# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
## 2 Multi-Scale Supersymmetric Network (Cycle-JNet) Method
### Cycle-JNet  
The UNet network is called UNet because of its symmetrical structure, which resembles the letter U. The structure is shown in Figure 2: the gray layer is Input/Output Layers, the orange layer is Convolution Layers, the red layer is Downsampling Layers, the green layer is Upsampling Layers, and the red layer is Channel (channel is one of the dimensions of the input data or feature map, which is different types of features in the same spatial position). The network mainly extracts the detailed feature information of the data by using Encoder and decodes and restores the size of the original data by using Decoder, which can effectively extract the details in the seismic data after wavelet transform. Although the resolution of well curve data (about 0.625 ms) is much higher than that of seismic data (2 ms), the resolution of the well curve data generated is actually consistent with that of seismic data due to the symmetry of its structure when using the UNet network for training. It not only reduces the accuracy of machine learning, but the seismic resolution of Vsh properties produced by the trained network inversion is also low, which is not conducive to the identification of sand bodies of different thickness. In this study, several layers of convolution kernel are added to the output end of the UNet network to expand the data generated by the machine, so that the resolution of the output end is greatly increased, which is consistent with the well data. Because its structure is like the letter \"J\", it is named JNet. The structure is shown in Figure 3; the legend in the figure is consistent with that in Figure 2. The original network is called \"thick sand identification network (TSIN)\", which can distinguish and learn the thick sand body and muddy layers in seismic data. The convolution kernel expanded at the output end is called \"super-resolution thin sand identification network (SR-TSIN)\", and it can distinguish and learn the details of high-resolution seismic data after wavelet transform, so as to improve the data learning and output seismic body resolution. The Cycle-JNet network built in this study uses the following hyperparameters: The batch size of samples processed at one time is 10; the sample size (well vibration data) is 128 \(\times\) 4 \(\times\) 4; the sampling interval was 8; learning factor lr = 0.001; initial channel = 2, convolution kernel size = 4, stride = 2, padding = 1. There are seven convolution layers in the discriminant network and 11 deconvolution layers in the generative network (seven in TSIN; SR-TSIN has five layers). The input channel of the first layer X\({}_{0}\) is 2, and the output channel is 128. The input channel of the second layer X\({}_{1}\) of the discriminant network is 128, and the output channel is 256. The input channel and output channel of the third layer X\({}_{2}\) of discriminant network are 256 and 512, respectively. The input channel of the fourth layer X\({}_{3}\) is 512 and the output channel is 1024. The input channel of the fifth layer X\({}_{4}\) is 1024, and the output channel is 2048. The input channel and output channel of X\({}_{5}\) of the sixth layer of  
Figure 3: Schematic diagram of the JNet neural network structure. the discriminant network are 2048 and 4096, respectively. The input channel and output channel of the seventh layer X\({}_{6}\) of the discriminant network are 4096 and 4096, respectively. The input channel and output channel of the first layer X\({}_{7}\) of the generation network are 4096 and 4096, respectively. The input channel of the second layer X\({}_{8}\) of the generation network is 8192, and the output channel is 2048. The input channel of the third layer X\({}_{9}\) of the generation network is 4096, and the output channel is 1024. The input channel of the fourth layer X\({}_{10}\) of the generation network is 2048, and the output channel is 512. The input channel of Layer 5 X\({}_{11}\) of the generation network is 1024, and the output channel is 256.