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Pattern recognition of seismic and morphostructural 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 between1980–2015 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.
Pattern recognition of seismic and morphostructural 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 a mong all nodes by new catalog generated Self-organizing feature maps (SOFM). Our input data are extracted from catalog, consisting long and latitude of event between1980-2015 with magnitude larger or equal to 4.5. It has contributed node D1 is candidate for big earthquakes in comparison with other nodes are in lower levels of this potential.