the demands of high precision. Domain transformation is required for noise removal by f-k filtering, Radon transformation, and other methods. Due to algorithmic mechanisms, these methods can generate artifacts that affect the use of effective signals. At the same time, in the f-k domain or Radon domain, effective signals and noise are often mixed together, making it difficult to completely separate. Traditional methods can cause the loss of effective signals when attenuating traffic noise. [PERSON] et al. introduce \(l_{p}-norm\) in the RPCA to effectively remove strong traffic noise, but the choice of parameter \(p\) in \(l_{p}-norm\) requires repeated experiments to achieve the best performance [8]. The traditional method requires human setting of parameters when attenuating noise. The increasing volume of seismic data and human manipulation not only affects efficiency, but also generates subjective errors. To overcome the shortcomings of traditional denoising methods, we introduce deep learning methods to more intelligently and efficiently attenuate traffic noise in seismic data. With the development of deep learning research in geophysics, deep learning methods are used to first break picking [9; 10], seismic data reconstruction [11; 12], inversion [13; 14; 15], noise attenuation [16; 17; 18; 19; 20; 21; 22], etc. The clever and automatic noise attenuation technique based on the deep neural network was studied as an essential direction; multi-layer network structures and nonlinear activation functions can solve complex nonlinear problems.