Paleogeographic regionalization of Triassic seas based on conodontophorids

Geographic differentiation of conodontophorids between northern and southern latitudes commenced in the Triassic since the early Induan. Cosmopolitan long-lived genera of predominantly smooth morphotypes without sculpturing were characteristic of high-latitude basins of the Panboreal Superrealm. Since the early Olenekian until the Carnian inclusive, this superrealm consisted of the Siberian Realm that extended over Northeast Asia and the Canada-Svalbard Realm that included the Svalbard Archipelago and northern regions of Canada. Throughout the Triassic period, conodontophorids characteristic of the Tethys-Panthalassa Superrealm spanning the Tethys and low-latitude zones of the Pacific were highly endemic, very diverse in taxonomic aspect, having well-developed sculpturing and tempos of morphological transformations. Distinctions between the Early-Middle Triassic conodontophorids from northern and southern zones were not as great as afterward, and their impoverished assemblages from southern Tethyan basins were close in some respects to the Boreal ones. Their habitat basins of that time can be grouped into the Mediterranean-Pacific and India-Pakistan realms. Hence, the extent of geographic differentiation of conodontophorids was not constant and gradually grew, as their taxonomic diversity was reducing in northern basins but relatively increasing in southern ones. The Panboreal e Tethys-Panthalassa superrealms of conodontophorids, which are most clearly recognizable, are close to first-rank paleobiochores (superrealms) established earlier for ammonoids and bivalve mollusks. Main factor that controlled geographic differentiation of Triassic conodontophorids was climatic zoning. Initially lower diversity of southern Tethyan assemblages points probably to relatively cooler water regime in the peri-Gondwanan part of the Tethys. The established patterns in geographic distribution of conodontophorids characterize most likely the real trend of their differentiation and evolution, i.e., the distribution area contraction prior to complete extinction at the end of the Triassic     

Geochemistry and mineralogy of arsenic in (natural) anaerobic groundwaters

Here new data from field bioremediation experiments and geochemical modeling are reported to illustrate the principal geochemical behavior of As in anaerobic groundwaters. In the field bioremediation experiments, groundwater in Holocene alluvial aquifers in Bangladesh was amended with labile water-soluble organic C (molasses) and MgSO₄ to stimulate metabolism of indigenous SO₄-reducing bacteria (SRB). In the USA, the groundwater was contaminated by Zn, Cd and SO₄, and contained <10 μg/L As under oxidized conditions, and a mixture of sucrose and methanol were injected to stimulate SRB metabolism. In Bangladesh, groundwater was under moderately reducing conditions and contained ∼10 mg/L Fe and ∼100 μg/L As. In the USA experiment, groundwater rapidly became anaerobic, and dissolved Fe and As increased dramatically (As > 1000 μg/L) under geochemical conditions consistent with bacterial Fe-reducing conditions. With time, groundwater became more reducing and biogenic SO₄ reduction began, and Cd and Zn were virtually completely removed due to precipitation of sphalerite (ZnS) and other metal sulfide mineral(s). Following precipitation of chalcophile elements Zn and Cd, the concentrations of Fe and As both began to decrease in groundwater, presumably due to formation of As-bearing FeS/FeS₂. By the end of the six-month experiment, dissolved As had returned to below background levels. In the initial Bangladesh experiment, As decreased to virtually zero once biogenic SO₄ reduction commenced but increased to pre-experiment level once SO₄ reduction ended. In the ongoing experiment, both SO₄ and Fe(II) were amended to groundwater to evaluate if FeS/FeS₂ formation causes longer-lived As removal. Because As-bearing pyrite is the common product of SRB metabolism in Holocene alluvial aquifers in both the USA and Southeast Asia, it was endeavored to derive thermodynamic data for arsenian pyrite to better predict geochemical processes in naturally reducing groundwaters. Including the new data for arsenian pyrite into Geochemist’s Workbench, its stability field completely dominates in reducing Eh–pH space and “displaces” other As-sulfides (orpiment, realgar) that have been implied to be important in previous modeling exercises and reported in rare field conditions.     

A neutron diffraction study of alkali cation migration in montmorillonites

Neutron powder diffraction measurements on lithium and cesium saturated montmorillonite samples before and after heat treatment at 300°C are studied, in order to undertake a complete refinement of crystal structure and unravel the migration mechanism for the interlayer cations of Li or Cs. Rietveld analysis of the corresponding diffraction patterns finds that montmorillonite crystallizes in the C2/m space group with unit cell dimensions consistent with the size of the specific interlayer cation. We show that thermal treatment affects the two types of samples in a different way. This is with respect to their unit cell dimensions and the migration of Li from the 2b to the 2c clay lattice site, in constrast to the Cs positioning which remains effectively unchanged.     

Comparative analyses of East Texas forest cover maps generated from Landsat and AVHRR data

Comparing satellite data derived map products are affected by differences in data characteristics, image acquisition dates, processing techniques, and classification schemes used for assigning pixels to a thematic class. By comparing two forest maps generated from Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Very High Resolution Radiometer (AVHRR) images acquired on the same day, and processed using identical classification scheme and methods these differences were minimized. The ETM+ derived map had higher classification accuracy values and more precise area estimates than the AVHRR derived map. In the ETM+ derived map, 87 of the 599 verification data were misclassified, whereas in the AVHRR derived map, 155 of the 469 verification data were misclassified. Detailed error analyses by land cover class revealed that a land use based definition of forest accounted for 74% (64 out of 87) and 57% (89 out of 155) of the classification errors in ETM+ and AVHRR derived maps, respectively.     

