Mechanism of unipolar electromagnetic pulses emitted from the hypocenters of impending earthquakes

Freund, Friedemann; Heraud, J. A.; Centa, V. A.; Scoville, J. T.

doi:10.1140/epjst/e2020-000244-4

Cited by 23 publications

(9 citation statements)

References 56 publications

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“…Several studies have investigated the physical mechanisms of ionospheric pre-earthquake perturbations [47][48][49][50][51]. Perhaps the most widely accepted hypotheses regarding these mechanisms were presented by Pulinets, et al [52] and Kuo, Lee, and Huba [27], who proposed complex lithosphere-atmosphere-ionosphere coupling as the physical basis for the generation of short-term earthquake precursors.…”

Section: Discussionmentioning

confidence: 99%

SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks

et al. 2021

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Low Earth orbit satellites collect and study information on changes in the ionosphere, which contributes to the identification of earthquake precursors. Swarm, the European Space Agency three-satellite mission, has been launched to monitor the Earth geomagnetic field, and has successfully shown that in some cases it is able to observe many several ionospheric perturbations that occurred as a result of large earthquake activity. This paper proposes the SafeNet deep learning framework for detecting pre-earthquake ionospheric perturbations. We trained the proposed model using 9017 recent (2014–2020) independent earthquakes of magnitude 4.8 or greater, as well as the corresponding 7-year plasma and magnetic field data from the Swarm A satellite, and excellent performance has been achieved. In addition, the influence of different model inputs and spatial window sizes, earthquake magnitudes, and daytime or nighttime was explored. The results showed that for electromagnetic pre-earthquake data collected within a circular region of the epicenter and with a Dobrovolsky-defined radius and input window size of 70 consecutive data points, nighttime data provided the highest performance in discriminating pre-earthquake perturbations, yielding an F1 score of 0.846 and a Matthews correlation coefficient of 0.717. Moreover, SafeNet performed well in identifying pre-seismic ionospheric anomalies with increasing earthquake magnitude and unbalanced datasets. Hypotheses on the physical causes of earthquake-induced ionospheric perturbations are also provided. Our results suggest that the performance of pre-earthquake ionospheric perturbation identification can be significantly improved by utilizing SafeNet, which is capable of detecting precursor effects within electromagnetic satellite data.

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Section: Discussionmentioning

confidence: 99%

SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks

et al. 2021

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“…The current required to induce detected pulses is greater than i 40 kA for the strongest pulses. Unipolar magnetic pulses can be, for example, generated deep in the rock column by peroxy defects when rocks are subjected to increasing deviatoric stresses (Freund et al, 2021). The proposed strip of diffuse current constitutes the electromagnetic source of ULF waves, which is able to produce low intensities of magnetic inductions on the Earth's surface, even if it is measurable both near and far from the EQ epicenter.…”

Section: Different Precursors Combinedmentioning

confidence: 99%

West Pacific Earthquake Forecasting Using NOAA Electron Bursts With Independent L-Shells and Ground-Based Magnetic Correlations

Fidani¹

2021

Front. Earth Sci.

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Recent advances in statistical correlations between strong earthquakes and several non-seismic phenomena have opened the possibility of formulating warnings within days or even hours. The retrieved correlations have been discovered for those ionospheric physical observations which lasted a long time and realized using the same instruments, including multi-satellite recordings. One of those regarded the electron burst phenomena detected by NOAA, for which the conditional probability of a seismic event was calculated. Then an earthquake probability greater than its frequency was assigned when a satellite realized such a phenomenological observation. This approach refers to the correlations obtained between high-energy electrons detected using the NOAA POES and strong Indonesian and Philippine earthquakes. It is reformulated here to realize a test of earthquake forecasting. The fundamental step is obtained by using a unique electron L-shell interval of 1.21 ≤ L ≤ 1.31, which decouples the electron parameters from the earthquake parameters. Then, the optimized correlation was recalculated to be 1.5–3.5 h early, between electron bursts and an increased number of seismic events with M ≥ 6, therein improving the significance too. Moreover, this methodology is reconnected to the frequency theory, and to Molchan’s error diagram, by the probability gain, where a comparison among the significances of various methods is given. The previously proposed physical link between the crust and the ionosphere through magnetic interaction, presumably operating 4–6 h before strong earthquakes, is examined quantitatively on the basis of recent magnetic pulse measurements. Consequently, the probability gain of earthquake forecasting is hypothetically calculated for both the dependent measurements of electron bursts using NOAA satellites and possible ground-based magnetic pulse detection. This method of combining probability gains for earthquake forecasting is general enough that it can be applied to any pair of observables from space and the ground.

