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Today intensive development of systems and technologies for registration of the Earth’s magnetic field parameters causes an exponential increase of geomagnetic data quantity, mainly collected by the ground magnetic stations. Imperfection of applied equipment and enabled channels of information transfer leads to the presence of omissions in the registered data time series. Along with spatial anisotropy it creates a serious obstacle to the processing of geomagnetic data. Russian and foreign scientific organizations are used to recover missing geomagnetic data by the linear interpolation. The approach provides admissible results in conditions of a quiet magnetosphere, but significantly distorts time series when changing the surrounding magnetic environment. This fact causes a scientific and technical problem, which is concerned with the development of new approach to recovering geomagnetic data registered in unquiet magnetosphere with acceptable time series imputation quality metrics. The authors suggest the approach for time series recovering based on inductive method of machine learning. According to the approach each magnetic station operates its own knowledge base, which is formed during the registration of geomagnetic field and its variations parameters. The combination of the values of the series preceding and following the gap is supposed to be a characteristic description, which is used for searching the precedent in the magnetic station knowledge base. The result contains the required fragment of the time series, which replaces the missing values. The complexity of the information signal, caused by an unquiet magnetic environment, increases the accuracy of search by precedents. The greater the knowledge base of the magnetic station, the higher the effectiveness of the search. Analysis of the results obtained during gap recovering in geomagnetic data time series (registered in conditions of unquiet magnetosphere) demonstrated that the suggested inductive method of imputation allows increasing the accuracy of the missing values recovery by an average of 79.54% compared with the methods currently used. The approach will enhance the efficiency of geomagnetic data processing for solving applied problems.

Inductive Method of Geomagnetic Data Time Series Recovering