# A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
## 2 Multi-Scale Supersymmetric Network (Cycle-JNet) Method
### Cycle-JNet
The input channel of X\({}_{12}\) of the sixth layer of the generation network is 512, and the output channel is 128. The input channel of the seventh layer X\({}_{13}\) of the generation network is 256, and the output channel is 128. The input channel of the eighth layer X\({}_{14}\) of the generation network is 128, and the output channel is 64. The input channel of X\({}_{15}\) at layer 9 of the generation network is 64, and the output channel is 32. The input channel of X\({}_{16}\) of the tenth layer of the generation network is 32, and the output channel is 16. The input channel of X\({}_{17}\) of the eleventh layer of the generation network is 16, and the output channel is 8. The input channel of Layer 12 X\({}_{18}\) of the generation network is 8, and the output channel is 2. The optimization function is Adam, the loss function is MCEloss, the activation function of layers X\({}_{0}\) to X\({}_{17}\) is LeakyReLU, layer X\({}_{18}\) is Sigmoid, and the epoch is 200. Since JNet is a convolution-based inversion network, the learning ability of the global information correlation of seismic data is defective [25], which affects the accuracy of inversion results. Therefore, a Cycle network is introduced to improve the accuracy of machine learning. In actual network operation, due to the 1D nature of well curve data, there are only two scales of effective information when db1 wavelet transform is performed. Therefore, the well shock data will be decomposed into eight results after DWT. However, due to the 1D nature of well shock data, there are only two effective results, namely low-resolution well shock data (aaa) and high-resolution well shock data (daa), and the other results are the mapping of them in different dimensions. Based on the two-resolution information, in order to improve efficiency and accuracy, the Cycle-JNet network is designed as two parallel aaa networks and daa networks; the structure is shown in Figure 4. Take the aaa network as an example, where \"real\" is defined as actual well shock data, \"fake\" is defined as machine-generated fake well shock data, and \"R\" and \"F\" are short for \"real\" and \"fake\". In the process of network operation, the low-frequency seismic data of real seismic data Segy (real) after DWT are called Segy (R-aaa), and the high-frequency seismic data are called Segy (R-daa). Segy (R-aaa) is input into the forward convolution network JNet (S1-aaa) of the aaa network to obtain Vsh (F-aaa), which is a false well curve corresponding to Segy (R-aaa), that is, the Vsh curve considered by the machine in the aaa network. The purpose of forward training is to let the network learn the correspondence between seismic data and well data, and linearly add the residuals obtained by the two aaa networks and daa networks to obtain the residual Loss1 (S1) of the JNet (S1) network, that is, the weight of the S1 network under the network. Then, Vsh (F-aaa) output from JNet (S1-aaa) is reversely input into the JNet (S2) of the aaa network to convolve Vsh (F-aaa) and generate false seismic data Segy (F-aaa) generated according to Vsh (F-aaa) under the aaa network. At the same time, the daa network generates the corresponding false seismic data Segy (F-daa), and the purpose of reverse training is to let the network learn the correspondence between well data and seismic data, carry out inverse wavelet transform (IWT) on Segy (F-aaa) and Segy (F-daa), and combine the seismic data of the two scales into one. By comparing the fake seismic data SegY (fake) with the real seismic data SegY (real), the residual Loss2 (S2) of the JNet (S2) network can be obtained, that is, the weight of the S2 network under the network. Total residual Loss (S) is the linear addition of Loss1 (S1) and Loss2 (S2). Using Loss (S) to optimize JNet (S1) and JNet (S2), the machine is then allowed to enter the next round of training and learning. Thus, the forward training network JNet (S1-aaa) and JNet (S1-daa) with ideal results can be obtained in multiple samples and long training time.