Ground motion estimation during the Kashmir earthquake of 8th October 2005

In this article, analytical methods have been used to estimate ground motion during the 8 October 2005, Kashmir earthquake. Peak ground acceleration (PGA) values at several stations in the epicentral region have been estimated by empirical analytical source mechanism models. As an alternate analysis, PGA estimates have also been obtained using the stochastic finite fault seismological model. The estimated PGAs are compared with that obtained from damage values. A PGA contour map in the near-source region is provided. It is found that very near to the epicenter, PGA would have reached more than 1 g. It is demonstrated that empirical analytical models can be effectively used to estimate ground motion due to rupture of active faults.     

Platinum group minerals in podiform chromitites of the Bou Azzer ophiolite, Anti Atlas, Central Morocco

Summary The Neoproterozoic Bou Azzer ophiolite complex hosts numerous, small lenticular bodies of massive and disseminated chromite. Metallurgical-grade high-Mg and high-Cr spinels (cores with 48–62 wt% Cr₂O₃) reveal complex alteration patterns of successive Cr and Mn enrichment and loss of Al towards the rims, while the Mg# ratios [(Mg/(Mg + Fe²⁺)] remain almost constant. Concentration patterns of platinum-group elements are typical for ophiolitic chromitite poor in sulfides, with predominance of the IPGE, variable Rh, and low Pt and Pd. The most abundant platinum-group mineral is Rh-bearing laurite that occurs either included in spinel or in silicate matrix, whereas Os-Ir-Ru alloy is always included in spinel. Laurite inclusions reveal complex intergrowth textures with Rh-Ru-Pt rich alloy, and with Rh-rich sulfide. Most laurites display trends to sulfur-poor compositions leading to local formation of very fine-grained Ru-Os-Ir alloy phases. Ni-Co-Fe sulfides, arsenides and sulfarsenides devoid of PGE are associated with the alteration of chromite. Textural position and chemical composition of the base metal inclusions, as well as comparison of alteration features between chromite and accessory chromian spinel in the Co-Ni-As ores of the Bou Azzer ophiolite indicate a close connection. It is suggested that hydrothermal fluids percolated through the marginal zones of the ophiolite belt during greenschist facies metamorphism and deposited Ni-Co-Fe arsenides, sulfarsenides and minor sulfides as accessories within altered chromitites, and also in structurally favourable zones as Ni-Co-As ores. Author’s address: Dr. Frank Melcher, Federal Institute for Geosciences and Natural Resources, Stilleweg 2, 30655 Hannover, Germany     

Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey)

In the international literature, although considerable amount of publications on the landslide susceptibility mapping exist, geomorphology as a conditioning factor is still used in limited number of studies. Considering this factor, the purpose of this article paper is to implement the geomorphologic parameters derived by reconstructed topography in landslide susceptibility mapping. According to the method employed in this study, terrain is generalized by the contours passed through the convex slopes of the valleys that were formed by fluvial erosion. Therefore, slope conditions before landsliding can be obtained. The reconstructed morphometric and geomorphologic units are taken into account as a conditioning parameter when assessing landslide susceptibility. Two different data, one of which is obtained from the reconstructed DEM, have been employed to produce two landslide susceptibility maps. The binary logistic regression is used to develop landslide susceptibility maps for the Melen Gorge in the Northwestern part of Turkey. Due to the high correct classification percentages and spatial effectiveness of the maps, the landslide susceptibility map comprised the reconstructed morphometric parameters exhibits a better performance than the other. Five different datasets are selected randomly to apply proper sampling strategy for training. As a consequence of the analyses, the most proper outcomes are obtained from the dataset of the reconstructed topographical parameters and geomorphologic units, and lithological variables that are implemented together. Correct classification percentage and root mean square error (RMSE) values of the validation dataset are calculated as 86.28% and 0.35, respectively. Prediction capacity of the different datasets reveal that the landslide susceptibility map obtained from the reconstructed parameters has a higher prediction capacity than the other. Moreover, the landslide susceptibility map obtained from the reconstructed parameters produces logical results.     

Nonlinear Analysis of Local Site Effects on Seismic Ground Response in the Bam Earthquake

The influence of local geologic and soil conditions on the intensity of ground shaking is addressed in this study. The amplification of the ground motion due to local site effects resulted in severe damage to dwellings in the Bam area during the 2003 Bam Earthquake. A unique set of strong motion acceleration recordings was obtained at the Bam accelerograph station. Although the highest peak ground acceleration recorded was the vertical component (nearly 1 g), the longitudinal component (fault-parallel motion) clearly had the largest maximum velocity as well as maximum ground displacement. Subsurface geotechnical and geophysical (down-hole) data in two different sites have been obtained and used to estimate the local site condition on earthquake ground motion in the area. The ground response analyses have been conducted considering the nonlinear behavior of the soil deposits using both equivalent linear and nonlinear approaches. The fully nonlinear method embodied in FLAC was used to evaluate the nonlinear soil properties on earthquake wave propagation through the soil layer, and compare with the response from the equivalent linear approach. It is shown that thick alluvium deposits amplified the ground motion and resulted in significant damage in residential buildings in the earthquake stricken region. The comparison of results indicated similar response spectra of the motions for both equivalent and nonlinear analyses, showing peaks in the period range of 0.3–1.5 s. However, the amplification levels of nonlinear analysis were less than the equivalent linear method especially in long periods. The observed response spectra are shown to be above the NEHRP building code design requirements, especially at high frequencies.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.