Detection of Artificial ULF Signals at Staraya Pustyn Magnetic Station during the FENICS-2019 Experiment

2021 ◽  
Vol 61 (3) ◽  
pp. 365-375
Author(s):  
A. V. Ryabov ◽  
V. A. Pilipenko ◽  
E. N. Ermakova ◽  
N. G. Mazur ◽  
E. N. Fedorov ◽  
...  
Keyword(s):  
Magnetic Station

scholarly journals Development and application of problem-oriented digital twins for magnetic observatories and variation stations

2021 ◽  
pp. 60-71
Author(s):  
Andrei Vorobev ◽  
Vyacheslav Pilipenko ◽  
Gulnara Vorobeva ◽  
Olga Khristodulo
Keyword(s):  
Time Series ◽  
Geomagnetic Field ◽  
Missing Values ◽  
Information Environment ◽  
Small Scale ◽  
Geomagnetic Variation ◽  
Digital Twin ◽  
Digital Twins ◽  
Geomagnetic Data ◽  
Magnetic Station

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.

scholarly journals Geomagnetic data recovery approach based on the concept of digital twins

2021 ◽  
Vol 7 (2) ◽  
pp. 53-62
Author(s):  
Andrey Vorobev ◽  
Vyacheslav Pilipenko
Keyword(s):  
Missing Values ◽  
Auroral Zone ◽  
Automatic Identification ◽  
Digital Twins ◽  
Geomagnetic Data ◽  
Efficient Processing ◽  
Data Source ◽  
Recovery Approach ◽  
Magnetic Station

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.

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

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 859 ◽  
Author(s):  
Peng Han ◽  
Jiancang Zhuang ◽  
Katsumi Hattori ◽  
Chieh-Hung Chen ◽  
Febty Febriani ◽  
...  
Keyword(s):  
Low Frequency ◽  
Magnetic Anomalies ◽  
Prediction Performance ◽  
Magnetic Data ◽  
Optimal Prediction ◽  
Forecasting Performance ◽  
Earthquake Predictions ◽  
Precursory Information ◽  
Better Than ◽  
Magnetic Station

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.

scholarly journals Multipoint observations of Ionospheric Alfvén Resonance

2016 ◽  
Vol 6 (1) ◽  
pp. 45-49 ◽  
Author(s):  
N. Baru ◽  
A. Koloskov ◽  
Y. Yampolsky ◽  
R. Rakhmatulin
Keyword(s):  
Low Frequency ◽  
Solar Observatory ◽  
Resonance Structure ◽  
Data Series ◽  
Annual Data ◽  
The Earth ◽  
Electromagnetic Resonances ◽  
Global Nature ◽  
Propagation Medium ◽  
Magnetic Station

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.

scholarly journals Geomagnetic data recovery approach based on the concept of digital twins

2021 ◽  
Vol 7 (2) ◽  
pp. 48-56
Author(s):  
Andrey Vorobev ◽  
Vyacheslav Pilipenko
Keyword(s):  
Missing Values ◽  
Auroral Zone ◽  
Automatic Identification ◽  
Digital Twins ◽  
Geomagnetic Data ◽  
Efficient Processing ◽  
Data Source ◽  
Recovery Approach ◽  
Magnetic Station

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.

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