Long term air ion monitoring in search of pre-earthquake signals

Warden, Sheldon; Bleier, Thomas; Kappler, K. N.

doi:10.1016/j.jastp.2019.01.009

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…However, when these anomalies manifest in the records of individual stations, they are prone to being attributed to environmental noise or instrument-related faults. Deploying multiple sensors or instrument arrays for AEF monitoring within the same area can help mitigate such uncertainties [43]. In light of the extensive dataset available, this study utilizes time series analysis and wavelet analysis methods to validate the occurrence of AEF anomalies during the pre-seismic phase.…”

Section: Discussionmentioning

confidence: 99%

Identification and Analysis of Multi-Station Atmospheric Electric Field Anomalies before the Yangbi Ms 6.4 Earthquake on 21 May 2021

Nie,

Zhang

2023

Atmosphere

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This study reports the atmospheric electric field (AEF) anomalies associated with seismic-geological activity recorded by the monitoring network in the Sichuan–Yunnan region of China during the 15–30 days prior to the Yangbi earthquake in Yunnan Province, China, on 21 May 2021. Based on the real-time AEF data from continuous observation, this study summarized the characteristics of the anomalous interference of different meteorological factors on the AEF, compared the simultaneous meteorological data of the AEF anomalies, and ruled out the influence of precipitation, wind, fog, and other weather factors on the AEF anomalies in Yangbi County prior to the Yangbi Ms 6.4 earthquake. The AEF anomalies were identified and extracted from the two-month data from 1 April to 1 June, which were from multiple days, stations, and rupture zones near the 100 km radius from the epicenter of the Yangbi Ms 6.4 main earthquake. Using time series and wavelet transform analysis methods, the obvious common features of the anomalies were summarized, and the homology of the anomalies was verified. The main outcome of the investigation in this study will be used to distinguish and characterize the AEF anomalies associated with pre-seismic geologic activity of non-meteorological elements in the near future.

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Section: Discussionmentioning

confidence: 99%

Identification and Analysis of Multi-Station Atmospheric Electric Field Anomalies before the Yangbi Ms 6.4 Earthquake on 21 May 2021

Nie,

Zhang

2023

Atmosphere

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“…Generally, SFSs encompass two main research areas [5,6]: (1) long-term seismic forecasting and (2) short-term seismic forecasting. Long-term seismic forecasting involves predicting regions where destructive earthquakes may occur in the coming years or decades, and there have been numerous successful cases [7][8][9][10]. However, achieving reliable and stable short-term seismic forecasting remains a challenge [11].…”

Section: Introductionmentioning

confidence: 99%

A pre-seismic anomaly detection approach based on graph attention isomorphism network

Huang,

Shi,

Zhu

et al. 2023

Meas. Sci. Technol.

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Pre-seismic anomaly detection plays a crucial role in reducing economic losses and casualties caused by earthquakes. This paper proposes a novel four-step pre-seismic anomaly detection approach. In the first step, a series of pre-seismic features are extracted by analyzing the earthquake catalog and geomagnetic signals. In the second step, the multi-view learning (MVL) strategy is employed to obtain the fusion features. In the third step, multiple seismic stations in one seismic zone are treated as a seismic station network, and a pre-seismic anomaly detection model is constructed based on the station network. In the final step, 4 evaluation indicators (EIs) are introduced to evaluate the detection results comprehensively. Verification results show the proposed method is effective and achieves a better performance than other existing methods.

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“…The evolution of the array was described in Warden et al. (2019), and the growth and magnitude of the data set in Kappler et al. (2019).…”

Section: Introductionmentioning

confidence: 99%

Case‐Control Study on a Decade of Ground‐Based Magnetometers in California Reveals Modest Signal 24–72 hr Prior to Earthquakes

et al. 2022

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Magnetic field changes as earthquake precursors have been the subject of numerous studies and some controversy. Infrequent large earthquakes and sparse magnetometer coverage along fault zones complicate statistical analysis. We present an analysis of ground‐based magnetic time‐series measurements before 19 earthquakes ≥M4.5 in California drawing from over 330,000 site‐days of measurement spanning a decade. To perform a fair existential test for electromagnetic antecedents we applied a pre‐specified statistical analysis with two key ideas. First, we combine signals from nearby (≤40 km) sites via spectral cross‐power, and then look for large spikes in frequency domain (0.016–25 Hz). The former is only possible with a dense set of sites running over a long period of time. In this statistical case‐control study we used the machine learning concept of rigorously separated train and test sets of earthquakes which were generated via a rule‐based query of the USGS earthquake catalog. Before each declustered earthquake, we constructed one period 24–72 hr before (the “precursor” or “p‐period”) and a series of seven equally‐sized preceding periods (“quiescent” or “q‐periods”). We distilled the data in each period to a frequency‐dependent feature—the 98th percentile of spectral cross power. We trained a model based on Linear Discriminant Analysis and applied the discriminator to the test set revealing a modest effect in the days leading up to an earthquake. While the observed effect size is not directly useful for earthquake prediction (long a scientific goal), it suggests a relationship which should be further investigated for a physical link.

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Long term air ion monitoring in search of pre-earthquake signals

Cited by 15 publications

References 28 publications

Identification and Analysis of Multi-Station Atmospheric Electric Field Anomalies before the Yangbi Ms 6.4 Earthquake on 21 May 2021

Identification and Analysis of Multi-Station Atmospheric Electric Field Anomalies before the Yangbi Ms 6.4 Earthquake on 21 May 2021

A pre-seismic anomaly detection approach based on graph attention isomorphism network

Case‐Control Study on a Decade of Ground‐Based Magnetometers in California Reveals Modest Signal 24–72 hr Prior to Earthquakes

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