Calcium isotope (δCa) fractionation along hydrothermal pathways, Logatchev field (Mid-Atlantic Ridge, 14°45′N)

We investigate the Logatchev Hydrothermal Field at the Mid-Atlantic Ridge, 14°45′N to constrain the calcium isotope hydrothermal flux into the ocean. During the transformation of seawater to a hydrothermal solution, the Ca concentration of pristine seawater ([Ca]_SW) increases from about 10 mM to about 32 mM in the hydrothermal fluid endmember ([Ca]_HydEnd) and thereby adopts a δ^44/40Ca_HydEnd of −0.95 ± 0.07‰ relative to seawater (SW) and a ⁸⁷Sr/⁸⁶Sr isotope ratio of 0.7034(4). We demonstrate that δ^44/40Ca_HydEnd is higher than that of the bedrock at the Logatchev field. From mass balance calculations, we deduce a δ^44/40Ca of −1.17 ± 0.04‰ (SW) for the host-rocks in the reaction zone and −1.45 ± 0.05‰ (SW) for the isotopic composition of the entire hydrothermal cell of the Logatchev field. The values are isotopically lighter than the currently assumed δ^44/40Ca for Bulk Earth of −0.92 ± 0.18‰ (SW) [Skulan J., DePaolo D. J. and Owens T. L. (1997) Biological control of calcium isotopic abundances in the global calcium cycle. Geochim. Cosmochim. Acta61,(12) 2505-2510] and challenge previous assumptions of no Ca isotope fractionation between hydrothermal fluid and the oceanic crust [Zhu P. and Macdougall J. D. (1998) Calcium isotopes in the marine environment and the oceanic calcium cycle. Geochim. Cosmochim. Acta62,(10) 1691-1698; Schmitt A. -D., Chabeaux F. and Stille P. (2003) The calcium riverine and hydrothermal isotopic fluxes and the oceanic calcium mass balance. Earth Planet. Sci. Lett. 6731, 1-16]. Here we propose that Ca isotope fractionation along the fluid flow pathway of the Logatchev field occurs during the precipitation of anhydrite. Two anhydrite samples from the Logatchev Hydrothermal Field show an average fractionation of about Δ^44/40Ca = −0.5‰ relative to their assumed parental solutions. Ca isotope ratios in aragonites from carbonate veins from ODP drill cores indicate aragonite precipitation directly from seawater at low temperatures with an average δ^44/40Ca of −1.54 ± 0.08‰ (SW). The relatively large fractionation between the aragonite precipitates and seawater in combination with their frequent abundance in weathered mafic and ultramafic rocks suggest a reconsideration of the marine Ca isotope budget, in particular with regard to ocean crust alteration.     

Sedimentologic attributes of the Proterozoic siliciclastic packages of the Garhwal–Kumaun Lesser Himalaya,India: Implication for their relationship and palaeobasinal conditions

The present work addresses the long-standing issues on the characterization aspect of the Proterozoic siliciclastic successions exposed in the central part of the Lesser Himalaya, restricted between the Main Boundary Thrust (MBT) and the Main Central Thrust (MCT). Geologic, sedimentologic, and petrographic study divides the Lesser Himalaya in two zones- northern Palaeo- Mesoproterozoic Inner Lesser Himalayan (ILH) and southern Neoproterozoic Outer Lesser Himalayan (OLH) zones. The major lithofacies recognized from the zones are - (i) coarse grained siliciclastic (CGS), (ii) interbedded medium and fine-grained siliciclastic (IMFS), (iii) argillite (ARG), and (iv) siliciclastic–argillite rhythmites (SAR). Amongst all these facies, the nearshore IMFS facies shows consistent presence in both OLH and ILH zones. From the facies distribution pattern, a northwest–southeasterly trending palaeo- shoreline has been envisaged. The CGS facies in the ILH hints towards an alluvial fan setting during 1.8 Ga rifting phase associated with penecontemporaneous basic magmatism. Compositionally, the siliciclastics of both the zones (ILH and OLH) are arenite and wacke types with minimal variation in their detrital proportions, derived from the early Proterozoic (between 2.4-1.6Ga) Aravalli-Delhi Supergroup provenance. Nearly matching types and content of detrital modes and the lithofacies pattern of the ILH and OLH siliciclastics probably conclude the derivation from the rising (nearby) Aravalli-Delhi orogen and deposition in a foreland like situation.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.     

Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.     

A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

Uniaxial Compressive Strength (UCS) is considered as one of the most important parameters in designing rock structures. Determination of this parameter requires preparation of rock samples which is costly and time consuming. Moreover discrepancy of laboratory test results is often observed. To overcome the drawbacks of traditional method of UCS measurement, in this paper, predictive models based on neuro-genetic approach and multivariable regression analysis have been developed for predicting compressive strength of different type of rocks. Coefficient of determinatoin (R2) and the Mean Square Error (MSE) were calculated for comparison of the models’ efficiency. It was observed that accuracy of the neuro-genetic model is significantly better than regression model. For the neuro-genetic and regression models, R2 and MSE were equal to 95.89 % and 0.0045 and 77.4 % and 1.61, respectively. According to sensitivity analysis for neuro-genetic model, Schmidt rebound number is the most effective parameter in predicting UCS.     

