Seismic Reflection Analysis of AETA Electromagnetic Signals

Bao, Zhenyu; Yong, Shanshan; Wang, Xinan; Yang, Chao; Xie, Jin-Han; He, Canfei

doi:10.3390/app11135869

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…The difference between the original input vector x and the reconstructed vector z is called the reconstruction error x − z (Hinton and Salakhutdinov, 2006). The autoencoder updates the weight of the network by minimizing the reconstruction error L (Bao et al, 2021):…”

Section: Autoencoder (Ae)mentioning

confidence: 99%

Coarse-graining research of the thermal infrared anomalies before earthquakes in the Sichuan area on Google Earth engine

Meng

Zhang

et al. 2023

Front. Earth Sci.

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Seismo-induced Thermal infrared (TIR) anomalies has been proposed as a significant precursor of earthquakes. Several methods have been proposed to detect Thermal infrared anomalies that may be associated with earthquakes. However, there is no comparison of the influence for Thermal infrared extraction methods with a long time statistical analysis. To quantify the effects of various techniques used in Thermal infrared anomaly extraction, in this paper, we offer a complete workflow of their comparative impacts. This study was divided into three parts: anomaly detection, statistical analysis, and tectonic factor research. For anomaly detection, daily continuous nighttime surface temperature (ConLST) data was obtained from the Google Earth Engine (GEE) platform, and each different anomaly detection method was used to detect Thermal infrared outliers in the Sichuan region (27°-37°N, 97°-107°E). During statistical analysis, The heated core model was applied to explore Thermal infrared anomalies which is to filter anomalies unrelated to earthquakes by setting time-space-intensity conditions. The 3D error diagram offers scores to assume the best parameter set using training-test-validation steps. In the final part, we considered information on stresses, active faults, and seismic zones to determine the optimal parameters for extracting the Thermal infrared anomalies. The Kalman filter method detected the highest seismic anomaly frequency without considerating the heating core condition. The Autoencoder and Isolation Forest methods obtain the optimal alert type and parameter set to determine if the anomaly is likely earthquake-related. The RST method performs optimally in the final part of the workflow when it considers physical factors such as active faults, seismic zones, and stresses. However, The six methods we have chosen are not sufficient to contain the entire Thermal infrared anomaly extraction. The consideration of tectonic factors in the research remains poorly developed, as statistical methods were not employed to explore the role of constructive factors. Nevertheless, it is a significant factor in comparing anomaly extraction methods and precursor studies.

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Section: Autoencoder (Ae)mentioning

confidence: 99%

Coarse-graining research of the thermal infrared anomalies before earthquakes in the Sichuan area on Google Earth engine

Meng

Zhang

et al. 2023

Front. Earth Sci.

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“…AETA is a multi-component earthquake-monitoring system, which has been installed in over 200 sites nationwide in China [30]. To date, there have been many advances in the study of earthquake precursors based on the AETA system [31,32]. This work is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

Yu,

Jing,

Wang

et al. 2024

Remote Sensing

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On 5 September 2022, the Ms 6.8 Luding earthquake occurred at 29.59°N and 102.08°E in China. To investigate the variations in geomagnetic signals before the earthquake, this study analyzes the geomagnetic data from nine stations around the epicenter. First, we apply the Multi-channel Singular Spectrum Analysis to reconstruct the periodic components of the geomagnetic data from multiple stations. Second, we employ K-means clustering to rule out the possibility of occasional anomalies caused by a single station. Subsequently, we construct a geomagnetic topology network considering the remaining stations. Network centrality is defined as a measure of overall network connectivity, where the higher the correlation between multiple stations, the greater the network centrality. Finally, we examine the network centrality 45 days before and 15 days after the Luding earthquake. The results show that several anomalies in network centrality are extracted about one week before the earthquake. We further validate the significance of the anomalies in terms of time as well as space and verify the utility of the centrality anomalies through the SEA technique. The anomalies are found to have a statistical correlation with the earthquake event. We consider that this study provides a new way and a novel observational perspective for earthquake precursor analysis of ground-based magnetic data.

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Multi-Station Collaborative Analysis of Impending Seismic Precursor Based on Graph Neural Networks

Chen

Huang

et al. 2022

2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)

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Seismic Reflection Analysis of AETA Electromagnetic Signals

Cited by 5 publications

References 20 publications

Coarse-graining research of the thermal infrared anomalies before earthquakes in the Sichuan area on Google Earth engine

Coarse-graining research of the thermal infrared anomalies before earthquakes in the Sichuan area on Google Earth engine

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

Multi-Station Collaborative Analysis of Impending Seismic Precursor Based on Graph Neural Networks

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