the oligocene-miocene granitic rocks of deh siahan, part of central iranian volcanic belt, are intruded into eocene volcano-sedimentary complex where their contact is marked by albite-epidote hornblende hornfels facies and granitic apophyses. the granitic rocks show enhanced lil element abundances and low hfs/lil ratios. geochemical data, various trace element discriminant diagrams, enhanced y/...

Introduction: Over the past several years, we have presented new data and proposed a cold-based glacial hypothesis for the Tharsis Montes fan-shaped deposits [14], which were first identified and analyzed using Viking data [5-8]. Based on stratigraphic relationships with young lava flows in Tharsis, we know that the deposits are of midto late-Amazonian age [8,9], but we have little information ...

This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then...

Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgement of where and by whom the paper was presented. Abstract This paper proposes an annealing-type algorithm for generating multiple realizations of facies distributions that: (i) honor the facies data at well locations, (ii) match global...

Jiama is a super-large porphyry copper–polymetallic deposit located in the Gangdese metallogenic belt of southern Tibet. The consists combination polymetallic skarn, Cu–Mo mineralization at contact between Porphyry and hornfels, distal Au fault. current metal reserves are 7.4 Mt Cu, 0.6 Mo, 1.8 Pb–Zn, 6.65 Moz Au, 360.32 Ag, with skarn to porphyry–hornfels host-rock ratio ~3:1. Based on detaile...

Physical properties of sediments are commonly used to define subsurface lithofacies and these same physical properties influence subsurface microbial communities. This suggests an (unexploited) opportunity to use the spatial distribution of facies to predict spatial variation in biogeochemically relevant microbial attributes. Here, we characterize three biogeochemical facies-oxidized, reduced, ...

We have done a geostatistical inversion of seismic data to facies probabilities. As a first step, we invert the seismic data for elastic parameters using the Bayesian AVA inversion method of Buland et. al. (2003). Next, we use an analysis of the uncertainty in the posterior distribution to filter the elastic parameters given in well logs. By comparing these filtered well logs with facies logs, ...