Methods of analysis of geomagnetic field variations and cosmic ray data

Abstract

In the present paper, we propose a wavelet-based method of describing variations in the Earth's magnetic field, such as the horizontal component of the geomagnetic field, in addition to methods for evaluating changes in the energy characteristics of the field and for isolating the periods of increased geomagnetic activity. Based on a combination of multiresolution wavelet decompositions with neural networks, we propose a method of approximation of the cosmic ray time course and the allocation of anomalous variations (Forbush effects) that occur during periods of high solar activity. During the realization of the method, an algorithm was created for selecting the level of the wavelet decomposition and adaptive construction of the neural network. By using the proposed methods, we performed a joint analysis of the geomagnetic field and cosmic rays during periods of strong magnetic storms. The strongest geomagnetic field perturbations were observed in periods of abnormal changes in cosmic ray level. Assessment of the intensity of geomagnetic disturbances on the eve of and during magnetic storm development allowed us to highlight local increases in intensity of the geomagnetic field occurring at different frequency ranges prior to the development of the storm's main phase. Implementation of the proposed method with theoretical tools in combination with other methods will improve the estimation accuracy of the geomagnetic field state during space weather forecasting.

Method for modeling of the components of ionospheric parameter time variations and detection of anomalies in the ionosphere

“…Figures 9 and 10 show the joint analysis of ionospheric and geomagnetic data during the magnetic storms from Mandrikova et al 2013bMandrikova et al , 2014b. A large-scale negative anomaly that lasted for about a day and reached its maximum mainly in daytime between 6:00 and 18:00 LT on March 18 was found at the same time in the ionosphere.…”

Method of analysis of geomagnetic data based on wavelet transform and threshold functions

“…In the papers [14,30] the authors propose geomagnetic field variation representation based on multiscale wavelet decompositions:…”

“…During magnetic storms they contain uneven local features occurring at random times and carrying important information on the processes in the magnetosphere [2][3][4][5][6][7][8][9][10][11][12][13][14]. Traditional methods for data analysis applying basic models of time series, different techniques of smoothing and Fourier analysis methods are not effective enough to investigate fast unsteady processes.…”

“…rd International conference "Information Technology and Nanotechnology 2017" 181 terms of wavelets, the authors of this paper suggested a new model of geomagnetic field variations [14,30] and developed automatic algorithms do detect calm diurnal variation and to estimate disturbance intensities [30]. This approach allowed us to automate the procedure for calculation of geomagnetic activity index K, close to J. Bartels method, and to decrease the calculation error in comparison to the current methods [30].…”

Analysis of magnetic data of ground station network during strong magnetic storms

“…The traditional approaches and methods of time series analysis are not sufficiently effective to solve this problem [8][9][10]. At the present time, modern mathematical methods and information technologies in this field are intensively developing [2][3][4][5][6][7][11][12][13][14][15][16][17]. Algorithms to isolate the interferences during the primary processing of magnetic data [2,13] and methods for the anomaly detection during magnetic storms [2,13] were developed on the basis of DataMining approaches.…”