# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## 3 Results
### Field Data  
The first field seismic data were obtained by microseismic monitoring in a well with 35 magnitude geophone. The distance between the two geophones is 15 m, and the geophone depth was 1800-2310 m. The sampling rate was 0.5 ms. The downhole microseismic data has 35 traces and time length is 0.5 s. The field seismic data has robust random noise and apparent intense amplitude noise, as shown in Figure 9a. When denoising the field data, the parameters of the used window were set to n = 15 traces and m = 50 time samples, and the other parameters chosen for the improved SVD filtering were the same as those used for processing synthetic seismic data. As seen in Figure 9b, a large amount of random noise is removed when traditional SVD filtering (k = 1) is used. However, a significant amount of noise still exists near the non-effective signal. When performing SVD filtering denoising with K = 2, the retained noise is more relative to K = 1, as illustrated in Figure 9c. The conventional SVD filtering (K = 1 and K = 2) minimally suppresses intense amplitude noise. However, the noise was eliminated in the region without seismic signals when the data were denoised by the improved SVD filtering. When the noise was attenuated, the seismic events were well preserved in the vicinity of the seismic signal, and the intense amplitude noise was eliminated (see Figure 9d). In general, the improved SVD filtering significantly improves the denoising of seismic data compared with conventional SVD filtering. Here, the results of SVD filtering were processed using low-pass filtering from 0 to 240 Hz. Since SNR cannot be calculated directly from field seismic data, the SNR of field seismic data and denoised seismic data were calculated by taking 0.2-0.225 s as seismic signal and 0.35-0.375 s as noise. The SNR of field seismic data is 11.3336 dB. After denoising the seismic data with the traditional SVD filter (K = 1, K = 2), the SNR of the seismic data is  
Figure 8: Filtered results were achieved using conventional SVD filtering with (**a**) K = 1 and (**b**) K = 2; (**c**) improved SVD filtering. 18.5297 dB and 16.8173 dB, respectively. After the improved SVD filter was used to denoise the seismic data, the noise part was basically removed, so there was a numerical calculation error, and the SNR of the seismic data was 65.7588 dB. The comparison of SNR shows that the improved SVD filter has an obvious denoising effect (Table 4). The second field seismic data were obtained by 31 magnitude geophones with a level spacing of 15 m and the depth of the geophone sinking was 1800-2250 m; the explosive source was placed in the adjacent well. The sampling rate was 1 ms. This downhole microseismic data comprised 31 traces and time length was 3.5 s. In Figure 10a, it can be seen that the 0.5 to 2 s contains a large amount of random noise, and there is erratic intense amplitude noise from 2.5 to 3.5 s. The parameters chosen for the improved SVD filtering were the same as those used to process the synthetic seismic data. The filtered results were compared to the conventional SVD filtering results. Figure 10b shows the denoised data obtained using conventional SVD filtering (K = 1). Most random noises were suppressed, but the erratic intense amplitude noise from 2.5 to 3.5 s could not be attenuated. At the same time, the seismic events near 2.5 s were also discontinuous and partially missing due to the interference of intense amplitude noise. After denoising the data with conventional SVD filtering (K = 2), the seismic signal was preserved, but the noise was also present. The conventional method did not achieve the effect of noise suppression (see Figure 10c). The noise was mainly removed from the seismic data by the improved SVD filtering rather than the original method. The irregular intense amplitude noise from 2.5 to 3.5 s was attenuated. The seismic events with complex characteristics were better preserved (Figure 10d).