Ranking of Attenuation Relationships Using Earthquake 11 August 2012 in Ahar- Varzaghan

In seismic hazard assessment for estimating damage function as well as linear and nonlinear response spectrum, we need to estimate seismic strong ground motion. For this purpose, we need ground motion prediction, which can be derived from stochastic or empirical methods. These methods lead to relationships called attenuation relationships. These attenuation relationships predict ground motions by means of seismic source characteristics such as magnitude and fault mechanism, and local parameters such as distance and soil type. Today, many attenuation relationships are presented by researchers. However, their results have conspicuous discrepancy even for the same region. Considering the increasing number and complexity of ground-motion prediction equations available for seismic hazard assessment, there is a definite need for an efficient, quantitative, and robust method to select and rank these models for a particular region of interest. Considering frequent seismic and destructive earthquakes in Iran, there is Significance appropriate Attenuation relation for a particular target area. Selecting the best model for target regions can significantly reduce epistemic uncertainty. This paper uses statistical methods to study goodness of fit 5 candidate attenuation relationships with observed data earthquake 11 August 2012 in Varzaghan. Five attenuation relationships studied [1-5] including Iran's relations with the world. To analyze the statistical distribution of the data set, statical graphics and goodness-of-fit of measurement are used. We show how observed ground-motion records can help to guide this process in a systematic and comprehensible way. A key element in this context is a new, likelihood based, goodnessof- fit measure that has the property not only to quantify the model fit, but also to measure in some degree how well the underlying statistical model assumptions are met. By design, this measure naturally scales between 0 and 1, with a value of 0.5 for a situation in which the model perfectly matches the sample distribution both in terms of mean and standard deviation. The ground motion models are four different classes of quality. Besides, by using statistical tests, the weight of each relation is obtained for using in seismic hazard analysis.