CHAOTIC SEISMIC SIGNAL MODELING BASED ON NOISE AND EARTHQUAKE ANOMALY DETECTION

Since ancient times, people have tried to predict earthquakes using simple perceptions such as animal behavior. The prediction of the time and strength of an earthquake is of primary concern. In this study chaotic signal modeling is used based on noise and detecting anomalies before an earthquake using artificial neural networks (ANNs). Artificial neural networks are efficient tools for solving complex problems such as prediction and identification. In this study, the effective features of chaotic signal model is obtained considering noise and detection of anomalies five minutes before an earthquake occurrence. Neuro-fuzzy classifier and MLP neural network approaches showed acceptable accuracy of 84.6491% and 82.8947%, respectively. Results demonstrate that the proposed method is an effective seismic signal model based on noise and anomaly detection before an earthquake.