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 13 publications

(3 citation statements)

References 28 publications

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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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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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“…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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“…As such, Rn has received attention from researchers since several decades, and statistical and modeling studies are published worldwide. Rn exhalation can vary depending on the geology, geochemistry, as well as soil properties of a given region (Külahcı and Şen, 2014;Warden et al, 2019). This exhalation is often associated with gasses like methane, carbon dioxide, hydrogen, and helium.…”

Section: Introductionmentioning

confidence: 99%

Radon transport from soil to air and Monte-Carlo simulation

Muhammad

Külahçı

2022

Journal of Atmospheric and Solar-Terrestrial Physics

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The exhalation of geochemical entities from soil to air is significant to understand Lithosphere-Atmospheric relationships. Some of these geochemical entities are capable of modifying the lower atmosphere, and they are employed in various studies. Radon is one of the geochemical gasses widely recognized as a dominant ionization source in near ground regions of the troposphere. The steady state Rn transport equation is considered in many cases for estimating Rn migration from soil to air on the condition that the time evolution is ignored. A method is proposed for estimating radon space-time transport from soil to air. This is achieved by solving the radon transport equation in soil with special boundary conditions. Similar results are obtained with some experimented models, as well as reported radon values in literature for some set of parameter combinations.Strengths and limitations of the method are discussed. The model is useable to study Lithosphere-Atmosphere relationships. It can also be significant in other studies like the Global Electric Circuit or Seismo-Ionospheric studies.

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

Cited by 13 publications

References 28 publications

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

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

Radon transport from soil to air and Monte-Carlo simulation

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