# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
## 3 Methods  
There are several advantages of using CNN on seismic waveform data: (1) It operates directly on waveform data with little preprocessing and without feature extraction; (2) it is shifted invariant and not sensitive to the time position of the feature (P- or S-waves); and (3) it does not make explicit use of existing physics-based knowledge such as S-P difference, or seismic travel times. The proposed methodology used in this study is shown in Figure 1. First, three component wave-from data are downloaded from IRIS (Incorporated Research Institute for Seismology). After preprocessing, in the second step, we use a CNN to examine the waveforms of a specific station. The three-component waveform is processed by this CNN, and then a set of features is extracted from it. After that, the geographic location (latitude and longitude) of the seismic station is appended to the feature vector to create it. In the third step, this feature vector is used as an input by the second component, which is a GNN. This GNN will recombine the time series features with the station location to create a final station-specific feature vector. This procedure is carried out once for each of the \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Study** & **Model** & **Spatial Info** & **Year** & **Data** & **Station** \\ \hline
[17] & GCNN & Yes & 2022 &
\begin{tabular}{l} Italy and \\ California \\ \end{tabular} & Multiple \\
[18] & GCNN & Yes & 2020 & California & Multiple \\
[27] & CNN & No & 2019 & IRIS & Single \\
[16] & CNN & No & 2021 & Central Italy & Multiple \\
[1] & GCNN & & 2022 & – & Multiple \\
[26] & SVMR & No & 2018 & Bogota, Colombia & Single \\
[31] &
\begin{tabular}{l} CNN + LSTM + BiLSTM \\ + Transformer \\ \end{tabular} & No & 2020 & STEAD & Single \\
[15] & CNN and Graph & No & 2021 & MeSO-Net & Multiple, \\
[23] & CNN & – & 2021 & NIED Japan & Single \\
[23] & Deep CNN & – & 2021 & CARABOBO & Single \\
[2] & CNN and Team. & Yes & 2021 & Japan, Italy & Multiple \\ Transformer & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the literature is discussed in this study. Most of the studies do not use spatial information about events. stations that make up the network, utilizing the CNN and GNN components in the same way (i.e., the same operations are applied to each station individually). Only along the time axis are the processes that constitute the convolution carried out. Output is flattened and the final prediction about the magnitude, location, and depth is made finally. The architecture of the proposed model is given in detail in Figure 2.  
BNGCNN is derived from the model [18] to detect an earthquake event and predict its magnitude and location. We have modified the architecture and added CNN with a batch normalization layer for optimized training and to improve prediction accuracy. Input to the BNGCNN is an array size (50, N, 3) for all stations where the number of components in data is three, 50 is the number of stations and N is the number of samples with a sampling rate of 512 per second. As a result of the fact that seismic waveforms recorded at various stations have distinct biases, we preprocess the data in accordance with:  
\[y_{m}=x_{m}-\frac{1}{M}\sum_{i=1}^{M}x_{i}\ \,\ m=1,\ldots,M \tag{1}\]  
where \(x_{m}\) is the seismic waveform data, \(y_{m}\) is the converted seismic waveform data, and M is the total number of samples in the occurrence event, preprocessing has the effect of geometrically pushing the data center to the origin. The ordering of the stations is preserved and each waveform starts its origin time. We have normalized the waveform by its maximum value as it is recommended to improve CNN performance [16]. A stack of 5 feed-forward convolutional blocks where each block consists of 2-D convolution filters of size 1 \(\times\) 5 \(\times\) f\({}_{i}\) where f = {4, 8, 16, 32, 64} is the number of filters in the \(i\)th block. Convolution is performed on the time-axis and recombined into the filters of the next layer.