Application of BIG DATA methods for INTERMAGNET data preprocessing
https://doi.org/10.17586/0021-3454-2023-66-7-533-538
Abstract
When solving geophysical problems related to the Earth's magnetic field, quite often there is a need to process large volume data. The information obtained as a result of the analysis can serve as the basis for solving various fundamental problems, for example, studying the movement of the magnetic poles, or applied problems, such as navigating the Earth's magnetic field. In addition, in many cases it is of interest to analyze data over a long period of time, which significantly increases the amount of data that needs to be processed. The use of modern technologies for working with large amounts of data, such as, for example, BIG DATA, allows solving a fairly wide class of geophysical problems. The problem of preprocessing the data of measurements of the state of the Earth's magnetic field provided by geomagnetic observatory Lycksele (Sweden) included in the international network INTERMAGNET. Preprocessing was performed using the BIG DATA methods implemented in the MatLab system.
About the Author
A. G. Korobeynikov
Pushkov Institute of Terrestrial Magnetism, Ionosphere
and Radio Wave Propagation of the RAS, St. Petersburg Branch
Russian Federation
Russian Federation
Anatoly G. Korobeynikov - Dr. Sci., Professor; Deputy Director for Science
St. Petersburg
References
1. Korobeynikov A.G., Grishentsev A.Y., Velichko E.N., Aleksanin S.A., Fedosovskii M.E., Bondarenko I.B., Korikov C.C. Optical Memory & Neural Networks (Information Optics), 2016, no. 3(25), pp. 184–191.
2. Dyakonov V.P. MATLAB i SIMULINK dlya radioinzhenerov (MATLAB and SIMULINK for radio engineers), Moscow, 2016, 976 р. (in Russ.)
3. Novgorodtsev A.B. Raschet elektricheskikh tsepey v MATLAB (Calculation of Electrical Circuits in MATLAB), St. Petersburg, 2004, 250 р. (in Russ.)
4. Matyushkin I.V. Modelirovaniye i vizualizatsiya sredstvami MATLAB fiziki nanostruktur (Modeling and Visualization by Means of MATLAB of the Physics of Nanostructures), Moscow, 2011, 168 р. (in Russ.)
5. Korobeynikov A.G., Fedosovsky M.E., Zharinov I.O., Shukalov A.V., Gurjanov A.V. International Journal of Applied Engineering Research, 2017, no. 6(12), pp. 1114–1122.
6. Gaiduk A.R., Belyaev V.E., Pyavchenko T.A. Teoriya avtomaticheskogo upravleniya v primerakh i zadachakh s resheniyami v MATLAB (Theory of Automatic Control in Examples and Problems with Solutions in MATLAB), St. Petersburg, 2016, 464 р. (in Russ.)
7. Porshnev S.V. Komp'yuternoye modelirovaniye fizicheskikh protsessov v pakete MATLAB (Computer Simulation of Physical Processes in the MATLAB Package), St. Petersburg, 2011, 736 р. (in Russ.)
8. Frisk V.V., Ganin V.I., Stepanova A.G. Komp'yuternyy analiz i modelirovaniye elektricheskikh tsepey postoyannogo toka v srede MATLAB (Computer Analysis and Modeling of DC Electrical Circuits in the MATLAB Environment), Moscow, 2021, 32 р. (in Russ.)
9. Korobeynikov A.G. Software & Systems, 2022, no. 3(35), pp. 452–457, DOI: 10.15827/0236-235X.139.452-457. (in Russ.)
10. Nikolayeva S.G. Neyronnyye seti. Realizatsiya v Matlab (Neural networks. Implementation in Matlab), Kazan', 2015, 92 р. (in Russ.)
11. Makshanov A.V., Zhuravlev A.E., Tyndykar L.N. Bol'shiye dannyye. Big data (Big Data. Big Data), St. Petersburg, 2022, 188 р., ISBN 978-5-8114-9834-5. (in Russ.)
12. Korobeynikov A.G. International Journal of Humanities and Natural Sciences, 2018, no. 8, pp. 91–98. (in Russ.)
13. Smolin A.A., Zhdanov D.D., Potemin I.S., Mezhenin A.V., Bogatyrev V.A. Sistemy virtual'noy, dopolnennoy i smeshannoy real'nosti (Virtual, Augmented and Mixed Reality Systems), St. Petersburg, 2018, 59 р., https://www.elibrary.ru/item.asp?id=46453270. (in Russ.)
Review
For citations:
Korobeynikov A.G. Application of BIG DATA methods for INTERMAGNET data preprocessing. Journal of Instrument Engineering. 2023;66(7):533-538. (In Russ.) https://doi.org/10.17586/0021-3454-2023-66-7-533-538
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