Scaling is an important measure of multi-scale fluctuation systems. Turbulence as the most remarkable multi-scale system possesses scaling over a wide range of scales. She-Leveque （SL） hierarchical symmetry, since i...Scaling is an important measure of multi-scale fluctuation systems. Turbulence as the most remarkable multi-scale system possesses scaling over a wide range of scales. She-Leveque （SL） hierarchical symmetry, since its publication in 1994, has received wide attention. A number of experimental, numerical and theoretical work have been devoted to its verification, extension, and modification. Application to the understanding of magnetohydrodynamic turbulence, motions of cosmic baryon fluids, cosmological supersonic turbulence, natural image, spiral turbulent patterns, DNA anomalous composition, human heart variability are just a few among the most successful examples. A number of modified scaling laws have been derived in the framework of the hierarchical symmetry, and the SL model parameters are found to reveal both the organizational order of the whole system and the properties of the most significant fluctuation structures. A partial set of work related to these studies are reviewed. Particular emphasis is placed on the nature of the hierarchical symmetry. It is suggested that the SL hierarchical symmetry is a new form of the self-organization principle for multi-scale fluctuation systems, and can be employed as a standard analysis tool in the general multi-scale methodology. It is further suggested that the SL hierarchical symmetry implies the existence of a turbulence ensemble. It is speculated that the search for defining the turbulence ensemble might open a new way for deriving statistical closure equations for turbulence and other multi-scale fluctuation systems.展开更多
Pattern recognition of seismic and mor- phostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have thi...Pattern recognition of seismic and mor- phostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have this potential. They are identified by morphostructural analysis. In this study, the Alborz region has considered as studied case and locations of future events are forecast based on Kohonen Self-Organized Neural Network. It has been shown how it can predict the location of earthquake, and identifies seismogenic nodes which are prone to earthquake of M5.5＋ at the West of Alborz in Iran by using International Institute Earthquake Engineering and Seismology earthquake catalogs data. First, the main faults and tectonic lineaments have been identified based on MZ （land zoning method） method. After that, by using pattern recognition, we generalized past recorded events to future in order to show the region of probable future earthquakes. In other word, hazardous nodes have determined among all nodes by new catalog generated Self-organizing feature maps （SOFM）. Our input data are extracted from catalog, consists longitude and latitude of past event between 1980-2015 with magnitude larger or equal to 4.5. It has concluded node D1 is candidate for big earthquakes in comparison with other nodes and other nodes are in lower levels of this potential.展开更多
基金the National Natural Science Foundation(90716008)MOST 973 project (2009CB724100)
文摘Scaling is an important measure of multi-scale fluctuation systems. Turbulence as the most remarkable multi-scale system possesses scaling over a wide range of scales. She-Leveque （SL） hierarchical symmetry, since its publication in 1994, has received wide attention. A number of experimental, numerical and theoretical work have been devoted to its verification, extension, and modification. Application to the understanding of magnetohydrodynamic turbulence, motions of cosmic baryon fluids, cosmological supersonic turbulence, natural image, spiral turbulent patterns, DNA anomalous composition, human heart variability are just a few among the most successful examples. A number of modified scaling laws have been derived in the framework of the hierarchical symmetry, and the SL model parameters are found to reveal both the organizational order of the whole system and the properties of the most significant fluctuation structures. A partial set of work related to these studies are reviewed. Particular emphasis is placed on the nature of the hierarchical symmetry. It is suggested that the SL hierarchical symmetry is a new form of the self-organization principle for multi-scale fluctuation systems, and can be employed as a standard analysis tool in the general multi-scale methodology. It is further suggested that the SL hierarchical symmetry implies the existence of a turbulence ensemble. It is speculated that the search for defining the turbulence ensemble might open a new way for deriving statistical closure equations for turbulence and other multi-scale fluctuation systems.
文摘Pattern recognition of seismic and mor- phostructural nodes plays an important role in seismic hazard assessment. This is a known fact in seismology that tectonic nodes are prone areas to large earthquake and have this potential. They are identified by morphostructural analysis. In this study, the Alborz region has considered as studied case and locations of future events are forecast based on Kohonen Self-Organized Neural Network. It has been shown how it can predict the location of earthquake, and identifies seismogenic nodes which are prone to earthquake of M5.5＋ at the West of Alborz in Iran by using International Institute Earthquake Engineering and Seismology earthquake catalogs data. First, the main faults and tectonic lineaments have been identified based on MZ （land zoning method） method. After that, by using pattern recognition, we generalized past recorded events to future in order to show the region of probable future earthquakes. In other word, hazardous nodes have determined among all nodes by new catalog generated Self-organizing feature maps （SOFM）. Our input data are extracted from catalog, consists longitude and latitude of past event between 1980-2015 with magnitude larger or equal to 4.5. It has concluded node D1 is candidate for big earthquakes in comparison with other nodes and other nodes are in lower levels of this potential.
