# Seismic Reflection Analysis of AETA Electromagnetic Signals
## 1 Introduction  
Before a large earthquake (M. \(>\) 6.0) occurs, there are many abnormal phenomena related to the earthquake which are called earthquake precursors. The International Association of Seismology and Physics of the Earth's Interior (IASPEI) has a subcommittee that developed a list of such precursors, namely seismic quiescence, a decrease in radon concentration, other geochemical phenomenon, and changes in ground water levels [1]. Precursor phenomena not included in the IASPEI list have also 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. However, it is necessary to establish that the signal anomaly is related to seismic activities before using algorithms to analyze the signal. 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. Some animals have evolved to develop a vibration-triggered early warning response, as outlined by [PERSON], which acts in the short time interval between the arrival of P and S waves [2]. [PERSON] utilized time series analysis to determine that stress accumulation preceding the earthquake could cause the radon anomaly [3]. [PERSON], [PERSON] et al., who used a method named superposed epoch analysis (SEA), observed that the ULF geomagnetic anomalies are more likely seen 6-15 days beforesizeable earthquake events (Es \(\geq\) 108) [4]. The moving median method was used in total electron content (TEC), land surface temperature (LST), and aerosol optical depth (AOD) analysis, and the result in [PERSON]'s study confirmed that those data are important potential precursors for earthquake prediction [5]. [PERSON], [PERSON] executed a correlation analysis between significant sequences of thermal anomalies (SSTAs) and M. \(\geq\) 6 Japanese earthquakes by using robust satellite techniques (RSTs), which address the constraints concerning space, time, and magnitude [6]. Many such methods have been used in the correlation analysis of seismic precursors. The LSTM autoencoder (LAE) is an unsupervised neural network that analyzes the anomaly information of the original data by reconstructing it, and this approach is often used for anomaly detection of temporal series. [PERSON] et al. used the autoencoder method to explore the latent space distribution without anomaly analysis from past seismic events, which was used as input for a subsequent earthquake prediction model [7]. [PERSON], [PERSON] et al. developed an unsupervised model using the LAE algorithm to mimic the daily activities of employees to detect anomalous employee behavior of a community [8]. The algorithm was also used to evaluate 12 representative types of anomalies from 1555 robot-assisted feeding executions by [PERSON]'s team, and, compared with five baseline detectors, the LAE achieved the best performance [9]. [PERSON], [PERSON] et al. utilized the LAE method to prevent industrial accidents by taking the sequential skeleton map as the input [10]. LSTM autoencoder and one-class support vector machines were combined by researchers in synthetic data and real data of fashion retail, which comprised a multivariate time series, to detect anomalies; the obtained results led to better performance compared to other machine learning methods [11]. [PERSON] et al. proposed a hybrid intelligent intrusion detection system, which was developed using the Spark MLlib and an LSTM autoencoder deep learning approach; the simulation results significantly outperformed existing intrusion detection approaches for the ISCX-UNB dataset [12].