VAK Russia 1.6
UDC 550.38
UDC 550.380
UDC 55
UDC 550.34
UDC 550.383
CSCSTI 37.15
CSCSTI 37.01
CSCSTI 37.25
CSCSTI 37.31
CSCSTI 38.01
CSCSTI 36.00
CSCSTI 37.00
CSCSTI 38.00
CSCSTI 39.00
CSCSTI 52.00
Russian Classification of Professions by Education 05.06.01
Russian Library and Bibliographic Classification 260
Russian Library and Bibliographic Classification 26
Russian Trade and Bibliographic Classification 6324
Russian Trade and Bibliographic Classification 63
BISAC SCI019000 Earth Sciences / General
BISAC SCI SCIENCE
Various aspects of the measurements and processing of raw magnetic data obtained at observatories are considered. It is noteworthy that the processing can be executed through simple mathematical methods and algorithms at almost all stages. Nevertheless, there are a number of tasks, for example, related to the mass recognition of noise in raw data and the need to fill in gaps, for the effective solution of which it is necessary to involve more powerful mathematical technologies.
magnetic observatory, raw data, noise, modern mathematical methods
1. Gonsette A., Rasson J., Humbled F. In situ vector calibration of magnetic observatories // Geoscientific Instrumentation, Methods and Data Systems. — 2017. — Vol. 6, no. 2. — P. 361–366. — DOI:https://doi.org/10.5194/gi-6-361-2017.
2. INTERMAGNET Technical Reference Manual, Version 5.1.0 / ed. by B. V. St-Louis. — INTERMAGNET Operations Committee, Executive Council, 2020. — 146 p.
3. Jankowski J., Sucksdorff C. Guide for magnetic measurements and observatory practice. — Warshaw : International Association of Geomagnetism, Aeronomy, 1996. — 236 p.
4. Khomutov S. Y., Mandrikova O. V., Budilova E. A., et al. Noise in raw data from magnetic observatories // Geoscientific Instrumentation, Methods and Data Systems. — 2017. — Vol. 6, no. 2. — P. 329–343. — DOI:https://doi.org/10.5194/gi-6-329-2017.
5. McCuen B. A., Moldwin M. B., Steinmetz E. S., et al. Automated high-frequency geomagnetic disturbance classifier: A machine learning approach to identifying noise while retaining high-frequency components of the geomagnetic field // Journal of Geophysical Research: Space Physics. — 2023. — Vol. 128, no. 2. — P. 1–22. — DOI:https://doi.org/10.1029/2022JA030842.
6. Papsheva S. Y., Mandrikova O. V., Khomutov S. Y. Method of noise detection in magnetic data // Vestnik KRAUNC. Fizikomatematicheskie nauki. — 2019. — Vol. 29, no. 4. — P. 87–97. — DOI:https://doi.org/10.26117/2079-6641-2019-29-4-87-97. — (In Russian).
7. Sidorov R. V., Soloviev A. A., Bogoutdinov S. R. Application of the SP algorithm to the INTERMAGNET magnetograms of the disturbed geomagnetic field // Izvestiya, Physics of the Solid Earth. — 2012. — Vol. 48. — P. 410–414. — DOI:https://doi.org/10.1134/S1069351312040088.
8. Soloviev A. A., Bogoutdinov S. R., Agayan S. M., et al. Detection of hardware failures at INTERMAGNET observatories: application of artificial intelligence techniques to geomagnetic records study // Russian Journal of Earth Sciences. — 2009. — Vol. 11, no. 2. — ES2006. — DOI:https://doi.org/10.2205/2009ES000387.
9. Soloviev A. A., Chulliat A., Bogoutdinov S. R., et al. Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory // Earth, Planets and Space. — 2012. — Vol. 64. — P. 743–752. — DOI:https://doi.org/10.5047/eps.2012.03.004.
10. Soloviev A. A., Lesur V., Kudin D. V. On the feasibility of routine baseline improvement in processing of geomagnetic observatory data // Earth, Planets and Space. — 2018. — Vol. 70. — P. 1–14. — DOI:https://doi.org/10.1186/s40623-018-0786-8.
