The Oligocene-Neogene deep-sea Columbia Channel system in the South Brazilian Basin: Seismic stratigraphy and environmental changes

The Columbia Channel (CCS) system is a depositional system located in the South Brazilian Basin, south of the Vitoria-Trindade volcanic chain. It lies in a WNW-ESE direction on the continental rise and abyssal plain, at a depth of between 4200 and 5200 m. It is formed by two depocenters elongated respectively south and north of the channel that show different sediment patterns. The area is swept by a deep western boundary current formed by AABW. The system has been previously interpreted has a mixed turbidite-contourite system. More detailed study of seismic data permits a more precise definition of the modern channel morphology, the system stratigraphy as well as the sedimentary processes and control. The modern CCS presents active erosion and/or transport along the channel. The ancient Oligo-Neogene system overlies a ""upper Cretaceous-Paleogene"" sedimentary substratum (Unit U1) bounded at the top by a major erosive ""late Eocene-early Oligocene"" discordance (D2). This ancient system is subdivided into 2 seismic units (U2 and U3). The thick basal U2 unit constitutes the larger part of the system. It consists of three subunits bounded by unconformities: D3 (""Oligocene-Miocene boundary""), D4 (""late Miocene"") and D5 (""late Pliocene""). The subunits have a fairly tabular geometry in the shallow NW depocenter associated with predominant turbidite deposits. They present a mounded shape in the deep NE depocenter, and are interpreted as forming a contourite drift. South of the channel, the deposits are interpreted as a contourite sheet drift. The surficial U3 unit forms a thin carpet of deposits. The beginning of the channel occurs at the end of U1 and during the formation of D2. Its location seems to have been determined by active faults. The channel has been active throughout the late Oligocene and Neogene and its depth increased continuously as a consequence of erosion of the channel floor and deposit aggradation along its margins. Such a mixed turbidite-contourite system (or fan drift) is characterized by frequent, rapid lateral facies variations and by unconformities that cross the whole system and are associated with increased AABW circulation. (C) 2009 Elsevier B.V. All rights reserved.

A general threshold stress hybrid hazard model for lifetime data

In this paper we propose a hybrid hazard regression model with threshold stress which includes the proportional hazards and the accelerated failure time models as particular cases. To express the behavior of lifetimes the generalized-gamma distribution is assumed and an inverse power law model with a threshold stress is considered. For parameter estimation we develop a sampling-based posterior inference procedure based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumption of vague priors. Further, some discussions on model selection criteria are given. The methodology is illustrated on simulated and real lifetime data set.

INFERENCES FOR THE CHANGE-POINT OF THE EXPONENTIATED WEIBULL HAZARD FUNCTION

In many applications of lifetime data analysis, it is important to perform inferences about the change-point of the hazard function. The change-point could be a maximum for unimodal hazard functions or a minimum for bathtub forms of hazard functions and is usually of great interest in medical or industrial applications. For lifetime distributions where this change-point of the hazard function can be analytically calculated, its maximum likelihood estimator is easily obtained from the invariance properties of the maximum likelihood estimators. From the asymptotical normality of the maximum likelihood estimators, confidence intervals can also be obtained. Considering the exponentiated Weibull distribution for the lifetime data, we have different forms for the hazard function: constant, increasing, unimodal, decreasing or bathtub forms. This model gives great flexibility of fit, but we do not have analytic expressions for the change-point of the hazard function. In this way, we consider the use of Markov Chain Monte Carlo methods to get posterior summaries for the change-point of the hazard function considering the exponentiated Weibull distribution.