Geomagnetic data recovery approach based on the concept of digital twins

There is no ground-based magnetic station or observatory that guarantees the quality of information received and transmitted to it. Data gaps, outliers, and anomalies are a common problem affecting virtually any ground-based magnetometer network, creating additional obstacles to efficient processing and analysis of experimental data. It is possible to monitor the reliability and improve the quality of the hardware and soft- ware modules included in magnetic stations by develop- ing their virtual models or so-called digital twins. In this paper, using a network of high-latitude IMAGE magnetometers as an example, we consider one of the possible approaches to creating such models. It has been substantiated that the use of digital twins of magnetic stations can minimize a number of problems and limitations associated with the presence of emissions and missing values in time series of geomagnetic data, and also provides the possibility of retrospective forecasting of geomagnetic field parameters with a mean square error (MSE) in the auroral zone up to 11.5 nT. Integration of digital twins into the processes of collecting and registering geomagnetic data makes the automatic identification and replacement of missing and abnormal values possible, thus increasing, due to the redundancy effect, the fault tolerance of the magnetic station as a data source object. By the example of the digital twin of the station “Kilpisjärvi” (Finland), it is shown that the proposed approach implements recovery of 99.55 % of annual information, while 86.73 % with M not exceeding 12 nT.

Geomagnetic data recovery approach based on the concept of digital twins

There is no ground-based magnetic station or observatory that guarantees the quality of information received and transmitted to it. Data gaps, outliers, and anomalies are a common problem affecting virtually any ground-based magnetometer network, creating additional obstacles to efficient processing and analysis of experimental data. It is possible to monitor the reliability and improve the quality of the hardware and soft- ware modules included in magnetic stations by develop- ing their virtual models or so-called digital twins. In this paper, using a network of high-latitude IMAGE magnetometers as an example, we consider one of the possible approaches to creating such models. It has been substantiated that the use of digital twins of magnetic stations can minimize a number of problems and limitations associated with the presence of emissions and missing values in time series of geomagnetic data, and also provides the possibility of retrospective forecasting of geomagnetic field parameters with a mean square error (MSE) in the auroral zone up to 11.5 nT. Integration of digital twins into the processes of collecting and registering geomagnetic data makes the automatic identification and replacement of missing and abnormal values possible, thus increasing, due to the redundancy effect, the fault tolerance of the magnetic station as a data source object. By the example of the digital twin of the station “Kilpisjärvi” (Finland), it is shown that the proposed approach implements recovery of 99.55 % of annual information, while 86.73 % with M not exceeding 12 nT.

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.

DELAY TOLERANT NETWORKING SUPPORT FOR CREATION HIGH-ACCURACY MAGNETIC FIELD MAPS

Context. The main purpose of Correlation Extremal Navigation system is finding coordinates in case of absence of Global Positioning System signal and as a result high-accuracy maps as the main source of information for finding coordinates are very important. Magnetic field map as the main source of information can include errors values, as an example: not good enough equipment or human factor can cause error value of measurements. Objective. In order to create high-accuracy maps given work proposes to improve the process of creating magnetic field maps. The given work represents delay tolerant networking as an additional approach for data transmission between magnetic observatory and magnetic station and its improvement. Method. Improved Dijkstra’s algorithm together with Ford-Fulkerson’s algorithm for finding path with minimum capacity losses, earliest delivery time and maximum bit rate in case of overlapping contacts should be represented in the given work because nowadays, delay tolerant networking routing protocols do not take into account the overlap factor and resulting capacity losses and it leads to big problems Results. For the first time will be presented algorithm that chooses the route that guarantees the minimum of capacity losses, earliest delivery time and maximum bit rate in the delay tolerant networking with overlapping contacts and increases the probability of successful data transmission between magnetic stations and magnetic observatories. Conclusions. In order to perform high-accuracy measurement of magnetic field group of people allocate their equipment for magnetic field measurement in remote areas in order to avoid the influence of environment on measurements of magnetometer. Since magnitude of magnetic field can vary dependent on temperature, proximity to the ocean, latitude (diurnal variation of magnetic field) and magnetic storms magnetic station from time to time adjusts its measurements with a help of reference values of magnetic field (magnetic station sends request for reference values to magnetic observatory). The problem of the given approach is that remote areas usually are not covered by network (no Internet) and as a result the adjustment of measurements is impossible. In order to make adjustment of measurements possible and as a result improve accuracy of magnetic maps given work proposed the usage of Delay Tolerant Networking that delivers internet access to different areas around the world and represented its improvement to make its approach even better.The results are published for the first time.

