# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
## 3 Methodology  
We used a joint GAN-LSTM network to classify and identify seismic phases and noise signals. GAN is an unsupervised learning method developed by [PERSON] [30]. This method is composed of two neural networks, a generator G and a discriminator D. GAN trains on real seismic signal data. It trains D to maximize the accuracy of the discriminator model and trains G to minimize the accuracy of the discriminator model. The two neural networks compete with each other. When the generator has fully learned the distributions of real samples, the network reaches the optimal solution. The GAN discriminator network is a binary classifier of real and false data. After the GAN networks are trained, the convolutional layer part of the discriminator network can serve as a feature extractor of real seismic signals. Figure 5 shows seismic waveforms generated by the GAN generator. We can see that the generator restored the shape of the seismic waveforms quite well. It also indicates the GAN learned the features of the seismic waveforms after training using real seismic data. RNN is a typical deep learning model for processing time sequences. LSTM is one kind of RNN. LSTM network adjusts information flows by introducing the internal mechanism of a \"door\" and effectively overcomes the short memory of traditional neural networks. RNN is sequence sensitive, but its training speed is slow. Excessively long sequence inputs can lead to gradient disappearance or gradient explosion. With the help of the GAN discriminator, a feature sequence of the original time sequence with limited length is obtained. Using this feature sequence as the input for bidirectional LSTM, the LSTM and fully connected layer parameters are trained on seismic phase data and noise signal data for binary classification. \begin{table}
\begin{tabular}{c c} \hline \hline
**Phase** & **Count** \\ \hline P & 51,121 \\ \hline Pn & 4083 \\ \hline Lg & 4074 \\ \hline PKP & 2274 \\ \hline Sn & 2001 \\ \hline Pg & 784 \\ \hline S & 259 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Types of phases selected and their distributions. Figure 5: Seismic waveforms generated by the GAN generator. In this study, we used the DCGAN [31] architecture as the implementation of GAN. In order to improve the training efficiency of GAN, we adopted the following model construction regime: tanh was used instead of sigmoid as the last layer of the generator; normal distribution was used instead of uniform distribution to sample points in potential spaces; dropouts were used in the discriminator, and random noises were added into their identities; LeakyReLu was used instead of ReLu. We trained the GAN on 15s seismic waveform data (the sampling ratio was 40, the input data was 600 \(\times\) 1 dimension) as the input. For generator G, we used upper sampling plus convolutional layers to replace the pooling layer. On the output layer, we used tanh as the activation function. The input data were 50-dimensional Gaussian noise data. After a series of convolutions, we created 600 \(\times\) 1 one-dimensional waveform data consistent with the size of the training data. For the discriminator D, we use convolutional layers with a step size of 2 to replace the pooling layer; a total of three convolutional layers, and the convolution kernel of each layer was 5. The activation function was LeakyReLU. The slope was 0.2. The inputs were 600 \(\times\) 1 waveform data. The data dimension was reduced after a series of convolutional layers. The outputs were the binary classification results of seismic signals or noise signals. By connecting the discriminator D to the bidirectional LSTM network, we learned and modeled the sequential pattern of the data. The number of hidden layers on LSTM was set to 128. Finally, we connected the two fully connected layers to classify signals. The sigmoid binary activation function was used for the last fully connected layer. The outputs were the predicted probabilities of each sampling point belonging to seismic or noise.