Next Article in Journal
The Potential of Visible and Far-Red to Near-Infrared Light in Glaucoma Neuroprotection
Next Article in Special Issue
Opportunities for Machine Learning in District Heating
Previous Article in Journal
Nanosecond-Fiber Laser Cutting and Finishing Process for Manufacturing Polycrystalline Diamond-Cutting Tool Blanks
Previous Article in Special Issue
NPU RGBD Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players+
Article

Seismic Reflection Analysis of AETA Electromagnetic Signals

1
The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, China
2
Engineering Department, Shenzhen MSU-BIT University, Shenzhen 518055, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Sławomir Nowaczyk, Mohamed-Rafik Bouguelia and Hadi Fanaee
Appl. Sci. 2021, 11(13), 5869; https://doi.org/10.3390/app11135869
Received: 2 June 2021 / Revised: 22 June 2021 / Accepted: 22 June 2021 / Published: 24 June 2021
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. View Full-Text
Keywords: AETA; anomaly detection; LSTM autoencoder; earthquake precursor; unsupervised learning AETA; anomaly detection; LSTM autoencoder; earthquake precursor; unsupervised learning
Show Figures

Figure 1

MDPI and ACS Style

Bao, Z.; Yong, S.; Wang, X.; Yang, C.; Xie, J.; He, C. Seismic Reflection Analysis of AETA Electromagnetic Signals. Appl. Sci. 2021, 11, 5869. https://doi.org/10.3390/app11135869

AMA Style

Bao Z, Yong S, Wang X, Yang C, Xie J, He C. Seismic Reflection Analysis of AETA Electromagnetic Signals. Applied Sciences. 2021; 11(13):5869. https://doi.org/10.3390/app11135869

Chicago/Turabian Style

Bao, Zhenyu, Shanshan Yong, Xin’an Wang, Chao Yang, Jinhan Xie, and Chunjiu He. 2021. "Seismic Reflection Analysis of AETA Electromagnetic Signals" Applied Sciences 11, no. 13: 5869. https://doi.org/10.3390/app11135869

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop