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<strong>First</strong> <strong>International</strong> <strong>Conference</strong> on MOLDAVIAN RISKS - FROM GLOBAL TO LOCAL SCALE 16-19 May 2012, Bacau, Romania CLUSTERING OF EARTHQUAKE EPICENTER DATA BY DISCRETE PERFECT SETS ALGORITHM Gvishiani A.D 1 , Agayan S.M. 1 , Dobrovolsky M.N. 1 , Bogoutdinov Sh. R. 1 , Mandea M. 2 1 Geophysical Center of Russian Academy of Sciences (GC RAS), Moscow, Russia 2 CNES - Centre National d'Etudes Spatiales Corresponding author: a.gvishiani@gcras.ru Abstract: In the frame of “Discrete Mathematical Analysis” (DMA), launched by A. Gvishiani, J. Bonnin and S. Agayan in early 90s, numerous original fuzzy logic-based clustering algorithms have been designed. This branch of the DMA is called by DMAclustering. A new Discrete Perfect Sets (DPS) algorithm efficiently extends the DMAclustering branch. Algorithms of DMA-clustering deal with very noisy data and do not require separability of the sets under consideration. They focus on topological filtering of multidimensional data sets. This filtering allows efficient cutting off non-essential parts of subsets. In this sense, DMA-clustering represents a “postcluster” stage in cluster analysis. A formal definition of density of a multidimensional data set in each of its points serves as a basis of DMA-clustering algorithms. This formal definition allows us to introduce formal concepts of concentration, cluster and track. The paper is devoted to presentation of the DPS algorithm, DMA-monitoring of the seismic process in California and some other geophysical applications. Prospectives for Romanian and Moldavian territories will be depicted as well. 18
<strong>First</strong> <strong>International</strong> <strong>Conference</strong> on MOLDAVIAN RISKS - FROM GLOBAL TO LOCAL SCALE 16-19 May 2012, Bacau, Romania DEVELOPMENT OF EARTHQUAKE-PRONE AREAS PATTERN RECOGNITION FROM 1972 TO 2012 A.D. Gvishiani 1 , A.I. Gorshkov 2 , A. A. Soloviev 2 1 Geophysical Center, Russian Academy of Sciences, Moscow, Russia 2 Institute of Earthquake Prediction Theory and Mathematical Geophysics, Russian Academy of Sciences, Moscow, Russia Corresponding author: gvi@wdcb.ru Abstract: Accurate definition of earthquake sources plays a main role in development of seismic hazard assessment. We present the history of the development and the results of long-term implementation of an original methodology dedicated to pattern recognition of earthquake prone areas. The methodology was initiated by the pioneering work by I.Gelfand, V.Keilis-Borok, E.Rantsman (1972). It was based on the hypothesis, derived from the observations, that large earthquakes are associated with nodes, specific structures forming around the intersections of the fault zones. The further development and implementation of the methodology has been carried out in a close international cooperation with well known geologists, seismologists and geophysicists, including M.Caputo and G.Panza (Italy), L.Knopoff and F.Press (USA), A.Cisternas, H. Philip, C.Weber (France), M.Garcia-Fernandez (Spain). Since 1972 over 150 papers on the development and testing the methodology in different seismic regions of the world have been published in Russian and international journals and have been presented at international conferences. Seismogenic nodes for different target magnitudes have been recognized in numerous seismic regions worldwide including most part of the Alpine belt from Pyrenees to Himalaya, South America Andes, California, Kamchatka, Iberian Peninsula and some others. 86 earthquakes of target magnitudes occurred in the regions studied with this pattern recognition approach since 1972 up to now. 71 of them took place within the nodes recognized to be potential sites for strong earthquakes prior to their occurrence. Note that 22 out of these 71 earthquakes occurred at nodes, where, at the moment of the recognition, events of target magnitude have been unknown yet. Six postpublication earthquakes took place at nodes defined by pattern recognition as non potential sites for target magnitudes. Nine earthquakes happened outside the nodes. This 40 years implementation provided strong evidences in favor of reliability of the methodology. The results obtained for the mountain regions encouraged us to extend the methodology to intraplate regions of moderate seismicity, specifically, the interiors of the Iberian Peninsula and some areas within the Russian and West-European platforms. The paper describes the stages of development of pattern recognition of earthquake-prone areas during these 40 years and shows future prospective of the method. Special attention is paid to earthquakeprone nodes in Romania and Moldova. 19
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Page 4 and 5: Editors: Mioara Mandea, Gabriel Laz
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Sponsors of the conference Sodexo P
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