# U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
## 4 Numerical Results
### Real Data  
In this section, we verify the validity of the ACB-UNet on real data. As the geophones are deployed near the highway, the real data are heavily contaminated by traffic noise, and the noise has a variety of forms. We further prove the effectiveness of the ACB-UNet method by attenuating the noise in real data.  
Figure 12 shows a 3D pre-stack seismic record, which is not fed to the network for training. The record has 360 traces, and the trace interval is 25 m. Each trace has 3501 samples with a time interval of 2 ms. As seen from the figures, the traffic noise is most serious near traces No. 60 and No. 120, which are distributed in a fan shape and cover the reflections of the data.  
Figure 10: The (**a**) f-k spectra of the data in Figure 8a; (**b**) f-k spectra of the data in Figure 9e.  
Figure 9: The denoising results and noise sections of different methods for synthetic data: (**a**) the denoising result and (**b**) noise section using f-k filtering; (**c**) the denoising result and (**d**) noise section using the UNet; (**e**) the denoising result and (**f**) noise section using the ACB-UNet.  
Figure 11: (a) Amplitude spectra of Figures 8a,c and 9e; (b) enlarged rectangular area in (a).  
Figure 12: The original data with traffic noise.  
residual noise still exists in the record. Figure 13b shows the noise section processed with f-k filtering. There is some signal leakage to the noise section, especially since the effective signal loss near the first break is serious. The denoising results of the UNet are shown in Figure 13c, and Figure 13d is the noise section. The method effectively eliminates traffic noise, and the covered reflections are restored. However, there are some artifacts in the denoising results of the UNet (as shown by the red arrow in Figure 13c), and its accuracy is preferable to further improvement. Figure 8e shows the results processed by the ACB-UNet. Compared with other methods, the denoising result of the ACB-UNet has almost invisible traffic noise and no errors such as artifacts. For further analysis and comparison, we enlarge the rectangular area in Figure 13c,e to obtain Figure 14a,b. As shown in Figure 14a, the denoising results of the UNet still have relatively serious artifact problems (as shown by the black arrows). Comparing the area marked by the blue arrows, we can see that the ACBs remove noise while effectively retaining the signal. It has better detailing capabilities than the UNet, demonstrating the advantage of an asymmetric convolution kernel.  
We analyze the data before and after denoising on the f-k spectra. Figure 15a,b show the f-k spectra of the original data and the ACB-UNet denoising result. Figure 15 shows that the traffic noise overlaps with reflections in 0-30 Hz, and the traffic noise is strong in energy, covering almost all of the effective signals. It can also be inferred from this figure that the noise overlapped with the signal, leading to the unsatisfactory denoising effect of the f-k filter. Figure 15b shows the f-k spectrum of the data in Figure 13e. The traffic noise is invisible in the f-k spectra of the data processed by the ACB-UNet. It is evident that the ACB-Net denoising performance is better than other methods.  
Figure 13: The denoising results and noise sections of different methods for real data: (**a**) The denoising result and (**b**) noise section using f-k filtering; (**c**) the denoising result and (**d**) noise section using the UNet; (**e**) the denoising result and (**f**) noise section using the ACB-UNet.  
Figure 14: The enlarged rectangular area of (**a**) the UNet and (**b**) the ACB-UNet denoising results.  
Figure 15: (**a**) The f-k spectrum of original data; (**b**) the f-k spectrum of the ACB-UNet de-noised data.