# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 2 Related Work
[26] used a fully connected dense network with an attention layer to extract time-frequency features for earthquake detection from the Mediterranean dataset. Ref. [14] applied batch normalization and attention layers in a GCNN model for magnitude prediction. The results show that the proposed method outperformed the classical CNN and GCN models. Ref. [21] showed that the transformer model with an attention layer performs significantly better than the deep learning and traditional phase-picking and detection algorithms. Instead of using a single model, we are interested in employing multiple models in a sequence to perform earthquake detection tasks, also known as ensemble learning or multiple classifier systems. Ensemble systems have gained popularity in computational intelligence and machine learning communities in recent decades. Ensemble systems have shown their efficacy and versatility in a wide range of problem areas and real-world applications like text classification [17], traffic accident detection [27], earthquake detection [28], and earthquake causality prediction [10]. In the last couple of decades, multiple classifier or ensemble learning systems have grown in computational intelligence. Ref. [11] proposed a machine learning-based ensemble method that combines SVM, k-NN, DT, and RF to design an ensemble model that can effectively detect earthquakes. Ref. [29] ensembled a CNN and LSTM and proposed MagNet for earthquake magnitude estimation. Ref. [12] proposed an LSTM-GRU-based ensemble method that outperformed the LSTM and GRU on two datasets. Ref. [9] ensembled four machine learning models, AdaBoost, XGBoost, DT, and LightXGBoost, in a stack using multiple settings. Ensemble-based studies report that ensemble models outperform individual models of either machine learning or deep learning. There are two types of ensemble techniques: parallel and sequential. Base predictors are trained in parallel on the input, as shown in Figure 1a. The final result is determined by the majority voting method based on the results of the individual base models. The parallel ensemble utilizes many CPU cores, allowing for simultaneous predictions and model execution. As shown in Figure 1b, the base models used in the sequential ensemble technique are trained sequentially, with the output of one base model serving as the input to the next base model along with the input dataset. The next base model attempts to overcome the issues introduced by the preceding base model to enhance the accuracy of the overall detection system [18]. There are two possible types of base models: homogeneous and heterogeneous. In the homogenous learning environment, only one machine learning model (such as DT or NB) is trained in parallel or sequentially. However, many machine learning models are trained in parallel or sequentially in the heterogeneous learning environment. The ensemble learning strategy is advantageous when heterogeneous models are used as the base models [17]. The models under a heterogeneous approach can vary in terms of their feature sets, data for training, and evaluation criteria. A summary of the related work is given in Table 1. Most of the studies used convolutional models rather than recurrent models. The attention mechanism is also used in most of the studies. Batch normalization is more popular than the layer normalization method. Batch normalization is effective with convolutional models, while layer normalization performs better with recurrent models. Most ensemble methods use convolutional models. Batch normalization is not an effective technique for a small batch size because it depends on batch size, while layer normalization is independent of batch size and can be applied to any batch size. Each feature in the mini-batch is normalized separately using batch normalization. When using layer normalization, all features are normalized independently for each input in the batch.