# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
###### Abstract  
Earthquakes threaten people, homes, and infrastructure. Earthquake detection is a complex task because it does not show any specific pattern, unlike object detection from images. Convolutional neural networks have been widely used for earthquake detection but have problems like vanishing gradients, exploding, and parameter optimization. The ensemble learning approach combines multiple models, each of which attempts to compensate for the shortcomings of the others to enhance performance. This article proposes an ensemble learning model based on a stacked normalized recurrent neural network (SNRNN) for earthquake detection. The proposed model uses three recurrent neural network models (RNN, GRU, and LSTM) with batch normalization and layer normalization. After preprocessing the waveform data, the RNN, GRU, and LSTM extract the feature map sequentially. Batch normalization and layer normalization methods take place in mini-batches and input layers for stable and faster training of the model and improving its performance. We trained and tested the proposed model on 6574 events from 2000 to 2018 (18 years) in Turkey, a highly targeted region. The SNRNN achieves RMSE values of 3.16 and 3.24 for magnitude and depth detection. The SNRNN model outperforms the three baseline models, as seen by their low RMSE values. earthquake detection; ensemble learning; batch normalization; RNN; GRU 2023138121 10 July 2023138121 10 May 2023138121 10 July 2023138121 10 May 202313812  
waves without the need for human intervention [2]. The number of seismic networks and monitoring sensors has steadily increased in recent years, and the continuous growth of seismic records calls for new processing algorithms that assist in solving problems in seismology. Several seismic networks with multiple stations, like the Southern California Seismic Network and the Turkey Network, have provided catalogs of earthquake events in the last decade. Seismology benefits significantly from a practical and comprehensive analysis of these catalogs. Using computational approaches like machine learning and deep learning is more promising for automatically predicting earthquakes [3]. Many different machine learning models, including decision trees (DTs), support vector machines (SVMs), and k-nearest neighbors (k-NNs), have been applied to the problem of earthquake detection. Ref. [4] used the SVM model for an on-site early warning system. Ref. [5] used k-means clustering techniques for earthquake magnitude detection from global earthquake catalogs. Ref. [6] used k-NN, SVM, DT, and random forest (RF) for earthquake detection and found that RF outperformed the other models. Several factors like feature selection method, dataset size, and class imbalance distribution affect the performance of machine learning models. 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 large because they employ several layers to deal with such data [6]. A convolutional neural network (CNN) was used to classify earthquake events into macro, micro, and artificial earthquakes. Ref. [1] presented a CNN and a graph convolutional network (GCN) based on deep learning models to detect earthquakes 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. Recurrent neural networks (RNNs) have a different architecture than CNNs. RNNs use various gates for different operations instead of convolutional and pooling operations. Ref. [8] used an RNN-based DeepShake model, and Ref. [9] used a CNN model to predict earthquake shaking intensity from ground motion observation. Ref. [10] used LSTM and BLSTM for magnitude detection. Ref. [2] proposed a transformer-based TEAM method that issues accurate and timely warnings.