Geophysical Center RAS
Ufa, Russian Federation
Schmidt Institute of Physics of the Earth, RAS
Geophysical Center RAS
Moscow, Russian Federation
There is no ground-based magnetic station or observatory that guarantees the quality of information received and transmitted to it. Data gaps, outliers, and anomalies are a common problem affecting virtually any ground-based magnetometer network, creating additional obstacles to efficient processing and analysis of experimental data. It is possible to monitor the reliability and improve the quality of the hardware and soft- ware modules included in magnetic stations by develop- ing their virtual models or so-called digital twins. In this paper, using a network of high-latitude IMAGE magnetometers as an example, we consider one of the possible approaches to creating such models. It has been substantiated that the use of digital twins of magnetic stations can minimize a number of problems and limitations associated with the presence of emissions and missing values in time series of geomagnetic data, and also provides the possibility of retrospective forecasting of geomagnetic field parameters with a mean square error (MSE) in the auroral zone up to 11.5 nT. Integration of digital twins into the processes of collecting and registering geomagnetic data makes the automatic identification and replacement of missing and abnormal values possible, thus increasing, due to the redundancy effect, the fault tolerance of the magnetic station as a data source object. By the example of the digital twin of the station “Kilpisjärvi” (Finland), it is shown that the proposed approach implements recovery of 99.55 % of annual information, while 86.73 % with M not exceeding 12 nT.
digital twins, time series reconstruction, statistical analysis, geomagnetic data, magnetic stations
1. Datcu M., Le Moigne J., Loekken S., Soille P., Xia G.-S. Special Issue on Big Data From Space. IEEE Transactions on Big Data, 2020, vol. 6, no. 3, pp. 427-429. DOI:https://doi.org/10.1109/TBDATA.2020.3015536.
2. Demyanov V.V., Savelyeva E.A. Geostatistics: theory and practice. Moscow, Nauka Pabl., 2010, 327 p. (In Russian).
3. Engebretson M.J., Steinmetz E.S., Posch J.L., Pilipenko V.A., Moldwin M.B., Connors M.G. Nighttime magnetic perturbation events observed in Arctic Canada: 2. Multiple-instrument observations. J. Geophys. Res.: Space Phys. 2019, no. 124, pp. 7459-7476. DOI:https://doi.org/10.1029/2019JA026797.
4. GOST 27.0022015. Reliability in technology. Terms and Definitions. Moscow.: Standartinform, 2016.23 p.
5. Grieves M.W. Digital Twin: Manufacturing Excellence through Virtual Factory Replication, Florida Institute of Technology Publ., 2014, 7 p.
6. Gvishiani A.D., Agayan S.M., Bogoutdinov Sh.R., Kagan A.I. Gravitational smoothing of time series. Trudy Instituta matematiki i mekhaniki UrO RAN [Proceedings of the Institute of Mathematics and Mechanics of the Ural Branch of the Russian Academy of Sciences]. 2011, vol. 17, no. 2, pp. 62-70. (In Russian).
7. Gvishiani A.D., Lukyanova R.Yu. Study of the geomagnetic field and the problem of the accuracy of drilling directional wells in the Arctic region. Gorny Zhurnal [Mining Journal]. 2015, no. 10, pp. 94-99. DOI:https://doi.org/10.17580/gzh.2015.10.17. (In Russian).
8. Gvishiani A.D., Lukyanova R.Yu. Assessment of the impact of geomagnetic disturbances on the trajectory of directional drilling of deep wells in the Arctic region. Fizika Zemli [Physics of the Earth]. 2018, no. 4, pp. 19-30. DOI:https://doi.org/10.1134/S0002333718040051. (In Russian).
9. Gvishiani A.D., Lukyanova R.Yu., Soloviev A.A. Geomagnetism: from the Core of the Earth to the Sun. Moscow, RAS Pabl., 2019. 186 p. (In Russian).
10. Hoerl R.W. Ridge Regression: A Historical Context. Technometrics. 2020, vol. 62, iss. 4, pp. 420-425. DOI:https://doi.org/10.1080/00401706.2020.1742207.
11. Isaaks E.H., Mohan R. An Introduction to applied geostatistics. Oxford: Oxford University Press, 1989, 592 p.
12. Khomutov S.Yu. International project INTERMAGNET and magnetic observatories of Russia: cooperation and progress. E3S Web of Conferences. 2018, vol. 62, p. 02008. DOI:https://doi.org/10.1051/e3sconf/20186202008.
