###### Abstract
It is based on wavelet transform, which allows us to investigate complicated nonstationary changes in geophysical data and is widely used in physics, in particularly, in geophysics [4; 7; 9; 26; 34; 35; 36; 37; 38]. Based on the wavelet transform, we proposed an automated method to detect geomagnetic pulsations [34; 35], constructed an algorithm for automatic detection of magnetic storms with increased risk of occurrence of geomagnetically-induced currents [3], and developed a method to calculate the geomagnetic activity index WISA [36; 38]. Based on the discrete wavelet transform, the paper [4] proposed a method to determine the sudden beginning of a magnetic storm using the comparison of three methods for automatic detection of geomagnetic storms (first derivative analysis, Akaike information criterion, and discrete wavelet transform), the authors [3] show high efficiency of the discrete wavelet transform. The approach, suggested by the authors [3], using the algorithm of detecting coronal mass ejection shock fronts in ACE data on the solar wind before the trigger of storm arrival to the Earth, provides detection of a storm with high probability. The paper [7], applying statistical approaches and wavelet transforms, studies accelerometer measurements, solar index (F\({}_{10,7}\)), geomagnetic index data (Kp), and different types of atmospheric density time variations. In the paper [26], the authors investigate two algorithms for detecting micro pulsations in geomagnetic data. The authors [26] showed that a combination of wavelet transform with deep neural network allows one to improve the detection efficiency of micro pulsations compared to discrete wavelet transform and threshold functions. In this paper we show the possibility to apply wavelet transform to optimize the calculation of Dst-index [33], which is a measure of field change caused by ring current occurring in the magnetosphere during magnetic storms [39]. The authors also developed a new technique for data wavelet decomposition. It allows one to suppress noise and to detect sudden short-period variations of the geomagnetic field and estimate their parameters [31]. This paper continues the investigations [31; 32; 33]. Compared to the traditional approaches, the proposed wavelet model of geomagnetic field variations allows us to describe regular changes and anomalous short-period variations of different forms and duration. They occur during increased geomagnetic activity and determine the field disturbance degree. The paper describes the model identification technique and shows the results of its application for data recorded at the Observatory Paratunka of IKIR FEB RAS (IAGA code is PET, 52.97N, 158.24 E, [1]). The method results are compared with the traditional approach based on Sq-variation application [40]. Sq-curve is used to describe the characteristic time variations of the geomagnetic field during quiet periods [40] and is an average smoothed curve obtained from the <<quietest>> field variations over a certain period (following the paper [40], five <<quietest>> variations over a current month are usually considered). Special attention in the paper is given to the possibility of detecting sudden short-period anomalous changes occurring at the preparation stage of a magnetic storm and may be considered as predictors. To estimate the method efficiency and study its capabilities in an online mode, the processing was performed for raw 2 Hz and one-minute geomagnetic data and for 2 Hz and one-minute data obtained after preliminary processing by magnetologists.