Sh. R. Bogoutdinov

Recognition of disturbances with specified morphology in time series. Part 1: Spikes on magnetograms of the worldwide INTERMAGNET network

The International Real-time Magnetic Observatory Network (INTERMAGNET) is the world’s biggest international network of ground-based observatories, providing geomagnetic data almost in real time (within 72 hours of collection) [Kerridge, 2001]. The observation data are rapidly transferred by the observatories participating in the program to regional Geomagnetic Information Nodes (GINs), which carry out a global exchange of data and process the results. The observations of the main (core) magnetic field of the Earth and its study are one of the key problems of geophysics. The INTERMAGNET system is the basis of monitoring the state of the Earth’s magnetic field; therefore, the information provided by the system is required to be very reliable. Despite the rigid high-quality standard of the recording devices, they are subject to external effects that affect the quality of the records. Therefore, an objective and formalized recognition with the subsequent remedy of the anomalies (artifacts) that occur on the records is an important task. Expanding on the ideas of Agayan [Agayan et al., 2005] and Gvishiani [Gvishiani et al., 2008a; 2008b], this paper suggests a new algorithm of automatic recognition of anomalies with specified morphology, capable of identifying both physically- and anthropogenically-derived spikes on the magnetograms. The algorithm is constructed using fuzzy logic and, as such, is highly adaptive and universal. The developed algorithmic system formalizes the work of the expert-interpreter in terms of artificial intelligence. This ensures identical processing of large data arrays, almost unattainable manually. Besides the algorithm, the paper also reports on the application of the developed algorithmic system for identifying spikes at the INTERMAGNET observatories. The main achievement of the work is the creation of an algorithm permitting the almost unmanned extraction of spike-free (definitive) magnetograms from preliminary records. This automated system is developed for the first time with the application of fuzzy logic system for geomagnetic measurements. It is important to note that the recognition of time disturbances is formalized and identical. The algorithm presented here appreciably increases the reliability of spike-free INTERMAGNET magnetograms, thus increasing the objectivity of our knowledge of the Earth’s magnetic field. At the same time, the created system can accomplish identical, formalized, and retrospective analysis of large archives of digital and digitized magnetograms, accumulated in the system of Worldwide Data Centers. The relevant project has already been initiated as a collaborative initiative of the Worldwide Data Center at Geophysical Center (Russian Academy of Sciences) and the NOAA National Geophysical Data Center (Unite States). Thus, by improving and adding objectivity to both new and historical initial data, the developed algorithmic system may contribute appreciably to improving our understanding of the Earth’s magnetic field.

Recognition of disturbances with specified morphology in time series: Part 2. Spikes on 1-s magnetograms

Preliminary magnetograms contain different types of temporal anthropogenic disturbances: spikes, baseline jumps, drifts, etc. These disturbances should be identified and filtered out during the preprocessing of the preliminary records for the definitive data. As of now, at the geomagnetic observatories, such filtering is carried out manually. Most of the disturbances in the records sampled every second are spikes, which are much more abundant than those on the magnetograms sampled every minute. Another important feature of the 1-s magnetograms is the presence of a plenty of specific disturbances caused by short-period geomagnetic pulsations, which must be retained in the definitive records. Thus, creating an instrument for formalized and unified recognition of spikes on the preliminary 1-s magnetograms would largely solve the problem of labor-consuming manual preprocessing of the magnetic records. In the context of this idea, in the present paper, we focus on recognition of the spikes on the 1-s magnetograms as a key point of the problem. We describe here the new algorithm of pattern recognition, SPs, which is capable of automatically identifying the spikes on the 1-s magnetograms with a low probability of missed events and false alarms. The algorithm was verified on the real magnetic data recorded at the French observatory located on Easter Island in the Pacific.

Algorithm for recognizing Pc3 geomagnetic pulsations in 1-s data from INTERMAGNET equatorial observatories

The methods are suggested for analyzing the data of three-component geomagnetic observations in order to automatically recognize time anomalies-pulsations in the geomagnetic field. These methods include preliminary bandpass filtering of the data, calculating the eigenvalues of the covariance matrix of magnetic components in a moving time window, computing the generalized variance of the eigenvalues (generalization is understood as raising to a power that is distinct from the traditional power of 2), averaging the variance, and identifying the time intervals marked by the presence of pulsations by the criterion of the averaged variance of eigenvalues to exceed a certain threshold specified by the fuzzy-logic methods.

