Abstract
Acoustic and electromagnetics to artificial intelligence (AETA) is a system used to predict seismic events through monitoring of electromagnetic and geoacoustic signals. It is widely deployed in the Sichuan–Yunnan region (22° N–34° N, 98° E–107° E) of China. Generally, the electromagnetic signals of AETA stations near the epicenter have abnormal disturbances before an earthquake. When a significant decrease or increase in the signal is observed, it is difficult to quantify this change using only visual observation and confirm that it is related to an upcoming large earthquake. Considering that the AETA data comprise a typical time series, current work has analyzed the anomalism of AETA electromagnetic signals using the long short-term memory (LSTM) autoencoder method to prove that the electromagnetic anomaly of the AETA station can be regarded as an earthquake precursor. The results show that there are 2–4% anomalous points and some outliers exceeding 0.7 (after normalization) in the AETA stations within 200 km of the epicenter of the Jiuzaigou earthquake (M. 7.0) and the Yibin earthquake (M. 6.0) half a month before the earthquakes. Therefore, the AETA electromagnetic disturbance signal can be used as an earthquake precursor and for further earthquake prediction.
Highlights
Before a large earthquake (M. > 6.0) occurs, there are many abnormal phenomena related to the earthquake which are called earthquake precursors
The moving median method was used in total electron content (TEC), land surface temperature (LST), and aerosol optical depth (AOD)
Due to the timing of AETA electromagnetic signals, the aim of the present study was to confirm, using the LSTM autoencoder (LAE) algorithm, that the AETA station has an abnormal electromagnetic disturbance before the earthquake, which can be considered as an earthquake precursor
Summary
Before a large earthquake (M. > 6.0) occurs, there are many abnormal phenomena related to the earthquake which are called earthquake precursors. > 6.0) occurs, there are many abnormal phenomena related to the earthquake which are called earthquake precursors. Precursor phenomena not included in the IASPEI list have been confirmed as potential earthquake prediction tools, such as anomalous electromagnetic field changes, abnormal animal behavior, and fault creep or continuous strain. With the improvement of computer computing performance, many data analysts have attempted to utilize earthquake-related signals with machine learning or deep learning models for earthquake prediction or warning. The correlation analysis between seismic precursors and earthquake activities has always attracted the attention of geophysicists, geologists, and data analysts. Precursor signals with great correlation have stronger interpretability and even better performance in downstream tasks such as earthquake early warning. Kulahci utilized time series analysis to determine that stress accumulation preceding the earthquake could cause the radon anomaly [3]. The moving median method was used in total electron content (TEC), land surface temperature (LST), and aerosol optical depth (AOD)
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