# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
###### Abstract  
Earthquake is a major hazard to humans, buildings, and infrastructure. Early warning systems should detect an earthquake and issue a warning with earthquake information such as location, magnitude, and depth. Earthquake detection from raw waveform data using deep learning models such as graph neural networks (GNN) is becoming an important research area. The multilayered structure of the GNN with a number of epochs takes more training time. It is also hard to train the model with saturating nonlinearities. The batch normalization technique is applied to each mini-batch to reduce epochs in training and obtain a steady distribution of activation values. It improves model training and prediction accuracy. This study proposes a deep learning model batch normalization graph convolutional neural network (BNGCNN) for early earthquake detection. It consists of two main components: CNN and GNN. Input to the CNN model is multi-station and three-component waveform data with magnitude \(\geq\) 3.0 were collected from January 2000 to January 2015 for Southern California. The extracted features of CNN are appended with location information and input to GNN model for earthquake detection. After hyperparameter tuning of the BNGCNN, when testing and evaluating the model on the Southern California dataset, our method shows promising results to the baseline model GNN by obtaining a low error rate to predict the magnitude, depth, and location of an earthquake. batch normalization; deep learning; earthquake detection; graph convolution network; seismic network +
Footnote †: journal:  
structure damage. 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. The major drawback of machine learning techniques is their dependency on feature selection techniques. Several feature selection methods have been proposed in the past but several comparative studies show that there is no universal feature selection method that works well with all types of data. In contrast to traditional machine learning, convolutional filters in the convolutional layer extract feature automatically, and therefore, deep learning models outperform the traditional machine learning models on various tasks [11]. An important factor that can increase the performance of deep learning models is hyper-parameter tuning [12; 13]. A model has several parameters such as batch size, dropout, learning rate, activation function, number of epochs, and number of convolutional filters. Finding the best value of these parameters (also called parameter tuning) is a time-consuming and resource-exhausting process. Batch normalization is a technique used for more stable and faster training of deep learning models [13; 14]. Recently, deep learning techniques such as convolutional neural networks (CNN), graph neural networks (GNNs), and their ensemble models graph convolutional neural networks (GCNNs) have shown good performance for earthquake detection [15; 16]. CNN has strong power to extract useful information from the seismic raw waveform data. Convolutional layers in the CNN model can extract contextual local features from input waveform data and by applying pooling operation it can learn global features. Combining these features with spatial information of stations helps for the accurate prediction of an earthquake. Adding batch normalization in CNN results in faster and more stable training of the model. GNNs have been designed particularly to process data arising from networks [17]. CNN with the GNN model has shown very good performance on seismic data processing in several studies [18]. Several past attempts for earthquake detection using deep learning techniques have some shortcomings. Ref. [18] used deep learning-based GNN method for seismic source characterization and appended latitude and longitude information with data to extract features from CNN.