Application of the SP algorithm to the INTERMAGNET magnetograms of the disturbed geomagnetic field

The algorithmic system developed in the Laboratory of Geoinformatics at the Geophysical Center, Russian Academy of Sciences, which is intended for recognizing spikes on the magnetograms from the global network INTERMAGNET provides the possibility to carry out retrospective analysis of the magnetograms from the World Data Centers. Application of this system to the analysis of the magnetograms allows automating the job of the experts-interpreters on identifying the artificial spikes in the INTERMAGNET data. The present paper is focused on the SP algorithm (abbreviated from SPIKE) which recognizes artificial spikes on the records of the geomagnetic field. Initially, this algorithm was trained on the magnetograms of 2007 and 2008, which recorded the quiet geomagnetic field. The results of training and testing showed that the algorithm is quite efficient. Applying this method to the problem of recognizing spikes on the data for periods of enhanced geomagnetic activity is a separate task. In this short communication, we present the results of applying the SP algorithm trained on the data of 2007 to the INTERMAGNET magnetograms for 2003 and 2005 sampled every minute. This analysis shows that the SP algorithm does not exhibit a worse performance if applied to the records of a disturbed geomagnetic field.

Pc3 geomagnetic pulsations at near-equatorial latitudes at the initial phase of the magnetic storm of April 5, 2010

An analysis of sampled 1-s observational data on geomagnetic pulsations within the Pc3 range on the INTERMAGNET network of near-equatorial and low-latitude observatories spaced over longitude during the initial phase of a moderate magnetic storm (April 5–7, 2010) was carried out for the first time. The obtained results were compared with magnetic observations at the low-latitude Chambon-la-Foret (CLF) and subauroral Kerguelen (PAF) observatories, as well as with observations at six Australian observatories located at low and middle latitudes. Two time intervals were studied in detail: the sudden commencement (SC) of the storm and the onset of the great global substorm. In the first interval, maximal amplitudes of near-equatorial pulsations were observed in the near-noon sector; in the second interval, in the near-midnight sector. The dynamics of the spectral structure of Pc3 pulsations in the considered events was shown to be different in spite of the fact that in both cases an amplification of waves was observed in two close spectral bands of the Pc3 spectrum: ∼20–30 and ∼30–40 mHz. The considered Pc3 pulsations were characterized by very small azimuthal wavenumbers (0.5 and less). Possible generation mechanisms for the observed Pc3 pulsations are discussed.

Fuzzy logic algorithms in the analysis of electrotelluric data with reference to monitoring of volcanic activity

The expert processing of monitoring data of large networks on hazardous natural phenomena becomes increasingly more complicated due to an increase in the initial data flow. An approach alternative to the visual recognition of signals is proposed. A number of recognition algorithms and results of their application to the analysis of geoelectric potential monitoring data are discussed. Data of monitoring La Fournaise Volcano (Réunion Island) obtained in the vicinity of the intense volcanic eruption of 1988 are used. The obtained results show that these algorithms are capable of recognizing anomalous segments of records and discriminating between several types of anomalies presumably associated with the effects of various physical factors (heavy atmospheric precipitation, hydrothermal processes, and so on). The algorithms proposed in this work can be used both for the automation of expert work in operating monitoring systems and in investigations aimed at the identification of typical morphologic sequences in time series of data of various origins.

Large-scale irregularities of the winter polar topside ionosphere according to data from Swarm satellites

An analysis of the electron density measurements (Ne) along the flyby trajectories over the high-latitude region of the Northern Hemisphere under winter conditions in 2014 and 2016 has shown that the main large-scale structure observed by Swarm satellites is the tongue of ionization (TOI). At the maximum of the solar cycle (F10.7 = 160), the average value of Ne in the TOI region at an altitude of 500 km was 8 × 104 cm–3. Two years later, at F10.7 = 100, Ne ~ 5 × 104 cm–3 and Ne ~2.5 × 104 cm–3 were observed at altitudes of 470 and 530 km, respectively. During the dominance of the azimuthal component of the interplanetary magnetic field, the TOI has been observed mainly on the dawn or dusk side depending on the sign of By. Simultaneous observations of the convective plasma drift velocity in the polar cap show the transpolar flow drift to the dawn (By < 0) or dusk side (By < 0). Observations and numerical simulation of the Ne distribution have confirmed the significant role of the electric field of the magnetospheric convection in the generation of large-scale irregularities in the polar ionosphere.