# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
## 4 Results  
### Datasets  
This study uses a dataset from Southern California. The area of study is important because it is the economic and social hub of the states. The dataset consists of three component raw waveforms (BHN, BHE, BHZ) recorded directly from seismic stations. N oriented north-west, E oriented west-east, and Z oriented vertically. We extracted 120 s long time windows that contained the onsets of both P- and S-wave arrivals for all the available events. An earthquake with a magnitude less than 3.0 with no depth cut-off was not considered. Each station provides a simultaneous sampling of three channels with a 24-bit resolution. Each waveform file is converted to a sac file using python libraries. Waves are filtered at a frequency range from 0.1-8.0 Hz and interpolate onto a time base \(1<t<101\) s after the event origin time, over 512 evenly spaced time samples (5 Hz sampling frequency). We considered the waveform length starting from its origin time to 120 s after its end time.  
We use a python library ObsPy to collect data for datasets [32; 33]. ObsPy downloads the broadband inventory and earthquake catalog of the Southern California Seismic Network (SCSN (doi:10.7914/SN/CI)). In the dataset, there is a total of 1427 events collected from 187 stations from the period 31 January 2000 to 31 December 2015 (15 years). For both seismic stations and event locations, we set the limits for latitude from [32\({}^{\circ}\) to 36\({}^{\circ}\)] and longitude [\(-\)120\({}^{\circ}\) to \(-\)116\({}^{\circ}\)]. Maps of stations and events considered in this study are given in Figure 3. The locations of these seismic stations are shown as triangles in Figure 3a and the event locations are shown as dots in Figure 3b. Statistics of the dataset are given in Table 2.  
The magnitude distribution and depth distribution of each event are shown as Histograms in Figure 4a,b. Magnitude distribution shows that events are not equally distributed concerning the magnitude values. Half of the events in the dataset have magnitude ranges from 3.0 to 3.13. Only a few events have a magnitude greater than 3.9. The average magnitude value of all the events is 3.3. On the other side depth distribution, more than 60% of events have a depth range from 1.7 km to 10.2 km. Only a few events have a depth of more than 20.0 km. The average value of the depth in the dataset is 9.0 km.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Properties** & **Values** & **Properties** & **Values** \\ \hline Period & 2000–2015 & Min. and Max. Latitude & [32\({}^{\circ}\) to 36\({}^{\circ}\)] \\ No. of events & 1427 & Min and Max. Longitude & [\(-120^{\circ}\) to 116\({}^{\circ}\)] \\ No. of stations & 187 & Minimum magnitude & 3.0 \\ Filter the waveform & 0.1–8 Hz & Even spaced time sample & 2048 Hz \\ No. of stations & 187 & Scaled Min. max. source depth & 0 to 30 km \\ Scaled magnitude & 3–6 & Time-base & \(1<t<101\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the Southern California dataset used in this study.  
Figure 4: _Cont._  
Figure 3: (**a**) Shows the number of stations and their location for the Southern California region. (**b**) Event distribution for California. The depth and magnitude of an event are encoded by its color and size.