The continuous growth of geophysical observations requires adequate methods for their processing and analysis. This becomes one of the most important and widely discussed issues in the data science community. The system analysis methods and data mining techniques are able to sustain the solution of this problem. This paper presents an innovative holistic hardware/software system (HSS) developed for efficient management and intellectual analysis of geomagnetic data, registered by Russian geomagnetic observatories and international satellites. Geomagnetic observatories that comprise the International Real-time Magnetic Observatory Network (INTERMAGNET) produce preliminary (raw) and definitive (corrected) geomagnetic data of the highest quality. The designed system automates and accelerates routine production of definitive data from the preliminary magnetograms, obtained by Russian observatories, due to implemented algorithms that involve artificial intelligence elements. The HSS is the first system that provides sophisticated automatic detection and multi-criteria classification of extreme geomagnetic conditions, which may be hazardous for technological infrastructure and economic activity in Russia. It enables the online access to digital geomagnetic data, its processing results and modelling calculations along with their visualization on conventional and spherical screens. The concept of the presented system agrees with the accepted 'four Vs' paradigm of Big Data. The HSS can increase significantly the 'velocity' and 'veracity' features of the INTERMAGNET system. It also provides fusion of large sets of ground-based and satellite geomagnetic data, thus facilitating the 'volume' and 'variety' of handled data.

Modern satellite gravity missions and ground gravimetry provide operational data models that can be used in various studies in geology, tectonics, and climatology, etc. In the present study, sedimentary basins in the southern part of the East European Platform and adjoining areas including the Caucasus are studied by employing the approach based on decompensative gravity anomalies. The new model of sediments, implying their thickness and density, demonstrates several important features of the sedimentary cover, which were not or differently imaged by previous studies. We found a significant redistribution of the low-dense sediments in the Black Sea. Another principal feature is the increased thickness of relatively low-dense sediments in the Eastern Greater Caucasus. The deepest part of the South Caspian basin is shifted to the north, close to the Apsheron Trough. In its present position, it is almost joined with the Terek–Caspian depression, which depth is also increased. The thickness of sediments is significantly decreased in the eastern Pre-Caspian basin. Therefore, the new sedimentary cover model gives a more detailed description of its thickness and density, reveals new features and helps in better understanding of the evolution of the basins, providing a background for further detailed studies of the region.

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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. KEYWORDS: Geoscience data analysis; cluster analysis; geoprocessing tools; geoprocessing services; web-oriented GIS.Citation: Soloviev, A. A., J. I. Zharkikh, R. I. Krasnoperov, B. P. Nikolov, and S. M. Agayan (2016), GIS-oriented solutions for advanced clustering analysis of geoscience data using ArcGIS platform, Russ.

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