# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
## 2 Related Work  
Traditional machine learning requires user knowledge to extract meaningful features from the data. These feature selection or extraction methods heavily affect the performance of these models like decision trees, support vector machines, and naive Bayes [19]. Ref. [10] designed an earthquake early warning system using SVM to predict magnitude and peak ground velocity. The proposed system can effectively generate an alert at different levels from 0 to 3. Further, applying feature selection methods to the raw data is time-consuming and more prone to errors. Deep learning models perform feature selection automatically using multiple hidden layers. These layers are also used for dimensionality reduction which makes deep learning models more powerful for processing nonlinear and complex data [20]. The architecture of deep learning models such as graph neural networks and multilayer perceptron represents effectively spatial information such as stations and their relationship [18]. Deep learning models have shown superior performance to traditional machine learning models on a variety of tasks such as image processing [21], text classification [22], blockchain, etc. Deep learning has three mainstream architectures CNN [23], GNN [5], and RNN which are inherited from the machine learning model known as multi-layer perceptron (MLP) [18]. MLP has a fully connected layered architecture that is powerful but because of its high computational time, the depth of the model is limited. The advanced architectures have multiple layers of different types (convolutional, pooling, dropout, softmax) to learn complex data and improve performance. Refs. [9; 24] used an ANN-based MLP model to assess the safety of existing buildings. Results show that the model outperforms the others to classify concrete structure damage. Ref. [25] proposed a neural network-based forecasting model to predict earthquake intensity. Experiments show that DeepShake can effectively predict an earthquake five seconds before the event. In some studies, CNN-based models have been used for seismic data processing, feature extraction, and classification. Ref. [16] investigated the CNN for rapid earthquake detection and epicenter classification from the single station waveform data. Experiments on three-component waveform data obtained from IRIS show that the proposed CNN-based model achieves 87% accuracy to predict earthquake sources over a broad range of distances and magnitudes. Ref. [16] used CNN to predict the ground shaking intensity of earthquake after 15-20 s after earthquake origin time. Ref. [23] proposed two CNN-based models to estimate the seismic response of the surface. Ref. [20] used a deep CNN model for earthquake detection and source region estimation. The proposed models predict fairly well the amplitude and the natural periods. All these studies used single-station waveform data and do not consider location information in prediction. Recently, a hybrid deep learning model GCNN is becoming popular for earthquake detection using seismic data. Ref. [17] used the GNN model to show that along with the time series data sensor location information can be exploited using graph-based networks. Experimental results on two seismic datasets containing earthquake waveforms show promising results. Refs. [1; 18] proposed a deep CNN and GNN model for earthquake events classification from multiple stations. CNN layers aggregate features from the waveform data and combining these features with spatial information of stations helps the GNN for accurate prediction of an earthquake. Ref. [15] proposed a graph partitioning-based model that uses both CNN and GNN for earthquake detection from seismic array data. They used data from multiple seismic stations but the spatial information of these stations was ignored completely. Different events can be detected by multiple stations in different locations. Multiple stations enable a generation of heterogeneous data but there exists a relationship between the observations collected from multiple stations [1].