On magnetic precursors of earthquakes

Зотов, О. Д.; Guglielmi, A. V.; Собисевич, А. Л.

doi:10.1134/s1069351313050145

Cited by 19 publications

(9 citation statements)

References 23 publications

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“…We are looking for hydraulic (Wang and Manga, 2010) and geomagnetic anomalies (Zotov et al, 2013;Buchachenko, 2021) in the "quasiepicentral" or a precursor interaction area R. We choose the interaction length R = 200 km for hydraulic precursors of EQs of M ≥ 3.5 for a given well. Note that there are different assessments of the EQ precursors' area.…”

Section: Network Devices and Datamentioning

confidence: 99%

Earthquake Forecast as a Machine Learning Problem for Imbalanced Datasets: Example of Georgia, Caucasus

et al. 2022

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In this article, we considered the problem of M≥3 earthquake (EQ) forecasting (hindcasting) using a machine learning (ML) approach, using experimental (training) time series on monitoring water-level variations in deep wells as well as geomagnetic and tidal time series in Georgia (Caucasus). For such magnitudes’, the number of “seismic” to “aseismic” days in Georgia is approximately 1:5 and the dataset is close to the balanced one. However, the problem of forecast is practically important for stronger events—say, events of M≥3.5—which means that the learning dataset of Georgia became more imbalanced: the ratio of seismic to aseismic days for in Georgia reaches the values of the order of 1:20 and more. In this case, some accepted ML classification measures, such as accuracy leads to wrong predictions due to a large number of true negative cases. As a result, the minority class, here—seismically active periods—is ignored at all. We applied specific measures to avoid the imbalance effect and exclude the overfitting possibility. After regularization (balancing) of the training data, we build the confusion matrix and performed receiver operating classification in order to forecast the next day probability of M≥3.5 earthquake occurrence. We found that the Matthews’ correlation coefficient (MCC) is the measure, which gives good results even if the negative and positive classes are of very different sizes. Application of MCC to observed geophysical data gives a good forecast of the next day M≥3.5 seismic event probability of the order of 0.8. After randomization of EQ dates in the training dataset, the Matthews’ coefficient efficiency decreases to 0.17.

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Section: Network Devices and Datamentioning

confidence: 99%

Earthquake Forecast as a Machine Learning Problem for Imbalanced Datasets: Example of Georgia, Caucasus

et al. 2022

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“…Recall that the foreshocks as the mechanical precursors of strong earthquakes occur in the final stage of seismic cycle [Sobolev et al, 2009]. At the same stage there are a variety of the electromagnetic precursors of coming earthquake (e.g., see [Fraser-Smith et al, 1990;Hattori, 2004;Pulinets, 2004;Hayakawa, 2012;Zotov et al, 2013]). In the context of this work, the observations indicating a sharp activization of ULF magnetic pulsations that occurs in the time interval 2 -4 h before the strong earthquakes ] deserve a special attention.…”

Section: Activization Of Foreshocks Before Earthquakementioning

confidence: 99%

Foreshocks and aftershocks of strong earthquakes in the light of catastrophe theory

Guglielmi¹

2015

Успехи физических наук

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It is useful to consider the earthquakes in terms of catastrophe theory. In the paper, we illustrate this statement by analysis foreshocks preceding the strong earthquakes. We focused on the so-called catastrophe flags, and on the triggers that cause the critical transitions. The analysis shows a sharp increase of foreshock activity 3 h before the mainshock. This result is reminiscent of the well-known activation of the ULF magnetic precursors of earthquakes. Furthermore, we found that the characteristic frequency of foreshock sequence decreases a few hours before the mainshock, which is consistent with a prediction of the catastrophe theory. Finally, the analysis testifies that the strong foreshocks seems may be triggers of mainshocks. The idea is that the surface waves propagating outwards from the foreshock return back to the vicinity of the epicenter after having made a complete revolution around the Earth and induce there the mainshock.

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“…Геодинаміка 1(16)/2014 Мета Нерідко перед землетрусами і після них спостерігається вихід в атмосферу радону. Збільшення його концентрації в приземній атмосфері внаслідок розкритя тріщин і пор викликає локальне і варіабельне зростання електропровідності з відповідною зміною геоелектричного і геомагнітного поля [Zotov O. et al, 2013]. Проявляються і інші чутливі електромагнітні ефекти від землетрусів, пов'язані з електрокінетичними, тектономагнітними [Сурков В.В., 1997, 2000Fujinawa Y. et al, 2011;Guglielmi A. and Zotov O., 2010], магнітогідродинамічними, генерації УНЧ електромагнітних полів тощо [Adushkin V. et al, 2012;Arora B., 2012;Pisa D., et al, 2010;Rikitake T., 1987;Sobisevich L. et al, 2010;Stacey F.D., 1964;Yamada I., Masuda K., Mizutani H., 1989]; і навіть для пошуків пасток вуглеводнів [Revil A. and Jardani A., 2010].…”

Section: вступunclassified

Geodynamics

Tolstoy¹,

Shabatura²

2014

JGD

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On magnetic precursors of earthquakes

Cited by 19 publications

References 23 publications

Earthquake Forecast as a Machine Learning Problem for Imbalanced Datasets: Example of Georgia, Caucasus

Earthquake Forecast as a Machine Learning Problem for Imbalanced Datasets: Example of Georgia, Caucasus

Foreshocks and aftershocks of strong earthquakes in the light of catastrophe theory

Geodynamics

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