13. Kondrashov D., Shprits Y., Ghil M. Gap filling of solar wind data by singular spectrum analysis, Geophys. Res. Lett. 2010, vol. 37, iss. 15. L15101. DOI:https://doi.org/10.1029/2010GL044138.
14. Love J. An International Network of Magnetic Observatories. EOS, transactions, American geophysical union. 2013, vol. 94, no 42, pp. 373-384.
15. Mandrikova O. V., Soloviev I. S. Wavelet technology for processing and analyzing geomagnetic data. Tsifrovaya obrabotka signalov [Digital Signal Processing]. 2012, no. 2, pp. 24-29. (In Russian).
16. Mandrikova O.V., Solovyev I.S., Khomutov S.Y., Geppener V.V., Klionskiy D.M., Bogachev M.I. Multiscale variation model and activity level estimation algorithm of the Earth’s magnetic field based on wavelet packets. Ann. Geophys. 2018, vol. 36, iss. 5. pp. 1207-1225. DOI:https://doi.org/10.5194/angeo-36-1207-2018.
17. Parmar R., Leiponen A., Llewellyn D.W.T. Building an organizational digital twin, Business Horizons. 2020, vol. 63, no. 6, pp. 725-736. DOI:https://doi.org/10.1016/j.bushor.2020.08.001.
18. Reich K., Roussanova E. Visualising geomagnetic data by means of corresponding observations. International Journal on Geomathematics. 2013, vol. 4, pp. 1-25. DOI:https://doi.org/10.1007/s13137-012-0043-4.
19. She Y. Sparse regression with exact clustering. Electron. J. Statist. 2010, vol. 4, pp. 1055-1096. DOI:https://doi.org/10.1214/10-EJS578.
20. Tanskanen E.I. A comprehensive high-throughput analysis of substorms observed by IMAGE magnetometer network: Years 1993-2003 examined. J. Geophys. Res. 2009, vol. 114, iss. A5, p. A05204. DOI:https://doi.org/10.1029/2008JA013682.
21. Tokmakova A.A., Strizhov V.V. Estimation of hyperparameters of linear regression models in the selection of noise and correlated features. Informatika i yeye primeneniye [Informatics and its application]. 2012, vol. 6, no. 4, pp. 66-75. (In Russian).
22. Vorobev A.V., Vorobeva G.R. Approach to Assessment of the Relative Informational Efficiency of Intermagnet Magnetic Observatories. Geomagnetism and Aeronomy. 2018a, vol. 58, no. 5, pp. 625-628. DOI:https://doi.org/10.1134/S0016793218050158.
23. Vorobev A.V., Vorobeva G.R. Inductive method for reconstructing time series of geomagnetic data. Proc. SPIIRAS [Trudy SPIIRAN]. 2018b, no. 2, pp. 104-133. DOI:https://doi.org/10.15622/sp.57.5. (In Russian).
24. Vorobev A.V., Vorobeva G.R. Correlation analysis of geomagnetic data synchronously recorded by INTERMAGNET magnetic laboratories. Geomagnetism and Aeronomy. 2018c, vol. 58, no. 2, pp. 178-184. DOI:https://doi.org/10.1134/S001679321 8020196.
25. Vorobev A., Vorobeva G. Properties and type of latitudinal dependence of statistical distribution of geomagnetic field variations, 2019, In: Kocharyan G., Lyakhov A. (eds) Trigger Effects in Geosystems. Springer Proceedings in Earth and Environmental Sciences. Springer Cham. 1919. P. 197-206. DOI:https://doi.org/10.1007/978-3-030-31970-0_22.
26. Vorobev A.V., Pilipenko V.A., Enikeev T.A., Vorobeva G.R. Geographic information system for analyzing the dynamics of extreme geomagnetic disturbances based on observations of ground stations. Komp’yuternaya optika [Computer Optics]. 2020, vol. 44, no. 5, pp. 782-790. DOI:https://doi.org/10.18287/2412-6179-CO-707. (In Russian).
27. Zongyan W. Digital Twin Technology. Industry 4.0 - Impact on Intelligent Logistics and Manufacturing. IntechOpen. 2020. DOI:https://doi.org/10.5772/intechopen.80974.
28. Zou H., Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2005, vol. 67, iss. 2. pp. 301-320. DOI:https://doi.org/10.1111/j.1467-9868.2005.00503.x.
29. URL: https://space.fmi.fi/image (accessed 1 March 2021).
30. URL: https://space.fmi.fi/image/www/index.php?page= user_defined (accessed 1 March 2021).
