One of the complicated and demanding tasks in seismology is the reliable detection of earthquakes. The key challenge is that the detection models must be applied to a specific region, and models trained on one region may not perform as well in others. The limitations of datasets for most regions of the world pose another task. Comprehensive, high-quality datasets are essential for developing robust earthquake detection algorithms. Despite these challenges, developing effective earthquake detection systems is critically important. This paper proposes a novel deep network, Earth–Transformer–LSTM (ETL), to estimate earthquake magnitude with high precision. The proposed method uses Transformer encoders as its first layer to extract profound features from the dataset. To obtain highly accurate results, the extracted data is used as the input to the Long Short-Term Memory (LSTM) neural network. Additionally, one-dimensional convolution is replaced by Multi-Layer Perceptron (MLP), which performs better in Transformer encoders’ feed-forward networks. The Turkey earthquake dataset 2000–2018 was used in this research because significant earthquakes have occurred in this region in recent years. According to the obtained results, the proposed method’s Root Mean Squared Error (RMSE) is 0.7, representing a noticeable improvement over advanced conventional models.
Ghavifekr et al. (Wed,) studied this question.