M Mojarab; H Memarian; M Zare; V Kossobokov
Abstract
The earthquake of 23 October 2011, near the Turkish city of Van, had 600 victims and caused great damages in Van, Argis, Moradiyeh and Caldiran. Review of 20th century and historical earthquakes in eastern Anatolian plate and west of Iranian plateau confirmed the activity of this area with the notable ...
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The earthquake of 23 October 2011, near the Turkish city of Van, had 600 victims and caused great damages in Van, Argis, Moradiyeh and Caldiran. Review of 20th century and historical earthquakes in eastern Anatolian plate and west of Iranian plateau confirmed the activity of this area with the notable earthquake of 24 November 1976 in Caldiran. The main objective of this paper is evaluation of predictability of earthquakes in this region. Presently, the two main approaches for predicting extreme events are precursory and pattern recognition algorithms. For this study, we applied M8 algorithm that is based on pattern recognition. In this respect,a 49 point network were designed around the epicenter of Van earthquake and M8 algorithm applied to this network. The end result was four zones with some overlaps that were proposed as CTIP (current time of increase probability). This study could predict the Van earthquake with 1/1/2008 to 30/12/2012 time window, 281 km local radius and magnitude of more than 7. In addition, forward prediction in this area shows there is no alarm for magnitude 7+ in next 5 years. This study showed the strength of M8 algorithm for predicting earthquakes in the Middle East. It can be concluded that using algorithms based on pattern recognition can play an important role for mitigation of damages in seismic events.
M.E Hekmatian; V Ebrahimzadeh Ardestani; M.A Riahi; A Memar Koucheh Bagh; J Amini
Abstract
Pattern recognition algorithms especially neural network in geophysical interpretations and other Earth sciences have been used since some years ago. In neural network and other pattern recognition algorithms like support vector classifier (SVC) that the latter method is used in this research, by using ...
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Pattern recognition algorithms especially neural network in geophysical interpretations and other Earth sciences have been used since some years ago. In neural network and other pattern recognition algorithms like support vector classifier (SVC) that the latter method is used in this research, by using the values of the features, which has been extracted from the objects (in our work gravity profiles are objects), classification of the objects can be done. Usually the features are selected subjectively. In this paper, we have presented a homemade software that can select proper features objectively. By using SVC and the mentioned features selection (FS) software, depth estimations of anticlines have been done in this research. We have shown the difference of using proper features and improper ones in the mentioned depth estimation (a kind of classification). In this paper, twenty synthetic gravity profiles with anticline shape sources are created for training SVC and the same amount of synthetic profiles are created for testing. It has shown that depth estimation with proper features is more precise than depth estimation with improper features. Also it should be emphasized that FS is important not only in depth estimation of anticlines, but also in all kinds of classifications in Earth sciences and the mentioned homemade software code is applicable in all of them.
Ahmad Zamani; S. Farahi Ghasre Aboonasr
Abstract
The Iranian plateau is one of the active tectonic regions on the earth. Non-uniformly distribution of deformation and repetitive activity of faults have cause a complex pattern of tectonic and seismotectonic activity of Iran. Therefore, in order to study the seismic and geological behaviors of different ...
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The Iranian plateau is one of the active tectonic regions on the earth. Non-uniformly distribution of deformation and repetitive activity of faults have cause a complex pattern of tectonic and seismotectonic activity of Iran. Therefore, in order to study the seismic and geological behaviors of different parts of the country one has to perform tectonic and seismotectonic zoning. Tectonic and seismotectonic zoning of Iran began by conventional methods in the past and developed by numerical zoning in recent years. Conventional methods aren't capable for producing detailed zoning maps. Recently numerical data and statistical and mathematical models have used for produce modern numerical maps. The advantage of numerical pattern recognition is that this method is a powerful tool for objective interpretation of massive of data. Multivariate statistical methods not only apply for tectonic zoning, but also this is useful to reveal the degree of significance and relationship between effective variables on tectonic zoning. In this paper, a large numbers of up-to-date geophysical, seismological, geological and geomorphological data have analyzed by using multivariate statistical methods to produced self-organized numerical tectonic and seismotectonic zoning of Iran. Based on this techniques a seven zoning tectonic and seismotectonic map has constructed for Iran. The role and significance of various parameters have also investigated using ANOVA method. The results indicate that some of the parameters play more important role in self-organized zoning. Based on relationships between parameters, they are been classified into 12 groups. Variables in each group present maximum correlation with each other. It is interesting to note that despite the frequent application of a- and b- values of the Gutenberg Richter magnitude frequency formula, these values show poor correlation with others and do not play a significant role in zoning.
