# Research on Seismic Signal Analysis Based on Machine Learning
## 3 Methodology  
### Description of the Dataset  
Fujian Province has a large number of historical and modern earthquakes due to its proximity to the Taiwan subduction zone. There are four NE trending fault zones nearly parallel to the coastline and several NW trending faults almost perpendicular to the coastline. The continuous activity of these faults leads to frequent natural earthquakes in this area [39]. In addition, because Fujian is adjacent to the Taiwan Strait, it is a research area attracting many scholars to study the crust and upper mantle structure on both sides of the Taiwan Strait and adjacent areas by artificial blasting [39]. These blast data have caused great interference for the Fujian seismological network to accurately identify natural earthquake events. These abundant blasting data in this area provide a solid data basis for our work in this paper. The earthquakes and blast data recorded by XYSC, DSXP, FDQY, FZCM, and YXBM stations of the Fujian earthquake monitoring network from 2012 to 2019 were selected as the research object, as shown in Figure 1 and Table 1. These stations all use broadband recorders, which can save the frequency information of different earthquake types well.  
The seismic waveforms containing the main information of event records with high recording quality and not submerged by noise were selected as input data of model training and testing for deep learning training. Each input seismic signal is 20 s of data, and the three channels (E-W, N-S, and Z) of the same station were combined into one vector data, that is,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Station Name**} & **Number of** & \multirow{2}{*}{**Number of Blasts**} & \multirow{2}{*}{**Total**} \\  & **Earthquakes** & & \\ \hline XYSC & 3296 & 1674 & 4970 \\ DSXP & 3297 & 207 & 3504 \\ FDQY & 632 & 749 & 1381 \\ FZCM & 795 & 810 & 1605 \\ YXBM & 1774 & 1804 & 3578 \\ Sum of waves & 9794 & 5244 & 15,038 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of waveforms recorded at each station.  
Figure 1: Overview of the study data. (**a–c**) Station distribution and the waves number in the 5 stations. (**d–f**) Noise, blast, and earthquake signal sample.  
6000 sampling point waveforms. The time series data recording of the seismic waveform started 2 s before the arrival of the nearest P wave and ended at least at the beginning and end attenuation of the S wave so as to ensure complete waveform recording in the time window. If the epicenter distance is too large, the complete S wave cannot be recorded within the 20 s time window. Therefore, we only kept stations with complete records to ensure that the selected stations were free of interruptions and interference within the 20 s time window. Each piece of waveform vector data was stored in the folder corresponding to the event type as a data unit processed by the neural network. A total of 9794 natural earthquakes, 5244 artificial blasts, and 7000 background noises were intercepted in this paper. In terms of label making, label 0 was set to earthquake events, label 1 was blast events, label 2 was background noise, and Figure 1 is the time-frequency diagram of the three types of data. From the time-frequency analysis, the frequency range of noise data was less than 5 Hz, the frequency range of blasts was about 0-20 Hz, and the frequency range of earthquakes was about 0-40 Hz (Figure 1).  
We directly input the seismic event waveform into the deep learning network structure and then output its recognition results. Our network adopts many 'convolution' and'sampling' operations, and then uses the fully connected layer to realize the mapping between the input data and the output target.