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“…(2) Freund et al [55] offer insight from a physics perspective into some fundamental solid-state processes, by which stresses acting on rocks activate electronic charge carriers (specifically defect electrons, also known as holes or "positive holes") that can produce potentially powerful electric currents in the Earth's crust, where none existed without stress. These currents are thought to be responsible for bursts of unipolar electromagnetic pulses generated near hypocenters of impending seismic events.…”

Section: Review and Critique Of Each Contributionmentioning

confidence: 99%

“…Importantly, the GEFS is initiating a new and much needed phase of cross-disciplinary collaboration [8,50]. The signals addressed in the GEFS issue include: ground-based geoelectric and magnetic anomalies [51][52][53][54][55], radon activity [56], thermal infrared anomalies [57][58][59][60] and longwave radiation [57], combined thermal and gas anomalies [61], (sub)ionospheric signals [53,62,63], total electron content [57], groundwater changes [56,64], precipitation [65], low-frequency "stress waves" [66], solar activity coupling [67], as well as combinations of various signals due to lithosphere-atmosphere-ionosphere coupling [56,57] and comparisons of non-seismic signals to seismic precursors [54,68]. The introductory article of this volume [69] provides a review of the solid-state processes that are proposed to form the basis of the non-seismic precursors and others (in the same volume [70]).…”

Section: Introductionmentioning

confidence: 99%

Global Earthquake Forecasting System (GEFS): The challenges ahead

Mignan

Ouillon

Sornette

et al. 2021

Eur. Phys. J. Spec. Top.

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We conclude this special issue on the Global Earthquake Forecasting System (GEFS) by briefly reviewing and analyzing the claims of non-seismic precursors made in the present volume, and by reflecting on the current limitations and future directions to take. We find that most studies presented in this special volume, taken individually, do not provide strong enough evidence of non-seismic precursors to large earthquakes. The majority of the presented results are hampered by the fact that the task at hand is susceptible to potential biases in data selection and possible overfitting. The most encouraging results are obtained for ground-based geoelectric signals, although the probability gain is likely small compared to an earthquake clustering baseline. The only systematic search on satellite data available so far, those of the DEMETER mission, did not find a robust precursory pattern. The conclusion that we can draw is that the overall absence of convincing evidence is likely due to a deficit in systematically applying robust statistical methods and in integrating scientific knowledge of different fields. Most authors are specialists of their field while the study of earthquake precursors requires a system approach combined with the knowledge of many specific characteristics of seismicity. Relating non-seismic precursors to earthquakes remains a challenging multidisciplinary field of investigation. The plausibility of these precursors predicted by models of lithosphere-atmosphere-ionosphere coupling, together with the suggestive evidence collected here, call for further investigations. The primary goal of the GEFS is thus to build a global database of candidate signals, which could potentially improve earthquake predictability (if the weak signals observed are real and false positives sufficiently uncorrelated between different data sources). Such a stacking of disparate and voluminous data will require big data storage and machine learning pipelines, which has become feasible only recently. This special issue compiled an eclectic list of non-seismic precursor candidates, which is in itself a valuable source of information for seismologists, geophysicists and other scientists who may not be familiar with such types of investigations. It also forms the foundation for a coherent, multi-disciplinary collaboration on earthquake prediction.

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Mechanism of unipolar electromagnetic pulses emitted from the hypocenters of impending earthquakes

Cited by 23 publications

References 56 publications

SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks

SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks

West Pacific Earthquake Forecasting Using NOAA Electron Bursts With Independent L-Shells and Ground-Based Magnetic Correlations

Global Earthquake Forecasting System (GEFS): The challenges ahead

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