The research is focused on the development of automatic detection method of abnormal features, that occur in recorded time series of ionosphere critical frequency fOF2 during periods of high solar or seismic activity. The method is based on joint application of wavelet-transformation and neural networks. On the basis of wavelet transformation algorithms for the detection of features and estimation of their parameters were developed. Detection and analysis of characteristic components of time series are performed on the basis of joint application of wavelet transformation and neural networks. Method's approbation is performed on fOF2 data obtained at the observatory “Paratunka” (Paratunka settlement, Kamchatskiy Kray)

We introduce a new method for estimating the geomagnetic field. The method is based on a combination of a wavelet transform with radial basis neural networks. In the method, the recorded geomagnetic field variations are decomposed into different-scale components and the degree of disturbance of each component is estimated, enabling the conclusion about the field state. For the verification of the method, we used geomagnetic data from the "Paratunka" station (Paratunka, Kamchatka region, data registration is carried out by IKIR FEB RAS). Analysis of the spectral-temporal characteristics of geomagnetic field variations during periods of moderate and strong magnetic storms was performed. Weak perturbations were detected in the geomagnetic field before the storms. The obtained results have confirmed the effectiveness of the proposed method.

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Abstract.The paper presents our software system for estimation the degree of disturbance of the geomagnetic field. The system automatically classifies registered geomagnetic data and determines the state of the geomagnetic field for the current day. The results of approbation of the system showed the prospect of its application in problems of estimation and prediction of space weather. The system allows us to allocate weak disturbances of the geomagnetic field, which may occur before strong magnetic storms.