O. V. Mandrikova

Analysis of the Earth's magnetic field variations on the basis of a wavelet-based approach

In the present paper we will discuss a new wavelet-based approach aimed at processing and analyzing different features of complex geomagnetic signals. This approach makes it possible to automatically extract different kinds of disturbances in the Earth@?s magnetic field variations, which characterize solar activity and help to predict magnetic storms. In order to analyze geomagnetic signals wavelet packets are used in order to isolate local variations for quiet and perturbed periods and determine their intensity. Furthermore, a new automatic method of calculating the index of geomagnetic activity K is suggested on the basis of forming a quiet-day diurnal variation (Sq-curve). This method allows us to do calculations in the way that is closest to that developed by J. Bartels, who introduced the K-index in 1938. The results are compared with those obtained by INTERMAGNET and the original method of J. Bartels and the advantages of the suggested method are clearly demonstrated. For geomagnetic data collected in high-latitude regions of our planet it has become possible to reduce the error of estimating the K-index by 20% and unlike the technique used by INTERMAGNET here all the calculations can be done automatically. We will use geomagnetic signals that were kindly provided to us by the Institute of Cosmophysical Research and Radio Wave Propagation (Paratunka, Kamchatka region, Far East of Russia) for the period from January, 2002 till December, 2010.

Simulation and analysis of time variations in ionospheric parameters on the basis of wavelet transform and multicomponent models

This work is devoted to development of instruments for analysis of ionospheric parameters and detection of anomalies that occur during ionospheric disturbances. An algorithm is proposed to determine the parameters of a multicomponent model of ionospheric data. It is based on a combination of a wavelet transform and autoregressive-integrated moving average models. Methods for the model diagnosis are described. The multicomponent model allows description of quiet variations in ionospheric parameters, prediction of the variations, and detection of anomalies during disturbances. An algorithm based on wavelets and threshold functions is used for detection and detailed analysis of the anomalies. Data from the Institute of Cosmophysical Research and Radio Wave Propagation, Far East Branch, Russian Academy of Sciences, on the ionospheric foF2 critical frequency above Kamchatka were used during the experiments. Anomalies that occur in the ionosphere during increased solar and seismic activity above Kamchatka have been revealed on the basis of the simulation and data analysis.

New wavelet-based approach intended for the analysis of subtle features of complex natural signals

The present paper is devoted to the development of methods and algorithms intended for the analysis of complex natural signals (time series). Due to their variability, irregularity and complex structure the task of signal analysis and processing in the automatic mode is rather complicated. On the basis of contemporary methods of the analysis, processing, and recognition of complex data we have suggested a new approach, which allows us to automatically extract subtle features in complex natural signals of arbitrary structure. In addition, it becomes possible to identify components and characterize them in terms of a particular field. All the methods expounded in the following received approval from the Paratunka observatory (Paratunka, Kamchatka region, Far East Russia). The data were provided to the team of authors by the University of Cosmophysical Research and Radio Wave Propagation (Kamchatka region, Far East Russia).

Analyzing subtle features of natural time series by means of a wavelet-based approach

The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability, irregularity, and complex structure of the time series in question, the problem of automatic processing, i.e., in automatic mode, is rather complicated and merits further investigation in order to produce better solutions than those that presently exist. Relying on contemporary methods of signal processing, signal analysis, and recognition of complex data, we have suggested a new wavelet-based approach, which allows one to extract subtle structural features from a complex natural time series in an automatic mode. After that, it becomes possible to identify these features and analyze them in terms of a particular knowledge domain. Our methods and approaches have been successfully tested on the Earth’s magnetic field data obtained from the Paratunka observatory (Paratunka village, Kamchatka region, Far East of Russia).

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

The suggested method is aimed at studying the dynamics of the magnetospheric current systems during magnetic storms. The method is based on algorithmic solutions for processing of geomagnetic field variations, detection of local increases in geomagnetic disturbance intensity and estimation of their dynamic characteristics. Parameters of the algorithms allow us to evaluate the characteristics of small-scale local features emerging during geomagnetic activity slight increases and large-scale variations observed during magnetic storms. To evaluate the method, geomagnetic data from the stations located in the north-east of Russia and equatorial India were used. The method testing showed the possibility to apply it for the detection of pre-storm anomalous effects in geomagnetic data.

Methods of analysis of geophysical data during increased solar activity

This work is directed at creation of methods of study of the processes in the ionospheric–magnetospheric system during increased solar and geomagnetic activity. Method of modeling and analysis of the parameters of the ionosphere, which allows prediction of the data and identification of the anomalies during the ionospheric disturbances, are given. Computational solutions for determination and estimation of the geomagnetic disturbances are described. Method of determination of the anomalous changes in the time course of cosmic rays, which allows qualitative estimations of the moments of their origination, duration, and intensity, is suggested.On the basis of the methods elaborated, the data on the periods of strong and moderate magnetic storms are complexly analyzed. Sharp oscillations in the electron density of the ionosphere with positive and negative phases, which originate in the regions analyzed during an increase in geomagnetic activity, are distinguished. Positive phases of the ionospheric disturbances from several hours to one and a half days long were formed before the beginning of the magnetic storms. At the moments of the increase in the electron concentration, a local increase is observed in the level of cosmic rays (several hours before the magnetic storms) that supported the solar nature of these effects. During the strongest geomagnetic disturbances, the electron concentration in the ionosphere decreased significantly and led to prolonged negative phases of ionospheric storms, which coincided with the decrease in the level of cosmic rays (a Forbush decrease).

Estimation of geomagnetic field disturbance using the wavelet transform

This paper discusses the main aspects of geomagnetic data processing using the wavelet transform. The wavelet transform is shown to be efficient for automatic extraction of unperturbed level of the horizontal component of the Earth’s magnetic field. As a result, it becomes possible to significantly reduce the errors arising during automatic calculations of the local geomagnetic activity index (local K-index) in comparison with adaptive smoothing (KAsm is Adaptative Smoothing method) recommended by INTERMAGNET. It has been found that prior to magnetic storms, we can observe a weak rise of geomagnetic activity in different frequency bands connected with the development of an approaching storm.