Development and Application of Problem-Oriented Digital Twins for Magnetic Observatories and Variation Stations

Introduction: Magnetic stations are one of the main tools for observing the geomagnetic field. However, gaps and anomalies in time series of geomagnetic data, which often exceed 30% of the number of recorded values, negatively affect the effectiveness of the implemented approach and complicate the application of mathematical tools which require that the information signal is continuous. Besides, the missing values ​​add extra uncertainty in computer simulation of dynamic spatial distribution of geomagnetic variations and related parameters. Purpose: To develop a methodology for improving the efficiency of technical means for observing the geomagnetic field. Method: Creation of problem-oriented digital twins of magnetic stations, and their integration into the collection and preprocessing of geomagnetic data, in order to simulate the functioning of their physical prototypes with a certain accuracy. Results: Using Kilpisjärvi magnetic station (Finland) as an example, it is shown that the use of digital twins, whose information environment is made up of geomagnetic data from adjacent stations, can provide the opportunity for reconstruction (retrospective forecast) of geomagnetic variation parameters with a mean square error in the auroral zone of up to 11.5 nT. The integration of problem-oriented digital twins of magnetic stations into the processes of collecting and registering geomagnetic data can provide automatic identification and replacement of missing and abnormal values, increasing, due to the redundancy effect, the fault tolerance of the magnetic station as a data source object. For example, the digital twin of Kilpisjärvi station recovers 99.55% of annual information, and 86.73% of it has an error not exceeding 12 nT. Discussion: Due to the spatial anisotropy of geomagnetic field parameters, the error at the digital twin output will be different in each specific case, depending on the geographic location of the magnetic station, as well as on the number of the surrounding magnetic stations and the distance to them. However, this problem can be minimized by integrating geomagnetic data from satellites into the information environment of the digital twin. Practical relevance: The proposed methodology provides the opportunity for automated diagnostics of time series of geomagnetic data for outliers and anomalies, as well as restoration of missing values and identification of small-scale disturbances.