# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 3 Experiments
### Field Example  
Figure 6a shows the raw hydrophone component data, and Figure 6b shows the raw Z-component data. It can be seen that the Z-component has a lower signal-to-noise ratio compared to the P-component data, which affects subsequent data processing and interpretation. The original horizontal X- and Y-component in the field data are shown in Figure 4, which are pre-processed and then sequentially fed into the network to predict the noise in the noisy Z-component (Figure 6b). The similarity between the shear wave noise in the Z-component and two horizontal component data suggests that it is reasonable to use the X- and Y-component data as the initial noise model. In the network training, the learning rate is set to 0.001, the batch size is fixed to 1, and the number of iterations is 1900. The network hyperparameters are selected by trial and error. To analyze the denoising results, we plot the original Z-component data and the denoising results by applying the proposed deep learning method for shear wave noise attenuation in Figure 7. Black arrows point out much obvious noise, and red arrows indicate signals. Figure 7b,c show the denoising results and predicted noise by the proposed method. The blue rectangle of the noisy Z-component is where the noise amplitudes are large. The noise overlaps the signal and even masks the valid signal. The proposed method in this paper effectively suppresses the shear wave noise. The magnified display results of the data corresponding to the blue rectangles in Figure 7 are shown in Figure 8. The black \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset 1-1** & **Dataset 1-2** & **Dataset 1-3** \\ \hline Noisy data & 6.55 dB & 0.53 dB & \(-\)5.49 dB \\ Denoising result & 16.12 dB & 13.47 dB & 9.47 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SNRs of synthetic data examples before and after denoising. Figure 6: Field OBN data example. (**a**) Raw P-component data. (**b**) Raw Z-component data. and red circles indicate the area where the noise and signal overlap. Our method exhibits effective denoising in these region. Particularly, the red circles highlight that our method achieves signal recovery while denoising. In addition, the continuity of the valid signal is improved. For field data, we cannot use SNR as a metric for evaluating the denoising results due to the unavailability of clean data. Inspired by [PERSON] and [PERSON] [19], we utilize the raw P-component data as a reference to evaluate the denoising results since the pressure wave recorded by a hydrophone is not disturbed by shear wave noise. Figure 9a shows the auto-correlation of P-component data used as a reference. Figure 9b shows a cross-correlation plot between the P-component and the raw Z-component, and Figure 9c shows a cross-correlation plot between the P-component and the denoised Z-component. Compared with Figure 9b, Figure 9c has a higher similarity with Figure 9a, which indicates that the proposed method effectively suppresses the shear noise while retaining the desired signal. Figure 8: Magnified display results of the blue rectangular areas in Figure 7. (**a**) Raw Z-component data. (**b**) Denoising result by the proposed method. (**c**) Predicted noise by the proposed method. The black and red circles indicate the area where noise and signal overlap. Figure 7: Field data example. (**a**) Raw Z-component data. (**b**) Denoising result by the proposed method. (**c**) Predicted noise by the proposed method. The blue rectangular areas are seismic data from time sampling points 500–1500. Black arrows point out several obvious noise and red arrows indicate signals. Furthermore, we estimate the local slope using the dip2d function in the Pyseistr package [44] to analyze the denoising effect. Figure 10a shows the estimated local slope of the P-component data which are considered as the reference indicating denosing performance.