Experiment on long-term forecasting of geomagnetic activity based on nonlocal correlations

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

The experiment was performed on the use of advanced macroscopic nonlocal correlations to forecast slow random fluctuations of the Dst index of geomagnetic activity. The global maximum correlation of Dst with the signal of the electrode detector reaches 0.97, which is sufficient for the forecast, and its time shift corresponds to the advance of the detector signal relative to Dst by 329 days. The large magnitude of the time shift is due to the slow diffusion mechanism of entanglement swapping between the detector and the source. At the same time, the position of the global maximum of the correlation function coincides with the position of the global minimum of the entropy independence function, which confirms its undistorted by possible nonlinearity of the relationship and determines the optimal lead time of the forecast. Long series of test forecasts Dst have been calculated using data from a nonlocal correlation detector with a fixed lead time using three methods: current regression, current impulse transient response and current neural network. The accuracy of the forecasts is sufficient for all practical purposes.

Texto integral

Acesso é fechado

Sobre autores

S. Korotaev

Schmidt Institute of Physics of the Earth, RAS (IPE RAS)

Autor responsável pela correspondência
Email: korotaev@gemrc.ru

Geoelectromagnetic Research Centre (GEMRC)

Rússia, Moscow, Troitsk

V. Serdyuk

Schmidt Institute of Physics of the Earth, RAS (IPE RAS)

Email: korotaev@gemrc.ru

Geoelectromagnetic Research Centre (GEMRC)

Rússia, Moscow, Troitsk

I. Popova

Schmidt Institute of Physics of the Earth, RAS (IPE RAS)

Email: korotaev@gemrc.ru

Geoelectromagnetic Research Centre (GEMRC)

Rússia, Moscow, Troitsk

J. Gorohov

Pushkov Institute of Terrestrial Magnetism, Ionosphere and Radio Wave Propagation, RAS IZMIRAN)

Email: korotaev@gemrc.ru
Rússia, Moscow, Troitsk

E. Kiktenko

Schmidt Institute of Physics of the Earth, RAS (IPE RAS)

Email: korotaev@gemrc.ru

Geoelectromagnetic Research Centre (GEMRC)

Rússia, Moscow, Troitsk

D. Orekhova

Schmidt Institute of Physics of the Earth, RAS (IPE RAS)

Email: korotaev@gemrc.ru

Geoelectromagnetic Research Centre (GEMRC)

