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 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.

scholarly journals Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
D. J. Wagg ◽  
K. Worden ◽  
R. J. Barthorpe ◽  
P. Gardner
Keyword(s):  
Asset Management ◽  
State Of The Art ◽  
Industrial Applications ◽  
Transport Systems ◽  
Synthesis Process ◽  
Small Scale ◽  
Mathematical Framework ◽  
Digital Twin ◽  
Digital Twins

Abstract This paper presents a review of the state of the art for digital twins in the application domain of engineering dynamics. The focus on applications in dynamics is because: (i) they offer some of the most challenging aspects of creating an effective digital twin, and (ii) they are relevant to important industrial applications such as energy generation and transport systems. The history of the digital twin is discussed first, along with a review of the associated literature; the process of synthesizing a digital twin is then considered, including definition of the aims and objectives of the digital twin. An example of the asset management phase for a wind turbine is included in order to demonstrate how the synthesis process might be applied in practice. In order to illustrate modeling issues arising in the construction of a digital twin, a detailed case study is presented, based on a physical twin, which is a small-scale three-story structure. This case study shows the progression toward a digital twin highlighting key processes including system identification, data-augmented modeling, and verification and validation. Finally, a discussion of some open research problems and technological challenges is given, including workflow, joints, uncertainty management, and the quantification of trust. In a companion paper, as part of this special issue, a mathematical framework for digital twin applications is developed, and together the authors believe this represents a firm framework for developing digital twin applications in the area of engineering dynamics.

scholarly journals Extended isolation forest – application to outlier detection in geomagnetic data

2021 ◽  
Vol 929 (1) ◽  
pp. 012022
Author(s):  
S A Imashev
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Geomagnetic Field ◽  
Daily Variation ◽  
Sampling Rate ◽  
Geomagnetic Data ◽  
Detection Of Outliers ◽  
Isolation Forest

Abstract The aim of this study is to present a method for detection of outliers in the time series of total intensity of geomagnetic field using Extended Isolation Forest algorithm. The method is consisted of three steps: 1) generation of additional features that take into account the regular daily variation and smooth behaviour of normal data, 2) detection of potential outliers based on ensemble of extended isolating trees and 3) subsequent refinement based on difference between the outlier and its replacement with interpolated value. Application of the method for detection of outliers in yearly time series of the total geomagnetic field at Ak-Suu and Kegety stations showed that the algorithm identifies both global and contextual outliers. Average classification metrics for the method are characterized as high and have the following values: precision 94.3%, recall 93.9% and F-score 94.5%, and probabilities of errors of the first and second kind are comparable to similar algorithms used for detection of outliers in magnetograms of different sampling rate.

scholarly journals Geomagnetic Virtual Observatories: monitoring geomagnetic secular variation with the Swarm satellites

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Magnus D. Hammer ◽  
Grace A. Cox ◽  
William J. Brown ◽  
Ciarán D. Beggan ◽  
Christopher C. Finlay
Keyword(s):  
Time Series ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Time Windows ◽  
Low Earth Orbit ◽  
Data Selection ◽  
Spherical Harmonic Analysis ◽  
Core Field ◽  
The Core ◽  
Swarm Satellites

AbstractWe present geomagnetic main field and secular variation time series, at 300 equal-area distributed locations and at 490 km altitude, derived from magnetic field measurements collected by the three Swarm satellites. These Geomagnetic Virtual Observatory (GVO) series provide a convenient means to globally monitor and analyze long-term variations of the geomagnetic field from low-Earth orbit. The series are obtained by robust fits of local Cartesian potential field models to along-track and East–West sums and differences of Swarm satellite data collected within a radius of 700 km of the GVO locations during either 1-monthly or 4-monthly time windows. We describe two GVO data products: (1) ‘Observed Field’ GVO time series, where all observed sources contribute to the estimated values, without any data selection or correction, and (2) ‘Core Field’ GVO time series, where additional data selection is carried out, then de-noising schemes and epoch-by-epoch spherical harmonic analysis are applied to reduce contamination by magnetospheric and ionospheric signals. Secular variation series are provided as annual differences of the Core Field GVOs. We present examples of the resulting Swarm GVO series, assessing their quality through comparisons with ground observatories and geomagnetic field models. In benchmark comparisons with six high-quality mid-to-low latitude ground observatories we find the secular variation of the Core Field GVO field intensities, calculated using annual differences, agrees to an rms of 1.8 nT/yr and 1.2 nT/yr for the 1-monthly and 4-monthly versions, respectively. Regular sampling in space and time, and the availability of data error estimates, makes the GVO series well suited for users wishing to perform data assimilation studies of core dynamics, or to study long-period magnetospheric and ionospheric signals and their induced counterparts. The Swarm GVO time series will be regularly updated, approximately every four months, allowing ready access to the latest secular variation data from the Swarm satellites.

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