# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
## 2 Dataset  
The data used in this study are from the IMS array MKAR. The seismic phase data come from IDC's analyst-reviewed event bulletin (LEB) from 2012 to 2018, including P and S phases such as Pg, Pn, P, PKP, Lg, Sn, and S that have good f-k qualities. Figure 1 shows the distribution of seismic events associated with these phases. Using the arrival time, azimuth, and slowness data corrected by manual analysis, we beamformed the data of subsites at the array. Then we removed the linear trend of the beamformed data, filtered them over the 0.8-3.5 Hz passband, and used the result as input for the deep neural network. Figure 2 shows the root-mean-square (rms) signal-to-noise ratio (SNR) distribution of the selected dataset, where signal and noise are defined by the rms amplitude in a window 10 s after and 5 s before the arrival time, respectively. The noise dataset comprises signals recorded between 2013 and 2015 that are automatically detected and identified as \"N\" and have not been changed to seismic phases by analysts, as well as those corrected as \"N\" by manual analysis. We also limited our selection of noise signals to having no event within 2 min of the detection time. For noise signals, it is also necessary to beamform the data of subsites of the array. As noise signals do not associate with any event, we can only beamform them according to their automatically detected arrival times, azimuth, and slowness.  
A seismic array is composed of multiple sensors located in a certain range, and the signal-to-noise ratio of far-field signals can be effectively improved by means of delay and sum beamforming. By summing the coherent part of seismic signals at the different array sites using a time delay calculated according to the site's location relative to the reference site and a given slowness vector, the seismic signals are enhanced, and the incoherent noise is canceled out. The theoretical improvement of the SNR of the N element seismic array is be \(\sqrt{N}\) through the \"delay and sum\" process. The beamforming data of MKAR was used to train the joint network to achieve better performance in our method. For historical seismic phase data, the real azimuth and slowness of a phase are obtained based on the final event location and used to calculate the delay of each subsite in the array relative to the reference station, and then the vertical channels data of all subsites are delayed and summed to create the beamforming data of this phase. Figure 3 compares the original waveforms of each site of MKAR with the beamforming data. We train the GAN on the beamforming data of the array, effectively improving the identification ability for weak signals of small-magnitude events. Figure 4 shows the beamformed data of different detected phases.  
Figure 1: Distribution of IDC LEB events from 2012 to 2018, which associate MKAR phases.  
Figure 2: The rms SNR of seismic signals. The window of the signal is 10s after the arrival time, and the window of noise is 5 s before the arrival time.  
A total of 64,596 seismic phase records were obtained. Table 1 lists the number of each phase. We then preprocessed the data, including (1) truncating the waveform data over a period of 15 s from 5 s before to 10 s after arrival time; (2) linear detrending processing; (3) filtering over the 0.8-3.5 Hz band; and (4) amplitude normalization.  
Figure 4: Beamforming data of different phases.  
Figure 3: Comparison of the individual subsite waveforms and beamforming waveforms. (**a**) beamforming waveform. (**b**) vertical channel waveform of each site of MKAR.