Anatoly Soloviev

Mathematical Tools for Geomagnetic Data Monitoring and the Intermagnet Russian Segment

In this paper, a new approach to the detection of anomalies in geophysical records is connected with a fuzzy mathematics application. The theory of discrete mathematical analysis and collection of algorithms for time series processing constructed on its basis represents the results of this research direction. These algorithms are the consequence of fuzzy modeling of the logic of an interpreter who visually recognizes anomalies in records. They allow analyzing large data sets that are not subjected to manual processing. The efficiency of these algorithms is demonstrated in several important geophysical applications. Plans for an extension of the Russian INTERMAGNET segment are presented.

Recognition of earthquake-prone areas: Methodology and analysis of the results

We present the results of verifying the areas that were detected as prone to strong earthquakes by the pattern recognition algorithms in different regions of the world with different levels of seismicity and, therefore, different threshold magnitudes demarcating the strong earthquakes. The analysis is based on the data presented in the catalog of the U.S. National Earthquake Information Center (NEIC) as of August 1, 2012. In each of the regions considered, we examined the locations of the epicenters of the strong earthquakes that occurred in the region after the publication of the corresponding result. There were 91 such earthquakes in total. The epicenters of 79 of these events (87%) fall in the recognized earthquake-prone areas, including 27 epicenters located in the areas where no strong earthquakes had ever been documented up to the time of publication of the result. Our analysis suggests that the results of the recognition of areas prone to strong earthquakes are reliable and that it is reasonable to use these results in the applications associated with the assessment of seismic risks. The comparison of the recognition for California with the analysis of seismicity of this region by the Discrete Perfect Sets (DPS) algorithm demonstrates the agreement between the results obtained by these two different methods.

Numerical modeling of block structure dynamics: dependence of a synthetic earthquake flow on the structure separateness and boundary movements

A seismically active region is modelled as a system of absolutely rigid blocks separated by infinitely thin plane faults. The interaction of the blocks along the fault planes and with the underlying medium is viscous-elastic. The system of blocks moves as a consequence of prescribed motion of the boundary blocks and of the underlying medium. When for some part of a fault plane the ratio of the stress to the pressure exceeds a certain strength level a stress-drop (‘a failure’) occurs (in accordance with the dry friction model), and it can cause a failure in other parts of the fault planes. In the model the failures represent earthquakes. As a result of the numerical simulation a synthetic earthquake catalog is produced. The numerical modelling was carried out for three types of structures with increasing of the structure separateness inside of each type and for two types of boundary movements. A synthetic earthquake flow is characterised by several features including the frequency-magnitude relation (the Gutenberg-Richter curve). When the structure separateness increases the slope of the curve changes monotonously in the same direction for all considered types of structures if the boundary movement is the same. The directions of changing of the slope are opposite for two considered boundary movements.

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.

Survey of Geomagnetic Observations Made in the Northern Sector of Russia and New Methods for Analysing Them

An overview of the geomagnetic observations made in the northern part of Russia is presented from a historical perspective. Several stations were deployed on the territory of the former Soviet Union during the International Geophysical Year, 1957–1958, with the active participation and guidance of the Interagency Geophysical Committee which is inherited by the Geophysical Center of the Russian Academy of Sciences (GC RAS). In the 1990s, the majority of these stations, especially those in the remoter regions, were closed. Nowadays, the geomagnetic network, including the observatories of the INTERMAGNET program, has been restored. Examples of high-latitude geomagnetic variations in the Russian longitudinal sector are shown, and maps and trends of the secular variation over the territory of Russia presented. Particular attention is paid to the automated processing of data and to the analysis methods used. To process the growing amount of high-resolution geomagnetic data, sophisticated mathematical methods based on the fuzzy logic approach and new discrete mathematical analysis algorithms have been developed. The formal methods and algorithms for recognizing both artificial and natural disturbances in the magnetograms are described.

Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory

In the present paper we apply a recently developed pattern recognition algorithm SPs to the problem of automated detection of artificial disturbances in one-second magnetic observatory data. The SPs algorithm relies on the theory of discrete mathematical analysis, which has been developed by some of the authors for more than 10 years. It continues the authors’ research in the morphological analysis of time series using fuzzy logic techniques. We show that, after a learning phase, this algorithm is able to recognize artificial spikes uniformly with low probabilities of target miss and false alarm. In particular, a 94% spike recognition rate and a 6% false alarm rate were achieved as a result of the algorithm application to raw one-second data acquired at the Easter Island magnetic observatory. This capability is critical and opens the possibility to use the SPs algorithm in an operational environment.

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.

Application of the data on the lithospheric magnetic anomalies in the problem of recognizing the earthquake prone areas

For the first time, an attempt is made to apply the data on the lithospheric magnetic anomalies of the Earth for determining the areas prone to strong earthquakes by means of the pattern recognition algorithms. The Caucasian region with the threshold magnitude of the strong earthquakes M0 = 6 is considered. It is established that the data on the lithospheric magnetic anomalies are informative from the standpoint of recognizing the strong earthquake prone areas. Application of these data is promising for solving the similar problems for different seismically active regions.

Geomagnetic activity during St. Patrick''s Day storm inferred from global and local indicators

In this research, the comparison between the results of geomagnetic activity monitoring using the new local indicators of geomagnetic activity and the traditional geomagnetic indices for geomagnetic activity analysis is made for the period of the strongest geomagnetic disturbance of the current solar cycle – the St. Patrick’s Day storm (17–18 March 2015). The results of the research demonstrated that the classification of magnetic activity using the mentioned indicators does not contradict the classical methods. The local indicators, applied to recognition of disturbances in the magnetic observatory data, seem an effective tool for geomagnetic activity analysis, as they reveal the characteristic features of geomagnetic disturbances typical for the observatory latitudinal location and show agreement the conventional geomagnetic disturbance distribution and its evolution during a magnetic storm. This is the e-book version of the article, published in Russian Journal of Earth Sciences (doi:10.2205/2016ES000593). It is generated from the original source file using LaTeX’s ebook.cls class.