# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 2 Related Work  
Earthquake detection using machine learning models such as SVM, DT, and k-NN has grown popular. Ref. [4] proposed an on-site EES for magnitude and ground velocity detection using multiple features and the SVM method. The early warning system issues alerts at different levels when the magnitude of an event exceeds a threshold value. Ref. [5] proposed an improved k-means clustering algorithm based on space-time-magnitude (STM) to cluster the earthquake events from global earthquake catalogs. The proposed model outperforms the modified k-means algorithm and the classic k-means method on various cluster sizes. Ref. [11] used k-NN, SVM, DT, and RF for earthquake detection, resulting in RF outperforming the other models. Several factors like feature selection method, dataset size, and class imbalance distribution affect the performance of machine learning models [17; 18]. To overcome the machine learning issues, deep learning has been introduced. Deep learning is a subfield of machine learning. Like artificial neural networks, deep learning-based models use multiple layers to input and process data and output the results. Deep learning models are considered good when the dataset is complex and significant because it employs several layers to deal with such data. Ref. [6] used a CNN for earthquake event classification into macro, micro, and artificial earthquakes. The proposed method outperformed the existing state-of-the-art methods on the Korean earthquake database. On the same database, the proposed CNN and GCN-based deep learning model of [1] outperformed the methods to detect earthquake events from multiple stations. Ref. [7] proposed a method that uses a CNN and graph partitioning algorithms to detect events with extremely low signal-to-noise ratios. Ref. [8] used an RNN-based DeepShake model, and Ref. [19] used a CNN model to predict the earthquake shaking intensity from ground motion observation. Ref. [20] used LSTM and BLSTM for magnitude prediction. Ref. [2] proposed a transformer-based TEAM method that issues accurate and timely warnings. Model interpretation, hyperparameter tuning, and GPU-based special devices are the major issues with deep learning models [14; 15]. To overcome the issues of deep learning and machine learning methods, research studies propose three approaches: layer or batch normalization [3; 15], the attention mechanism in deep learning architectures [6; 21], and ensemble learning [9; 11]. To efficiently train deep learning models, batch normalization is effective. It standardizes the layer outputs using data from each small training batch. It speeds up the training process by eliminating the necessity for the precise initialization of the parameters and enabling the use of high learning rates in a safe manner [15; 22]. Several recent studies showed that using layer normalization and batch normalization in deep learning models improves their computational and detection performance. Ref. [14] used the batch normalization method in a GCNN model for earthquake source characterization. Ref. [15] showed that training of a deep learning model can be enhanced using batch normalization. Layer normalization is mainly used with RNN models to improve training and increase prediction [23]. When a model layer is normalized, all neurons receive the same distribution of features for the same input. Batching is no longer necessary when features and inputs to a particular layer are individually normalized [24; 25]. This is why sequence models, such as RNNs, benefit significantly from layer normalization. Earthquake detection is complex because it does not show any specific pattern, unlike object detection from images and text classification. The attention mechanism helps the deep learning model to give more attention to the most relevant and essential features from the input features for inference [6; 26]. Ref. [16] used LSTM with an attention layer that effectively detects large-magnitude earthquake events and outperformed the LSTM and ANN models. Ref.