Neural Network Recovery of Gaps in Geomagnetic Field Records
Barkhatov N.A.1, Revunov S.E.2, Smirnova Zh.V.3, Semahin E. A.4, Gruzdeva M. L.5
1Barkhatov N.A.*, Minin Nizhny Novgorod State Pedagogical University (Minin University), Nizhny Novgorod, Russian Federation.
2Revunov S.E. Minin Nizhny Novgorod State Pedagogical University (Minin University), Nizhny Novgorod, Russian Federation.
3Smirnova Zh.V. Minin Nizhny Novgorod State Pedagogical University, Nizhny Novgorod, Russian Federation.
4Semahin E. A. Minin Nizhny Novgorod State Pedagogical University (Minin University), Nizhny Novgorod, Russian Federation.
5Gruzdeva M. L.Minin Nizhny Novgorod State Pedagogical University (Minin University), Nizhny Novgorod, Russian Federation
Manuscript received on December 13, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1588-1591 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8632019320/2020©BEIESP | DOI: 10.35940/ijitee.C8632.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper demonstrates the capabilities of neural network recovery of ground-based geomagnetic field records at a selected magnetic station using similar magnetic field data at another station. By the example of the restoration of disturbance records made at the magnetic stations Kakioka, Kanoya, Alma-Ata, Hermanius, San Juan, Tucson, Honolulu with and without data from the OMNI satellite system on the parameters of the solar wind and interplanetary magnetic field, it is shown that the technique of artificial neural networks can to successfully fill in the gaps and failures in the records of individual observatories of the global network of magnetic observation stations. The created artificial neural network tool can be used for scientific and applied problems of geomagnetic information recovery.
Keywords: Magnetic Recording, Magnetic Observatory, Geomagnetic Storm, Magnetosphere, Artificial Neural Network, Solar Activity, Forecast
Scope of the Article: Artificial intelligent methods, models, techniques