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

Assessment of the influence of astronomical cyclicity on sedimentation processes in Eastern Paratethys based on paleomagnetic measurements using Discrete Mathematical Analysis

“…DMA was developed to create discrete equivalents of the concepts of classical mathematical analysis: for example, limit, continuity, smoothness, connectivity, monotonicity, and extremum. DMA methods and algorithms have proven to be useful in numerous studies related to the processing and analysis of various geological [Gvishiani et al, 2010], geophysical [Gvishiani et al, 2008a], geomagnetic [Bogoutdinov et al, 2010], seismological [Gvishiani et al, 2016] and other data [Gvishiani et al, 2008b].…”

CODATA and global challenges in data-driven science

“…Artificial intelligence (AI) [Gvishiani et al, 2002], such as machine learning, text and factographic data mining, as well as deep learning and IOT (Internet of Things) / IOE (Internet of Every-thing) have become prevalent as crucial versatile methodologies and technologies for conducting Data Science. Because these are in the middle of mainstream developments, sharing the most advanced technologies may be faced by a variety of problems such as digital divide between regions and challenges of reconciling Intellectual property (IP).…”

“…Two analytical platforms "Deductor" [https://basegroup.ru/deductor/description] and "PolyAnalyst" [http://www.megaputer.ru/ polyanalyst.php], that provide necessary tools for KBS creation were presented at the session. These examplar platforms demonstrate the possibilities of offered by Data Science methods, as applied in the generalization of the connections between the object experimental variables, as well as in forecasting of "new experimental results" without real experiments [Abrukov et al, 2007;Guhr et al, 1998;Bobyl A. et al, 2016, Generalized Radon-Nikodym Spectral Approach. Application to Relaxation Dynamics Study, https://arxiv.org/abs/1611.07386].…”

“…The session examined and discussed the results obtained by the creation and development of the phenomenological, system, and geoinformational approaches to the multivariate seismic hazard assessment based on artificial intellect algorithms. Also considered were the results of carrying out the seismic hazard assessment for specific tectonically active regions by means of these approaches and representation of the results using GIS technologies [Gvishiani et al, 2013].…”

“…The main conclusion from this analysis is that the identification of strong earthquake prone areas allows us to refine significantly the seismic hazard assessment for tectonically active regions. It facilitates the creation of the information basis for developing measures of damage reduction for civil and industrial infrastructure objects from seismic impacts [Gvishiani et al, 2016[Gvishiani et al, , 2017.…”