# Accelerating High-Resolution Seismic Imaging by Using Deep Learning
## 3 Results
The predicted patches and the ground truth high-resolution imaging result that migrated with the QPSTM are shown in Figure 11. It can be observed that the predicted patches are almost the same as the ground truth patches, which indicate that the network has a high generalization ability. To realize end-to-end learning, the predicted patches are combined into one fully size seismic imaging profile by using the overlapped sliding window method described in the Methods section and performed energy balancing as described in Formula (2). Finally, the predicted profile is obtained, as shown in Figure 12. For comparison, we use the conventional imaging result obtained using the PSTM (shown in Figure 13) and the imaging profile result obtained using the high-resolution image method (shown in Figure 14). It is obvious that the proposed method can achieve the same imaging effect as the high-resolution imaging method, which requires a large computation time. Both the methods considerably improve the resolution and show sufficiently detailed information of the structure. To intuitively illustrate the improvement, the spectra of the three profiles are compared, as shown in  
Figure 10: Panel (**a**) shows theicker wavelet, whereas panel (**b**) shows the corresponding predicted result using the trained convolutional neural network (CNN). The black lines in panel (**a,b**) represent the respective waveforms. The red line and blue line in panel (**c**) represent the spectrums of the ricker wavelet and its predicted result, respectively. The trained model can improve the dominated frequency of the ricker wavelet from 30 Hz to approximately 40 Hz. In addition, the phase is corrected, as expected. Figure 15. The frequency when using the learning method improved by approximately 16 Hz at the high-frequency end at \(-20\) dB, compared with that for the conventional PSTM result. Both the learned network and the QPSTM exhibit a comparable performance in terms of the frequency improvement. Figure 11. True QPSTM patches and patches for unseen data, predicted using the network. Patches in panel (**a**) represent the input of the network. The predicted patches (shown in panel (**b**)) are almost the same as the true patches (panel (**c**)), which indicates that the network has a high generalization ability. Figure 12. Predicted image using the proposed method. The proposed method can achieve the same imaging effect as that of the high-resolution imaging method, which requires a large computation time. Both the methods considerably improve the resolution and reflect detailed information of the structure. Figure 9.0 True high-resolution image. Figure 9.1 Conventional PSTM image. A total of 304 patches exist in the predicted QPSTM result, as shown in Figure 9.2 The prediction of each patch by the trained CNN requires approximately 10 ms when using one TITAN Xp GPU, which means that computing the profile (1001 (vertical) \(\times\) 601 (inline) samples) shown in Figure 14 requires only 3 s. In contrast, more than 32 h are required to compute this profile when using the QPSTM method with one TITAN Xp GPU. In fact, we need to compute 437 profiles for this field data totally. In detail, with the deep learning method, we need to prepare three QPSTM images used for the network as the training samples, which is called data generation cost and takes about 96 h. It takes about 4 h to train the network to a fine state with these samples. The prediction process, which is the procedure to calculate the 437 images by feeding small-patches into the network, takes 0.4 h. As shown in Table 2, it takes 100.4 h for the deep learning method to calculate these results with one GPU, while the QPSTM method takes 13,984 h to get similar results. The computing efficiency ultimately improved more than 100 times. Hence, the efficiency of the high-resolution imaging procedure combined with the end-to-end method is significantly improved compared with that of the QPSTM method.