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Network community structure of substorms using SuperMAG magnetometers.

ABSTRACT: Geomagnetic substorms are a global magnetospheric reconfiguration, during which energy is abruptly transported to the ionosphere. Central to this are the auroral electrojets, large-scale ionospheric currents that are part of a larger three-dimensional system, the substorm current wedge. Many, often conflicting, magnetospheric reconfiguration scenarios have been proposed to describe the substorm current wedge evolution and structure. SuperMAG is a worldwide collaboration providing easy access to ground based magnetometer data. Here we show application of techniques from network science to analyze data from 137 SuperMAG ground-based magnetometers. We calculate a time-varying directed network and perform community detection on the network, identifying locally dense groups of connections. Analysis of 41 substorms exhibit robust structural change from many small, uncorrelated current systems before substorm onset, to a large spatially-extended coherent system, approximately 10 minutes after onset. We interpret this as strong indication that the auroral electrojet system during substorm expansions is inherently a large-scale phenomenon and is not solely due to many meso-scale wedgelets.

SUBMITTER: Orr L

PROVIDER: S-EPMC7988152 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Network community structure of substorms using SuperMAG magnetometers.

Orr L L Chapman S C SC Gjerloev J W JW Guo W W

Nature communications 20210323 1

Geomagnetic substorms are a global magnetospheric reconfiguration, during which energy is abruptly transported to the ionosphere. Central to this are the auroral electrojets, large-scale ionospheric currents that are part of a larger three-dimensional system, the substorm current wedge. Many, often conflicting, magnetospheric reconfiguration scenarios have been proposed to describe the substorm current wedge evolution and structure. SuperMAG is a worldwide collaboration providing easy access to ...[more]

PMID: 33758181

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Project description:BackgroundAnimal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors.MethodsWe address this by first leveraging magnetometers' similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth's local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition.ResultsThe overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture.ConclusionsMagnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor's strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.

Dataset Information

Network community structure of substorms using SuperMAG magnetometers.

Publications

Network community structure of substorms using SuperMAG magnetometers.

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