Assessment of Seismogeodynamic Activity of Mining Areas on the Basis of 3D Geoinformation Modeling

The article deals with the creation of an information and analytical platform for assessing external causes of technogenic (man-made) accidents and for improving methods of their predication and prevention on the territories of long-term geological, mining, and engineering activities on the Southeastern (SE) Transbaikal region example — one of the key areas for mining and extraction of the strategic raw minerals in Russia. 3D modeling of active geological structures and hazardous seismogeodynamic processes on GIS-based technologies is a key instrument for forecasting dangerous natural and technogenic events and risk reduction of their occurrence. A matter of minimizing consequences of natural and technogenic disasters for such facilities of increased technological and environmental hazards as minefields, radiochemical facilities, sites for spent nuclear material disposal, dams, gas and oil pipelines, etc. has become essential. It is necessary to assess modern geodynamic territory’s activity on a unified geoinformation platform applying the newest integrated geological-geophysical researches’ methods for these objects, to select seismically active fault-fracture zones, to determine the rate and direction of surface displacement, to establish patterns of subsoil stressed-strained state natural component influence on behavior of local technogenic processes. Taking into account the specificities of geotectonic structure of mining territories, the structure of monitoring seismogeodynamic processes network on multifunctional geodynamic test sites should be developed. Observations made on test sites form the information basis for forecasting of lithosphere condition, taking decisions on rational subsurface management and providing ecological safety of the territory.

New Approach for Analyzing Marine Ecosystem Structure Using Bayesian Networks

Aquatic ecosystems of the Black Sea are complex multiparametric systems with a hierarchical structure. Thus, the main goal of our research was to investigate possibilities of using Bayesian networks to study the structure fo the natural systems in the Black Sea. We used CMEMS Black Sea environmental dataset, which consists of 7 different variables, that, in our opinion, can describe structural relations in the Black Sea ecosystem -- sea surface temperature and salinity, concentrations of nitrates and phosphates, amount of chlorophyll-a and net primary production and also dissolved oxygen concentration. We think, that these variables can generally define interactions in water environment of the Black Sea, organisms, that live there and human activity. As a modelling result, we receive a structure of environmental variables interactions. At the top of this structure is a dissolved oxygen, as a final result of the ecosystem functioning. Further, we think it's more appropriate to use Dynamic Bayesian networks for investigation of spatio-temporal changes to distinguish main drivers of changes and provide more balanced management of natural territories.