Assessing the Potential Earthquake Precursory Information in ULF Magnetic Data Recorded in Kanto, Japan during 2000–2010: Distance and Magnitude Dependences

In order to clarify ultra-low-frequency (ULF) seismomagnetic phenomena, a sensitive geomagnetic network was installed in Kanto, Japan since 2000. In previous studies, we have verified the correlation between ULF magnetic anomalies and local sizeable earthquakes. In this study, we use Molchan’s error diagram to evaluate the potential earthquake precursory information in the magnetic data recorded in Kanto, Japan during 2000–2010. We introduce the probability gain (PG′) and the probability difference (D′) to quantify the forecasting performance and to explore the optimal prediction parameters for a given ULF magnetic station. The results show that the earthquake predictions based on magnetic anomalies are significantly better than random guesses, indicating the magnetic data contain potential useful precursory information. Further investigations suggest that the prediction performance depends on the choices of the distance (R) and size of the target earthquake events (Es). Optimal R and Es are about (100 km, 108.75) and (180 km, 108.75) for Seikoshi (SKS) station in Izu and Kiyosumi (KYS) station in Boso, respectively.

Neural Network Recovery of Gaps in Geomagnetic Field Records

The paper demonstrates the capabilities of neural network recovery of ground-based geomagnetic field records at a selected magnetic station using similar magnetic field data at another station. By the example of the restoration of disturbance records made at the magnetic stations Kakioka, Kanoya, Alma-Ata, Hermanius, San Juan, Tucson, Honolulu with and without data from the OMNI satellite system on the parameters of the solar wind and interplanetary magnetic field, it is shown that the technique of artificial neural networks can to successfully fill in the gaps and failures in the records of individual observatories of the global network of magnetic observation stations. The created artificial neural network tool can be used for scientific and applied problems of geomagnetic information recovery.

Estimation of geomagnetic activity using measure of anomalousness

Herein, we present a newly developed indicator for estimating geomagnetic activity. It is based on the magnitude of measure of anomalousness (MA) of magnetometer recordings at a given time or interval. It is intended for automated estimation of geomagnetic activity level in the area of a specific magnetic station or in a given region using data of a set of stations. It reflects geomagnetic activity level at different observatories in a single scale [-1, 1], regardless of their latitudinal location and consequently typical disturbance amplitudes. To a certain extent MA indicator is an analog of traditional K index. However, a well-known shortcoming of the latter is its long, 3-hour update rate. Moreover, K index calculation requires subtraction of Sq variation that also causes delays. At the same time there is a demand for operational geomagnetic indices that have maximal time resolution and are available in near real-time. The proposed MA indicator aims to address the shortcomings of the traditional K index. The MA calculation may be implemented automatically with the same time resolution as the initial data are recorded.

Multipoint observations of Ionospheric Alfvén Resonance

Among the processes that form properties of the geospace in the circumterrestrial plasma the electromagnetic resonances of the Earth, such as Schummann Resonance (SR) and Ionospheric Alfvén Resonance (IAR) are of great importance. IAR is more localized in space than SR and its properties largely depend on the characteristics of the propagation medium. In contrast to the SR, which has global nature and which is continuously observable at any time of the day, IAR signals are registered mostly during the nighttime and demonstrate more variability of the parameters than SR signals. At the Earth surface IAR is registered as Spectral Resonance Structure of the natural electromagnetic noise at frequency range 0.1-40 Hz. In this work we studied an influence of the environment characteristics on IAR parameters by the means of multipoint observations. Annual data series recorded at Ukrainian Antarctic Station 'Akademik Vernadsky', Low Frequency Observatory of the Institute of Radio Astronomy near Kharkov (Ukraine) and magnetic station of Sayan Solar Observatory Mondy near Irkutsk (Russia) were used for the analysis. We investigated the behaviour of IAR parameters, such as probability of resonance lines registration and frequency spacing $\Delta F$, for annual and diurnal intervals. These parameters were compared with characteristics of the ionosphere above all of the observation points and geomagnetic activity.