Correlation between thermoluminescence sensitivity and crystallization temperatures of quartz: Potential application in geothermometry

The thermoluminescence (TL) characteristics of quartz are highly dependent of its thermal history. Based on the enhancement of quartz luminescence occurred after heating, some authors proposed to use quartz TL to recover thermal events that affected quartz crystals. However, little is know about the influence of the temperature of quartz crystallization on its TL characteristics. In the present study, we evaluate the TL sensitivity and dose response curves of hydrothermal and metamorphic quartz with crystallization temperatures from 209 +/- 15 to 633 +/- 27 degrees C determined through fluid inclusion and mineral chemistry analysis. The studied crystals present a cooling thermal history, which allow the acquiring of their natural TL without influence of heating after crystallization. The TL curves of the studied samples present two main components formed by different peaks overlapped around 110 C and 200-400 degrees C. The TL sensitivity in the 200-400 degrees C region increases linearly with the temperature of quartz crystallization. No relationship was observed between temperatures of quartz crystallization and saturation doses (<100 Gy). The elevated TL sensitivity of the high temperature quartz is attributed to the control exerted by the temperature of crystallization on the substitution of Si(4+) by ions such as Al(3+) and Ti(4+), which produce defects responsible for luminescence phenomena. The linear relationship observed between TL in the 200-400 degrees C region and crystallization temperature has potential use as a quartz geothermometer. The relative abundance of quartz in the earth crust and the easiness to measure TL are advantageous in relation to geothermometry methods based on chemistry of other minerals. (C) 2010 Elsevier Ltd. All rights reserved.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.