Extended isolation forest – application to outlier detection in geomagnetic data

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.

Analisis Big Data Geomagnetik Dengan Metode Diferensiasi Sebagai Prekursor Gempa Lombok Tahun 2018

Lombok is an area with the highest geomagnetic anomaly in Indonesia (Zubaidah et al., 2014). From the end of July to the end of August 2018, Lombok experienced a series of fairly large earthquakes. Identification of geomagnetic signals, especially in the Ultra Low Frequency (ULF) spectrum, can be used as earthquake precursors (Saroso, 2010). Intermagnet IAGA (International Aeronomy Geomagnetic Association) is a network of international geomagnetic observatory stations that have large world geomagnetic data. Big data analysis is very important because very large information is needed in disaster mitigation. This study uses geomagnetic data per second for 24 hours from 28 August to 30 November 2018 taken from Kakadu (KDU) Australia and Nurul Bayan Station (NRB) Lombok. The analytical method used is Differentiation by calculating the F value (total magnetic field) for KDU and NRB, then look for the difference and analyze the pattern. The results found that there was an anomaly phenomenon of the Earth's magnetic field in Nurul Bayan Lombok which was detected for 17 days during October 2018.

Simulation experiment to test strategies of geomagnetic navigation during long-distance bird migration

Background Different theories suggest birds may use compass or map navigational systems associated with Earth’s magnetic intensity or inclination, especially during migratory flights. These theories have only been tested by considering properties of the Earth’s magnetic field at coarse temporal scales, typically ignoring the temporal dynamics of geomagnetic values that may affect migratory navigational capacity. Methods We designed a simulation experiment to study if and how birds use the geomagnetic field during migration by using both high resolution GPS tracking data and geomagnetic data at relatively fine spatial and temporal resolutions in comparison to previous studies. Our simulations use correlated random walks (CRW) and correlated random bridge (CRB) models to model different navigational strategies based on underlying dynamic geomagnetic data. We translated navigational strategies associated with geomagnetic cues into probability surfaces that are included in the random walk models. Simulated trajectories from these models were compared to the actual GPS trajectories of migratory birds using 3 different similarity measurements to evaluate which of the strategies was most likely to have occurred. Results and conclusion We designed a simulation experiment which can be applied to different wildlife species under varying conditions worldwide. In the case of our example species, we found that a compass-type strategy based on taxis, defined as movement towards an extreme value, produced the closest and most similar trajectories when compared to original GPS tracking data in CRW models. Our results indicate less evidence for map navigation (constant heading and bi-gradient taxis navigation). Additionally, our results indicate a multifactorial navigational mechanism necessitating more than one cue for successful navigation to the target. This is apparent from our simulations because the modelled endpoints of the trajectories of the CRW models do not reach close proximity to the target location of the GPS trajectory when simulated with geomagnetic navigational strategies alone. Additionally, the magnitude of the effect of the geomagnetic cues during navigation in our models was low in our CRB models. More research on the scale effects of the geomagnetic field on navigation, along with temporally varying geomagnetic data could be useful for further improving future models.

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.

Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration

Background Migratory animals use information from the Earth’s magnetic field on their journeys. Geomagnetic navigation has been observed across many taxa, but how animals use geomagnetic information to find their way is still relatively unknown. Most migration studies use a static representation of geomagnetic field and do not consider its temporal variation. However, short-term temporal perturbations may affect how animals respond - to understand this phenomenon, we need to obtain fine resolution accurate geomagnetic measurements at the location and time of the animal. Satellite geomagnetic measurements provide a potential to create such accurate measurements, yet have not been used yet for exploration of animal migration. Methods We develop a new tool for data fusion of satellite geomagnetic data (from the European Space Agency’s Swarm constellation) with animal tracking data using a spatio-temporal interpolation approach. We assess accuracy of the fusion through a comparison with calibrated terrestrial measurements from the International Real-time Magnetic Observatory Network (INTERMAGNET). We fit a generalized linear model (GLM) to assess how the absolute error of annotated geomagnetic intensity varies with interpolation parameters and with the local geomagnetic disturbance. Results We find that the average absolute error of intensity is − 21.6 nT (95% CI [− 22.26555, − 20.96664]), which is at the lower range of the intensity that animals can sense. The main predictor of error is the level of geomagnetic disturbance, given by the Kp index (indicating the presence of a geomagnetic storm). Since storm level disturbances are rare, this means that our tool is suitable for studies of animal geomagnetic navigation. Caution should be taken with data obtained during geomagnetically disturbed days due to rapid and localised changes of the field which may not be adequately captured. Conclusions By using our new tool, ecologists will be able to, for the first time, access accurate real-time satellite geomagnetic data at the location and time of each tracked animal, without having to start new tracking studies with specialised magnetic sensors. This opens a new and exciting possibility for large multi-species studies that will search for general migratory responses to geomagnetic cues. The tool therefore has a potential to uncover new knowledge about geomagnetic navigation and help resolve long-standing debates.