# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
## 7 Analysis and Discussion  
We trained the GAN on beamformed data of an array by taking advantage of its unsupervised learning ability, enabling the GAN to learn seismic data features. We connected the discriminator network to the LSTM network into a classifier, trained this network on seismic and noise data, fixed the parameters of the discriminator network and only trained the LSTM network parameters, and finally, successfully achieved the seismic signals and noise signals classification. First, the GAN discriminator network was composed of several convolutional layers, enabling it to extract the features of seismic signals effectively. After rounds of training, we compared the seismic signal waveforms generated by the GAN generator network with the original waveforms to determine whether the GAN discriminator network learned the features of these data. LSTM can effectively process sequence data. If original waveform data are directly inputted, as the data volume is large, the training speed will be too slow. When the data features extracted by the GAN discriminator network are inputted into the LSTM, not only the input data volume is reduced, the major features of the data are also retained. At the same time, for the GAN, because the discriminator is the result of competing with the generator, the performance would be better than the simple CNN in terms of feature extraction. Figure 14: The identification results of MKAR seismic phases. (In the **a**–**p** subfigure, the blue “...... “ denotes a real signal category,1 means seismic phase, 0 means noise signal; the yellow “—” denotes a model-predicted signal category. The predicted results of these samples are consistent with actual results.). Traditional detection methods usually divide the monitored band range into a number of narrow bands to perform detection and identification in each of these narrow bands, then combine the results of multiple bands. After statistical analysis of the monitoring 10-year signals at MKAR, we can see that in the above detection band, the SNR of noise signals is sometimes greater than the detection threshold, but in a broad band of 0.8-3.5 Hz, the signal energy is commonly low. As the convolutional layers of deep CNN are intended to learn local patterns, they can examine each small piece of the neural network more intensively to obtain more abstract features, and the entire CNN can learn the spatial hierarchies of features. For this reason, we used the GAN discriminator network to perform the feature extraction function, allowing intensive analysis of different dimensional features of the input waveform data. Although it does not perform narrow-band filtering of the input data as the traditional detection methods perform, it can still obtain satisfactory classification and identification results. In this study, first, we trained the GAN on phase features, using its discriminator network as a feature extractor and its generator network as a generator of new phase data. The IDC automatically detected phases include regional P and S phases and teleseismic phases. From the phases listed in Table 1, we can see that the number of phases distributions is very uneven. In order to apply the deep learning method to IDC phase identification, a large volume of training data with roughly equal identified data distribution is required. In this sense, the GAN generator network can play a role. For example, if we train the GAN on data of a type of phase, we can generate data of this type of phase. These data possess the typical features of this type of phase but are not completely the same and have a degree of randomness. It is similar to the rotation, cropping, and change in color in image processing techniques, which can be used as a data enhancement method. The generated phase data can effectively enlarge the training data set and improve the performance of the model. In the records of IMS stations, there are teleseismic, regional, and local phases, among which the number of teleseismic phases is far more than that of local phases.