S. M. Agayan

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.

A new approach to recognition of the strong earthquake-prone areas in the Caucasus

Clustering the epicenters of Caucasian earthquakes with magnitudes M ≥ 3.0 is carried out, and the epicentral zones of the probable earthquakes with M ≥ 5.0 areas where epicenters of earthquakes with M ≤ 5.0 may occur are recognized by the Fuzzy Clustering and Zoning (FCAZ) algorithmic system developed by the authors at the Geophysical Center of the Russian Academy of Sciences. These zones correspond well to the locations of the epicenters of earthquakes with M ≥ 5.0. The zones recognized in this study are compared with the zones previously recognized by A.D. Gvishiani et al. in 1988 by the Earthquake-Prone Areas Recognition (EPA) technique. The comparison shows that the zones identified by FCAZ are mainly located inside the EPA-zones. The FCAZ-zones are also compared with the zones previously recognized using gravimetric and geological data. The results obtained by different methods closely agree.Contrary to EPA technique FCAZ algorithmic system relies on the DPS algorithm of objective classification that requires only the information about epicenters of the earthquakes in the region under study.

FCaZm intelligent recognition system for locating areas prone to strong earthquakes in the Andean and Caucasian mountain belts

The fuzzy clustering and zoning method (FCAZm) of systems analysis is suggested for recognizing the areas of the probable generation of the epicenters of significant, strong, and the strongest earthquakes. FCAZm is a modified version of the previous FCAZ algorithmic system, which is advanced by the creation of the blocks of artificial intelligence that develop the system-forming algorithms. FCAZm has been applied for recognizing areas where the epicenters of the strongest (M ≥ 73/4) earthquakes within the Andes mountain belt in the South America and significant earthquakes (M ≥ 5) in the Caucasus can emerge. The reliability of the obtained results was assessed by the seismic-history type control experiments. The recognized highly seismic zones were compared with the ones previously recognized by the EPA method and by the initial version of the FCAZ system. The modified FCAZm system enabled us to pass from simple pattern recognition in the problem of recognizing the locations of the probable emergence of strong earthquakes to systems analysis. In particular, using FCAZm we managed to uniquely recognize a subsystem of highly seismically active zones from the nonempty complement using the exact boundary.

Weighted gravitational time series smoothing

This article continues the series of papers by the authors on the new universal DMA-smoothing of time series, originally intended for the analysis of geophysical time series obtained in the framework of discrete mathematical analysis (DMA), developed by GC RAS. We formulated the general concept of weighted DMA-smoothing, constructed and analyzed one of its variants. This is the e-book version of the article, published in the Russian Journal of Earth Sciences (doi:10.2205/2014ES000543). It is generated from the original source file using LaTeX’s epub.cls class.

Integration of data mining methods for Earth science data analysis in gis environment

Spatial data handling and analysis is one of the most important trends in modern computer oriented geophysics and geology. This article describes a software complex designed for integration of geodata analysis algorithms in a unified geoinformation environment. The developed software system provides access to an extensive geodatabase on Earth sciences and constantly updated catalog of algorithms and requires only a Web browser and Internet connection. This paper contains a mathematical description of some methods of data mining and data analysis, which have been already incorporated into the system. The discussed results also include the application of the algorithms, arranged in a database, to geological and geophysical data within the GIS environment.

Strongest Earthquake-Prone Areas in Kamchatka

The paper continues the series of our works on recognizing the areas prone to the strongest, strong, and significant earthquakes with the use of the Formalized Clustering And Zoning (FCAZ) intellectual clustering system. We recognized the zones prone to the probable emergence of epicenters of the strongest (M ≥ 74/3) earthquakes on the Pacific Coast of Kamchatka. The FCAZ-zones are compared to the zones that were recognized in 1984 by the classical recognition method for Earthquake-Prone Areas (EPA) by transferring the criteria of high seismicity from the Andes mountain belt to the territory of Kamchatka. The FCAZ recognition was carried out with two-dimensional and three-dimensional objects of recognition.

Recognition of strong earthquake–prone areas with a single learning class

This article presents a new Barrier recognition algorithm with learning, designed for recognition of earthquake-prone areas. In comparison to the Crust (Kora) algorithm, used by the classical EPA approach, the Barrier algorithm proceeds with learning just on one “pure” high-seismic class. The new algorithm operates in the space of absolute values of the geological–geophysical parameters of the objects. The algorithm is used for recognition of earthquake-prone areas with М ≥ 6.0 in the Caucasus region. Comparative analysis of the Crust and Barrier algorithms justifies their productive coherence.

GIS-oriented solutions for advanced clustering analysis of geoscience data using ArcGIS platform

This paper presents software solutions for integration of geoscience data and data processing algorithms based on the Discrete Mathematical Analysis (DMA) in GIS environment. The DMA algorithms have been adapted and implemented within the ESRI ArcGIS software as geoprocessing tools and combined into a single set of tools named “Clustering”. This set can be used along with the standard ArcGIS geoprocessing instruments. The tools of the “Clustering” set have also been published on the GIS-server as geoprocessing services providing powerful analytical functions via the Internet. This paper gives a brief outlook of the geoprocessing tools preparation techniques. The results of DMA-based geoprocessing tools’ application to geophysical data are also discussed.

Definition of stochastic continuity by fuzzy logic methods and geophysical applications

The presentation deals with modeling of continuity on stochastic time series by methods of fuzzy logic in the framework of discrete mathematical analysis, elaborated in the GC RAS, a new approach to data analysis. The basis of modeling is so-called the iteration linear bounds and the iteration scalar bounds. Results are applied to searching anomalies on time series of geophysical nature.