Authigenic iron and titanium oxides in triassic red beds: (St. Bees Sandstone), Cumbria,Northern England

Authigenic iron and titanium oxides occur in three main textural varieties in the St. Bees Sandstone, a Triassic red bed succession in Cumbria. These are: (a) overgrowths of haematite, titaniferous haematite, anatase, and rutile which generally occur on detrital host grains of similar compositions. The overgrowths may occur as syntaxial rim cements or rhombohedral and prismatic projections and are always in optical continuity, (b) discrete crystals of anatase (including octahedrite) and haematite 10-40 μm in size occur in pore spaces, and (c) fine grained ( < 1 μm) pigmentary haematite occurs as grain coatings around detrital grains and in the interstitial matrix. The euhedral nature and pore-filling habit clearly indicate the authigenic origin of these iron- and titanium oxides. All three textural varieties are believed to have been precipitated from ground waters containing dissolved ferrous and titanium ions. These ions were derived from the intrastratal solution of detrital iron and titanium bearing grains including ferromagnesian silicates and iron-titanium oxides. The precipitation of iron oxides from migrating ground waters in the manner described here goes some way to explaining how thick, uniformly red successions can be formed.     

Revised identifications in the 5C2 survey

In the present paper revised distances of optical objects from radio sources of the 5C2 survey are given. A statistical investigation of the data is given for blue objects and galaxies by the statistical method of the “first neighbour”. The identification rate on blue plates for both the blue objects and galaxies amounts to about 40% out of the total number of 26 identifications. For every blue object and galaxy which are proposed as an identification the statistical reliability is given.     

Possible Interpretations of B2 and 5C Quasar Candidate Counts and the “Cut off” in Quasar Density

Optical quasar candidate counts in the far reaching radio surveys B₂ and 5C are consistent either with a luminosity function containing a high percentage of low luminosity objects and a cut off in quasar density or, more probably, with a normal number of quasars at high redshifts and a less steep luminosity function. The absence of high redshifted objects in currently available samples is to be expected of q₀ ≈︂ o and if some of the few quasars observed at z>2.2 are exceptionally bright intrinsically and not typical for the bulk.     

Measures of Parameter Uncertainty in Geostatistical Estimation and Geostatistical Optimal Design

Studies of site exploration, data assimilation, or geostatistical inversion measure parameter uncertainty in order to assess the optimality of a suggested scheme. This study reviews and discusses measures for parameter uncertainty in spatial estimation. Most measures originate from alphabetic criteria in optimal design and were transferred to geostatistical estimation. Further rather intuitive measures can be found in the geostatistical literature, and some new measures will be suggested in this study. It is shown how these measures relate to the optimality alphabet and to relative entropy. Issues of physical and statistical significance are addressed whenever they arise. Computational feasibility and efficient ways to evaluate the above measures are discussed in this paper, and an illustrative synthetic case study is provided. A major conclusion is that the mean estimation variance and the averaged conditional integral scale are a powerful duo for characterizing conditional parameter uncertainty, with direct correspondence to the well-understood optimality alphabet. This study is based on cokriging generalized to uncertain mean and trends because it is the most general representative of linear spatial estimation within the Bayesian framework. Generalization to kriging and quasi-linear schemes is straightforward. Options for application to non-Gaussian and non-linear problems are discussed.     

Formation Mechanism of Mud Bank Along the Southwest Coast of India

Mud bank formation during the southwest monsoon along the southwest coast of India remains an enigma to the researchers and coastal community in spite of several earlier studies. The present study attempts to unravel the mystery through a high-frequency, season-long time-series observation at Alappuzha, located at the southern part of the west coast of India, a region of frequent occurrence of mud bank. Using 7-month-long weekly time-series observation, we identified strong winds and high waves associated with onset of the southwest monsoon and subsequent three episodic atmospheric low-pressure events (LPEs).With the help of in situ time-series data, we show that the strong winds and high waves associated with southwest monsoon pre-conditions the near shore bottom sediment to bring it into suspension. The high amplitude waves associated with the southwest monsoon, while propagating from the deep water to shallow water region, interact with the bottom initiating bottom-sediment movement and its suspension due to wave refraction and shoaling. The sporadic occurrence of the atmospheric LPEs enhances the process of suspension of bottom sediment in the near shore region leading to the formation of fluid mud. Simulations with a cohesive sediment transport model yielded realistic estimates of sediment transport, in the presence of an onshore current, a pre-requisite for transporting the fluid mud toward the coast. The prevailing onshore upwelling current during the southwest monsoon provides the favorable pre-requisite conditions for transporting the fluid mud through depression channel network towards the coast. Once sufficient quantity and thickness of fluid mud is accumulated in the near shore region, it acts as a wave damper for subsequent high monsoon waves, as indicated by the time-series wave data, leading to the formation of tranquil mud bank region. Depression channel networks extending from the shelf to the coast off Alappuzha, Kochi, Ponnani, Beypore, and Ullal were found in the bathymetric charts, thus explaining why mud banks occur only at few locations in spite of the prevalence of similar monsoon conditions.