# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
## 3 Discussion of the Analysis Results
### Artificial Intelligence Seismic Analysis  
In the intelligent seismic training, the JNet network randomly selects ten \(1\times 1\times 128\) seismic data samples from seismic data for learning. Each seismic data sample contains a section of seismic curve and a Vsh curve of equal length, in which the input wave of seismic data is seismic wave. The seismic waves were input into JNet (S1) for training, and the training results were recorded, as shown in Figure 5. The vertical axis on the left of the figure represents the value of Vsh curve (that is the sand-mud ratio, the value ranging from 0 to 1. The smaller the value, the higher the sand body composition and the better the sand body, and the larger the value, the higher the mud composition). The vertical axis on the right represents the amplitude of the seismic curve (\(-\)56,053\(-\)67,456 Hz), and the horizontal axis represents the depth of the curve (1350\(-\)1600 ms). The artificial intelligence Vsh curve generated by the generator (the green curve in Figure 5), the real Vsh curve (the red curve in Figure 5), and the input seismic curve (the blue curve in Figure 5) were compared, and some typical curves of one training round, 50 training rounds, 100 training rounds, and 200 training rounds were selected for comparison. The artificial intelligence Vsh curve generated by the generator (the green curve in Figure 5) and the well Vsh curve (the red curve in Figure 5) were compared to obtain the coincidence rate of the generator generation curve under different training rounds. When comparing the three curves, it was found that the resolution of the seismic curve was much lower than that of the well Vsh curve, but the artificial intelligence Vsh curve successfully expanded the resolution through the unique structure of JNet and was consistent with the well Vsh curve.  
Figure 4: Multi-scale super-asymmetric Cycle-JNet network design.  
As can be seen from Figure 5a, during the first training of the machine, the Vsh curve generated by the generator was irregular with great fluctuation, poor coincidence rate with the well Vsh curve, almost no correlation with the seismic curve, and an insufficient learning of the characteristics of the well seismic relationship. After 50 rounds of learning, the artificial intelligence Vsh curve in Figure 5b began to show certain rules and had a certain coincidence rate with the well Vsh curve, and it showed a certain correlation with the seismic curve. After 100 rounds of training, the Vsh curve generated by the generator in Figure 5c had a 90% coincidence rate with the well Vsh curve, which was strongly correlated with the seismic curve and had an obvious learning effect, indicating that the neural network is sensitive to changes in well seismic data and has strong learning ability. However, as shown in Figure 5d, after 200 rounds of training, the efficiency improved slightly compared to after 100 rounds of training. Both of them showed the high efficiency and accuracy of the training, with the Vsh curves generated by the two generators having a very high coincidence rate with the well Vsh curves and the differences in the coincidence rate between the two being small. If the training continues, the network conformity rate increases very little, so the training rounds are set at 200 rounds, which reflects the high efficiency and accuracy of the training.