Synthesis of biochar from sugarcane filter-cake and its impacts on physiological performance of lettuce (<Emphasis Type="Italic">Lettuce sativa</Emphasis>) grown on cadmium contaminated soil

Soil contamination with cadmium has become major concern all over the world because of its adverse impacts on ecosystem health and agricultural land. Soil amendment with biochar may have varied effects on physical and chemical properties of soil. The objective of the study was to explore the impact of sugarcane filter-cake biochar on physiological performance and growth of lettuce in an aged soil. Four different doses (0, 1.5%, 3%, and 5%) of biochar were used in the soil and conditioned for 1 month. After this, lettuce seedlings were grown in the soil. The results showed that the biochar treatment improved the fresh and dry biomass of leaves and roots as well as plant height while diminished the bioavailability of cadmium from the soil. As compared to control, biochar significantly enhanced the chlorophyll content in lettuce leaves. Due to the biochar amendment, the oxidative stress decreased in lettuce shoots over the control. As compared to control, concentration of cadmium in lettuce significantly decreased after the application of biochar. It was concluded that biochar could mitigate the toxicity of cadmium in lettuce by altering the biochemical and physiological processes in cadmium contaminated soil.     

Detrital zircon provenance of Neoproterozoic to Cenozoic deposits in Iran: Implications for chronostratigraphy and collisional tectonics

Ion-microprobe U–Pb analyses of 589 detrital zircon grains from 14 sandstones of the Alborz mountains, Zagros mountains, and central Iranian plateau provide an initial framework for understanding the Neoproterozoic to Cenozoic provenance history of Iran. The results place improved chronological constraints on the age of earliest sediment accumulation during Neoproterozoic–Cambrian time, the timing of the Mesozoic Iran–Eurasia collision and Cenozoic Arabia–Eurasia collision, and the contribution of various sediment sources of Gondwanan and Eurasian affinity during opening and closure of the Paleotethys and Neotethys oceans. The zircon age populations suggest that deposition of the extensive ~ 1 km-thick clastic sequence at the base of the cover succession commenced in latest Neoproterozoic and terminated by Middle Cambrian time. Comparison of the geochronological data with detrital zircon ages for northern Gondwana reveals that sediment principally derived from the East African orogen covered a vast region encompassing northern Africa and the Middle East. Although most previous studies propose a simple passive-margin setting for Paleozoic Iran, detrital zircon age spectra indicate Late Devonian–Early Permian and Cambrian–Ordovician magmatism. These data suggest that Iran was affiliated with Eurasian magmatic arcs or that rift-related magmatic activity during opening of Paleotethys and Neotethys was more pronounced than thought along the northern Gondwanan passive-margin. For a Triassic–Jurassic clastic overlap assemblage (Shemshak Formation) in the Alborz mountains, U–Pb zircon ages provide chronostratigraphic age control requiring collision of Iran with Eurasia by late Carnian–early Norian time (220–210 Ma). Finally, Cenozoic strata yield abundant zircons of Eocene age, consistent with derivation from arc magmatic rocks related to late-stage subduction and/or breakoff of the Neotethys slab. Together with the timing of foreland basin sedimentation in the Zagros, these detrital zircon ages help bracket the onset of the Arabia–Eurasia collision in Iran between middle Eocene and late Oligocene time.     

Intra-survey reservoir fluctuations – implications for quantitative 4D seismic analysis

During the time taken for seismic data to be acquired, reservoir pressure may fluctuate as a consequence of field production and operational procedures and fluid fronts may move significantly. These variations prevent accurate quantitative measurement of the reservoir change using 4D seismic data. Modelling studies on the Norne field simulation model using acquisition data from ocean-bottom seismometer and towed streamer systems indicate that the pre-stack intra-survey reservoir fluctuations are important and cannot be neglected. Similarly, the time-lapse seismic image in the post-stack domain does not represent a difference between two states of the reservoir at a unique base and monitor time, but is a mixed version of reality that depends on the sequence and timing of seismic shooting. The outcome is a lack of accuracy in the measurement of reservoir changes using the resulting processed and stacked 4D seismic data. Even for perfect spatial repeatability between surveys, a spatially variant noise floor is still anticipated to remain. For our particular North Sea acquisition data, we find that towed streamer data are more affected than the ocean-bottom seismometer data. We think that this may be typical for towed streamers due to their restricted aperture compared to ocean-bottom seismometer acquisitions, even for a favourable time sequence of shooting and spatial repeatability. Importantly, the pressure signals on the near and far offset stacks commonly used in quantitative 4D seismic inversion are found to be inconsistent due to the acquisition timestamp. Saturation changes at the boundaries of fluid fronts appear to show a similar inconsistency across sub-stacks. We recommend that 4D data are shot in a consistent manner to optimize aerial time coverage, and that additionally, the timestamp of the acquisition should be used to optimize pre-stack quantitative reservoir analysis.