Rússia, Moscow, Troitsk

Bibliografia

  1. Коротаев С.М., Буднев Н.М., Сердюк В.О., Зурбанов В.Л., Миргазов Р.Р., Мачинин В.А., Киктенко Е.О., Бузин В.Б., Панфилов А.И. Новые результаты мониторинга вертикальной компоненты электрического поля в озере Байкал на базе поверхность — дно // Геомагнетизм и аэрономия. Т. 55. № 3. С. 406—418. 2015.
  2. Коротаев С.М., Буднев Н.М., Сердюк В.О., Киктенко Е.О., Орехова Д.А. Новые результаты Байкальского эксперимента по прогностическому эффекту макроскопических нелокальных корреляций // Вестник МГТУ им. Н.Э. Баумана. Сер. Естественные науки. № 4. С. 56—72. 2019.
  3. Коротаев С.М., Буднев Н.М., Сердюк В.О., Киктенко Е.О., Орехова Д.А., Горохов Ю.В. Макроскопические нелокальные корреляции по данным новых глубоководных измерений // Вестник МГТУ им. Н.Э. Баумана. Сер.: Естественные науки. 2021. № 2. С. 52—70. 2021.
  4. Коротаев С.М., Морозов А.Н. Нелокальность диссипативных процессов —причинность и время. М.: Физматлит, 216 с. 2018.
  5. Коротаев С.М., Сердюк В.О., Горохов Ю.В. Прогноз геомагнитной и солнечной активности на основе нелокальных корреляций // Доклады Академии наук. Т. 415. № 6. С. 814—817. 2007.
  6. Коротаев С.М., Сердюк В.О., Сорокин М.О. Проявление макроскопической нелокальности в геомагнитных и солнечно-ионосферных процессах // Геомагнетизм и аэрономия. Т. 40. № 3. С. 56—64. 2000.
  7. Попова И., Рожной А., Соловьева М., Левин Б., Чебров В. Нейросетевая методика выделения прогностических аномалий по низкочастотным электромагнитным сигналам в Курило-Камчатском регионе // Физика Земли. № 2. С. 1—13. 2016.
  8. Amico L., Fazio R., Osterloch A., Vedral V. Entanglement in many-body systems // Rev. Mod. Phys. V. 80. P. 517. 2008.
  9. Calsamiglia J., Hartmann L., Dür W., Briegel H.-J. Spin gases: quantum entanglement driven by classical kinematics // Phys. Rev. Lett. V. 95. P. 180502. 2005.
  10. Cramer J.G. The transactional interpretation of quantum mechanics // Rev. Mod. Phys. V. 58. P. 647—688. 1986.
  11. Dutta A.K. Earthquake prediction using Artificial Neural Networks // International Journal of Research and Reviews in Computer Science. № 2. P. 1279—1281. 2011.
  12. Hoyle F., Narlikar J.V. Cosmology and action-at-a-distance electrodynamics // Rev. Mod. Phys. V. 67. № 1. P. 113—156. 1995.
  13. Korotaev S.M. Causality and Reversibility in Irreversible Time. Irvine, CA: Scientific Research Publishing, 130 p. 2011.
  14. Korotaev S., Budnev N., Serdyuk V. Kiktenko E., Gorohov J., Zurbanov V. Macroscopic entanglement and time reversal causality by data of the Baikal experiment // Journal of Physics: Conf. Ser. V. 1051. P. 012019. 2018a.
  15. Korotaev S., Budnev N., Serdyuk V., Kiktenko E., Orekhova D., Gorohov J. Macroscopic nonlocal correlations in reverse time by data of the Baikal Experiment // Journal of Physics: Conf. Ser. V. 1557. P. 012026. 2020.
  16. Korotaev S., Budnev N., Serdyuk V., Kiktenko E., Orekhova D., Gorohov J. Macroscopic nonlocal correlations by new data of the Baikal Experiment // Journal of Physics Conf. Ser. V. 2197. P. 012019. 2022.
  17. Korotaev S.M., Gorohov J.V., Serdyuk V.O., Novysh A.V. Response of macroscopic nonlocal correlation detector to a phase transition // Journal of Physics: Conference Series. V. 1348. P. 012041. 2019.
  18. Korotaev S.M., Morozov A.N., Serdyuk V.O., Nalivayko V.I., Novysh A.V., Gaidash S.P., Gorohov J.V., Pulinets S.A., Kanonidi Kh. D. Manifestation of macroscopic nonlocality in the processes of solar and geomagnetic activity // Vestnik of BMSTU. Special Issue. P. 173—185. 2005.
  19. Korotaev S.M., Serdyuk V.O., Budnev N.M. Advanced response of the Baikal macroscopic nonlocal correlation detector to the heliogeophysical processes / Unified Field Mechanics II. London: World Scientific. P. 375—380. 2018b.
  20. Lee S.-S. B., Park J, Sim H.-S. Macroscopic quantum entanglement of a Kondo Cloud at finite temperature // Phys. Rev. Lett. V. 114. P. 057203. 2015.
  21. Reid M.D., He Q.Y., Drummond P.D. Entanglement and nonlocality in multi-particle systems // Frontiers of Physics. V. 7. № 1. P. 72—85. 2012.
  22. Popova I., Rozhnoi A., Solovieva M., Chebrov D., Hayakawa M. The behavior of the VLF/LF variations associated with the geomagnetic activity, earthquakes and quiet condition using neural network approach // Entropy. V 20. P. 691—702. 2018.
  23. Suratgar A.A., Setoudeh F., Salemi A.H., Negarestani A. Magnitude of Earthquake Prediction Using Neural Network / Natural Computation. Fourth International Conference on Natural Computation. Jinan, China: IEEE Publisher. P. 448—452. 2008.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar
2. Fig. 1. Diagram of the detector device [Korotaev et al., 2019]: E - electrodes (the complex internal structure is not shown), the potential difference of which (U) is the detector signal; NaCl—electrolyte (3% aqueous solution); V—detector body (hard rubber); t1,t2.te - temperature sensors. The distance between the electrodes is 30 cm.

Baixar (118KB)
Metadados
3. Fig. 2. Correlation function r U and Dst and independence function τ - time shift Dst relative to U, days.

Baixar (76KB)
Metadados
4. Fig. 3. Demonstration of the possibility of forecasting by U by shifting the filtered series by the amount of advance of the global maximum correlation (329 days). The upper time axis corresponds to U, the lower one to Dst.

Baixar (99KB)
Metadados
5. Fig. 4. Forecast of Dst by the current regression method with a fixed lead time of 329 days (thin line) compared with the actual curve (thick line). The root mean square error of the forecast is 0.99 nT.

Baixar (68KB)
Metadados
6. Fig. 5. Forecast of Dst by the current impulse transient response method with a fixed lead time of 329 days (thin line) in comparison with the actual curve (thick line). The root mean square error of the forecast is 0.40 nT.

Baixar (61KB)
Metadados
7. Fig. 6. Forecast of Dst by the current neural network method with a fixed lead time of 329 days (thin line) in comparison with the actual curve (thick line). The root mean square error of the forecast is 0.29 nT.

Baixar (51KB)
Metadados

Declaração de direitos autorais © Russian Academy of Sciences, 2024