# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
###### Abstract
Model interpretation, hyperparameter tuning, and GPU-based special devices are significant issues with deep learning models. Past research studies propose three approaches to overcome the problems of deep learning and machine learning methods: layer or batch normalization, the attention mechanism in deep learning architectures, and ensemble learning. Multiple or ensemble learning models have grown in computational intelligence in the last couple of decades. Different models (i.e., base classifiers) are organized sequentially or parallel to the design of a more powerful ensemble model. An ensemble model may consist of only machine learning models, deep learning models, or hybrid models. Ensemble-based studies report that ensemble models outperform the individual models of either machine learning or deep learning [9; 11]. In machine learning-based ensemble studies, [PERSON]. [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. [9] ensembled four machine learning models, AdaBoost, XGBoost, DT, and LightXGBoost, in a stack using multiple settings. Another stacked-based ensembled model was proposed in [10] to ensemble three models, bagging, AdaBoost, and stacking, for earthquake causality prediction. Ref. [12] 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. [13] proposed a hybrid model and used SVM and three ANN models for earthquake prediction. At different stations, the same earthquake event may be recorded differently. Applying normalization techniques like batch normalization and layer normalization can improve classification performance due to their normalization effects on raw seismic data. Within each layer, the feature maps are first standardized using the mean and standard deviation,and subsequently, these maps are transformed into standardized values using either a shift factor or a scale factor. Several recent studies showed that using layer normalization and batch normalization layers in deep learning models improves their computational and detection performance. Ref. [14] used batch normalization techniques in a GCNN model for earthquake source characterization. Ref. [15] showed that the training of a deep learning model can be enhanced using batch normalization. When a model uses layer normalization, all the cells receive the same feature distribution independently for each batch input. Ref. [16] used LSTM with an attention layer that effectively detected large-magnitude earthquake events and outperformed the LSTM and ANN models. Ref. [17] proposed an attention-based fully connected CNN model, and Ref. [16] used the attention layer in the LSTM model for earthquake detection. Ref. [15] applied batch normalization and attention layers in a GCNN model for magnitude detection. Taking advantage of both normalization and ensemble technique methods, in this study, we propose an RNN-based stacked normalized recurrent neural network (SNRNN) model that ensembles three recurrent neural network models in a stack: GRU, LSTM, and SimpleRNN. A GRU is an RNN model that uses less memory and is faster when the data have longer sequences. The LSTM and GRU used their gates to handle the gradient descent problem. The experimental results and their analysis show that the proposed model SNRNN is effective in earthquake detection tasks. The contribution of this study is as follows:  
* We propose a deep learning model: a stacked normalized recurrent neural network (SNRNN), which is an ensemble model of three SimpleRNN, GRU, and LSTM models with normalization layers;
* We evaluate the performance of three individual RNN models and stacked RNN models using layer normalization and batch normalization methods;
* We compare the proposed SNRNN model to classic recurrent models and find that it outperformed all other models, achieving the lowest RMSE values.