, S Karimi,
Volume 10, Issue 2 (2-2010)
Abstract
Abstra
The metapelites of North of Golpayegan show that these rocks can be divided into four categories based on mineral assemblages: chloritoid- garnet- biotite- schist, garnet- biotite- muscovite- schist, staurolite bearing garnet- biotite- muscovite schist and Kyanite bearing staurolite- biotite- muscovite- schist.
The appearance of chloritoid in chloritoid- garnet- biotite schists shows green schist facies. Garnets in garnet- biotite- muscovite schists shows 3 stage of growth and syn-tectonic formation.
The appearance of staurolite in staurolite bearing garnet- biotite- muscovite schists signifies the beginning of amphibolite facies. The absence of zoning in the staurolite contained in these schists suggests the formation and growth of this mineral in a prograde metamorphism.
The thermodynamic study of these rocks shows that North of Golpayegan's metapelites were formed within a temperature range of 480 – 560oC and a pressure range of 1.6 – 4.1 kbar. These results are consistent with the minerals' paragenetic evidence and show that effect of metamorphism on North of Golpayegan's pelitic sediments is to lower amphibolite facies (Epidote amphibolite).
Fatemeh Hosseini, Omid Karimi, Mohsen Mohammadzadeh,
Volume 13, Issue 3 (11-2013)
Abstract
Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed models, such that the spatial correlation of the data can be introduced via normal latent variables. The model parameters and the prediction of the latent variables at unsampled locations are of the most important interest in SGLMM by estimating of the latent variables at sampled locations. In these models, since there are the latent variables and non-Gaussian spatial response variables, likelihood function cannot usually be given in a closed form and maximum likelihood estimations may be computationally prohibitive. In this paper, a new algorithm is introduced for maximum likelihood estimation of the model parameters and predictions, that is faster than the former method. This algorithm obtains to combine the pseudo maximum likelihood method, the Expectation maximization Gradient algorithm and an approximate method. The performance and accuracy of the proposed model are illustrated through a simulation study. Finally, the model and the algorithm are applied to a case study on rainfall data observed in the weather stations of Semnan in 2012.
