###### Abstract  
Geomagnetic data analysis is an important basis for the investigation of the processes in the near-Earth space, Earth magnetosphere, and ionosphere. The negative impact of geomagnetic anomalies on modern technical objects and human health determine the applied significance of the investigation and requires the creation of effective methods for timely detection of the anomalies. Priory complicated structure of geomagnetic data makes their formalization and analysis difficult. This paper proposes a wavelet model for geomagnetic field variations. It describes characteristic changes and anomalies of different amplitude and duration. Numerical realization of the model provides the possibility to apply it in online analysis. We describe the process of model identification and show its efficiency in the detection of sudden, short-period geomagnetic anomalies occurring before and during magnetic storms. Raw second data of the Paratunka and Magadan observatories and post-processed minute data were used in the paper. The question of noise effect on the proposed model results was under consideration. geomagnetic field variations; geomagnetic anomalies; complicated structure data analysis; wavelet transform +
Footnote †: journal: Applied sciences  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0  
such events is quite topical and is has been solved for several decades by different scientists and research groups [2; 3; 4; 5; 6; 12; 20; 21; 22; 23; 24]. The geomagnetic data modeling and analysis problems are associated with their complicated irregular structure and the presence of noises of different origin (natural and anthropogenic) [22; 24; 25; 26]. The geomagnetic data structure and the anomalous feature occurrence times contain essential information on nonstationary processes in NES and characterize magnetic disturbance intensity at the place of data recording. Traditional methods for time series modeling (smoothing, spectral methods etc.) allow us to investigate long-term variations and regular diurnal changes of the magnetic field [2]. However, they are not effective enough to analyze sudden short-period features occurring during increased solar activity and magnetic storms [5; 22; 24; 25; 26]. At the present time, new methods and approaches are being developed to analyze complex geophysical data. For example, the authors of the paper [24] suggest using chirplet and warblet transforms to analyze fine structures of geomagnetic field variations. The authors [24] showed the possibility of estimating the instantaneous signal frequency and obtaining geomagnetic pulse characteristics based on a generalized wavelet transform. In the papers [5; 6; 25], it was proposed to apply a fuzzy logic technology to investigate geomagnetic data complicated structures. Application of fuzzy logic elements was further developed in the paper [22], where the authors consider a multi-scale approach and study morphological features of several intensive geomagnetic storms based on it. At the present time, to improve the efficiency of complicated raw data analysis, hybrid approaches, including machine learning methods, are frequently used [8; 26; 27; 28]. For example, the paper [8] suggests a model of fuzzy wavelet neural network (FWNN), applying fuzzy logic elements and wavelet transform [29; 30] to forecast a signal code transmitted from a well to the surface with wastewater. In the paper [28], the authors propose using deep learning structures to forecast the geoefficiency of coronal mass ejections (CME) and the times of geoeffective CME arrivals. They use a time series of satellite optical observations from 1996 to 2018 as the data for network training. However, the neural network apparatus validity and accuracy is determined by sampling representativity, and the required adaptation, taking into account geophysical data nonstationarity, decreases the neural network efficiency. In this paper we propose to use the approach developed by the authors [31; 32; 33].