印度板块和亚洲大陆在何时何地碰撞

印度板块和亚洲大陆的初始碰撞时间是所有相关的喜马拉雅-西藏造山体系演化模式的主控条件,并严重影响到对众多与青藏高原隆升和东亚大陆挤出相关的地质过程速率的解释,以及对新生代全球气候变化的理解。尽管印度板块和亚洲大陆汇聚的速率在55Ma突然减缓被广泛地认为是初始碰撞的标志,但这次碰撞所造成的主要构造效应直到20多个百万年以后才显现出来。对印度板块和亚洲大陆相对位置的重新估算,表明它们在55Ma时并没有达到可以彼此发生碰撞的距离。基于来自西藏新的野外证据和对已有数据的重新评估,认为初始碰撞发生在始新世—渐新世之交（约34Ma）,并对55Ma时发生的地质事件提出了另一种解释     

The statistical analysis of geochemical compositions

The analysis and interpretation of compositional data, such as major oxide compositions of rocks, has been traditionally plagued by the so-called constant-sum or closure problem. Particular difficulties have been the lack of a satisfactory, interpretable covariance structure and of rich, tractable, parametric classes of distributions on the simplex sample space. Consideration of logistic and logratio transformations between the simplex and Euclidan space has allowed the introduction of new concepts of covariance structure and of classes of logistic-normal distributions which have now opened up a substantial and meaningful array of statistical methodology for compositional data. From the motivation of a wide variety of practical geological problems we examine the range of possibilities with this new approach to the constant-sum problem.     

OSL dating of paleoshorelines at Lagkor Tso, western Tibet

Lagkor Tso, a saline lake located south of Gertse in western Tibet exhibits spectacular flights of paleoshorelines. Optically stimulated luminescence (OSL) dating on quartz using the single-aliquot regenerative-dose (SAR) protocol from five paleoshoreline deposits shows that the lake level was 130 m higher than the present lake surface 5.2 ka ago. The lake level dropped rapidly by 25 m between 5.2 ka and 3.7 ka ago. Lake shrinkage further accelerated between 3.7 ka and 3.2 ka ago, when the lake level was just 74 m above the present lake surface. Luminescence characteristics and problematic samples are discussed.     

Reducing the dimensionality of compositional data sets

The high-dimensionality of many compositional data sets has caused geologists to look for insights into the observed patterns of variability through two dimension-reducing procedures: (i)the selection of a few subcompositions for particular study, and (ii)principal component analysis. After a brief critical review of the unsatisfactory state of current statistical methodology for these two procedures, this paper takes as a starting point for the resolution of persisting difficulties a recent approach to principal component analysis through a new definition of the covariance structure of a composition. This approach is first applied for expository purposes to a small illustrative compositional data set and then to a number of larger published geochemical data sets. The new approach then leads naturally to a method of measuring the extent to which a subcomposition retains the pattern of variability of the whole composition and so provides a criterion for the selection of suitable subcompositions. Such a selection process is illustrated by application to geochemical data sets.     

Relative variation diagrams for describing patterns of compositional variability

In recognizing that a composition, such as a major oxide or sediment composition, provides information only about the relative, not the absolute, magnitudes of its components, this paper exposes the compositional variation array as the simplest and minimum way of summarizing the pattern of variability within a compositional data set. Such summaries are free of the notorious hazards of the constant-sum constraint and when depicted in relative variation diagrams can often provide substantial insights into the nature of the compositional variability. Concepts and practice are illustrated by reference to a number of real data sets.