Experimental studies to constrain parental magma of Malangtoli volcanics from Singhbhum craton of the eastern Indian shield

Malangtoli volcanics of the Singhbhum craton of the eastern Indian shield is one of the important Proterozoic lava suites. Experimental studies on 1 atmosphere pressure constrain the parental magma type and temperature range of crystallization of the parent magma (deduced to be in the range of 1500°C to 1200°C). The experimental studies show that at 1500°C, plagioclase is the first phase to crystallize, followed by few opaques which join along with plagioclase at 1450°C. At subsequent lower temperature (1400°C-1300°C), plagioclase and opaque continue to crystallize. At 1250°C plagioclase and opaque still persist while pyroxene appears first and liquid (glass) still remains. Appearance of opaque minerals (magnetite and illmenite) at both ~1400°C and ~1300°C indicate oscillation of oxygen fugacity in the parent magma, petrographically documented by coarser phenocrysts as well as finer or peripheral tiny grains. Use of tectonic discrimination diagrams (based on discrimination factors F₁-F₂ and FeO^t/MgO vs. TiO₂) shows an island arc tholeiitic affinity for Malangtoli volcanic, suggests that the role of proto-plate convergence in Singhbhum architecture played an important role to build up Malangtoli volcanics during Proterozoic.     

Local site effect of a clay site in Shiraz based on seismic hazard of Shiraz Plain

Propagation of seismic waves through soil layers would drastically change the frequency content and amplitude-based features of ground motions at the surface. These alterations are known as seismic site effects. Computation of site effects of high-populated areas such as large cities is of great importance (e.g., it is used in development of seismic microzonation of a region). Shiraz is one of the most populous cities of Iran and is located in a high seismic hazardous region. A representative clay site in this city is selected to assess local site effects. The time series and random vibration theory procedure in the frequency domain are implemented to analyze the aforementioned site. Furthermore, the nonlinear dynamic soil behavior is simulated by the equivalent linear method and the nonlinear method via DEEPSOIL program. Three types of soil column uncertainties such as shear wave velocity, modulus reduction, and damping ratio of soil layers as well as depth of underlying rock half-space (D _bed) are considered herein. The mean amplification and standard deviation of natural logarithm of amplification factors are computed for a variety of analysis types. The results of the current study show that the computed mean and standard deviation of amplification factor in ln units by considering only V _S uncertainty are in good agreement with the corresponding ones by considering V _S and modulus reduction and damping ratio variabilities simultaneously for the studied site. Furthermore, it seems that the effect of bedrock depth in definition of spectral shapes of the Iranian seismic building code should be taken into account.     

Flood frequency analysis based on simulated peak discharges

Flood frequency approaches vary from statistical methods, directly applied on the observed annual maximum flood series, to adopting rainfall–runoff simulation models that transform design rainfalls to flood discharges. Reliance on statistical flood frequency analysis depends on several factors such as the selected probability distribution function, estimation of the function parameters, possible outliers, and length of the observed flood series. Through adopting the simulation approach in this paper, watershed-average rainfalls of various occurrence probabilities were transformed into the corresponding peak discharges using a calibrated hydrological model. A Monte Carlo scheme was employed to consider the uncertainties involved in rainfall spatial patterns and antecedent soil moisture condition (AMC). For any given rainfall depth, realizations of rainfall spatial distribution and AMC conditions were entered as inputs to the model. Then, floods of different return periods were simulated by transforming rainfall to runoff. The approach was applied to Tangrah watershed in northeastern Iran. It was deduced that the spatial rainfall distribution and the AMCs exerted a varying influence on the peak discharge of different return periods. Comparing the results of the simulation approach with those of the statistical frequency analysis revealed that, for a given return period, flood quantiles based on the observed series were greater than the corresponding simulated discharges. It is also worthy to note that existence of outliers and the selection of the statistical distribution function has a major effect in increasing the differences between the results of the two approaches.     

Paleoenvironmental reconstruction of the upper Cretaceous succession (Abtalkh Formation) of the Kopeh-Dagh Basin,northeastern Iran based on foraminiferal and palynological analyses

Arabian Journal of Geosciences - In order to reconstruct sea surface water productivity and sea floor oxygenation during late Campanian-Maastrichtian, planktonic and benthic foraminiferal...     

Lake Neor reveals how mountain vegetation responded to 7000 years of hydroclimate variability in northwestern Iran

Palynological and geochemical analyses provide valuable information about modern and past climatic regimes and vegetation. The impact of climate and humans on past vegetation in the semi-arid areas of northwestern Iran has received increased interest in the wake of warming temperatures in the Middle East. Palynological and down-core XRF elemental abundances from a peat core from Lake Neor enabled a reconstruction of vegetational changes of the past 7000 years over the highlands of northwestern Iran. Periods of increased arboreal pollen (AP) types and high (Artemisia + Poaceae)/Chenopodiaceae ratios along with low titanium abundances, high percentages of total organic carbon, more negative δD values, and higher carbon accumulation rates suggest a relatively wet climate. These conditions have persisted during the periods 6700–6200, 5200–4450 and 3200–2200 cal a bp. The overall low AP values, substantial rise of Chenopodiaceae, high Ti abundances and low values of palaeo-redox proxies, are all evidences of a drier climate, as has been reconstructed for the periods 6200–5200 and 4030–3150 cal a bp and the last 2200 years. An important feature of the last centuries is the increase of anthropogenic and pastoral indicator pollen types. Our results may provide basic data to predict future trends in vegetation dynamics under future climate change in western Asia.     

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.     

Simultaneous estimation of shape factor and depth of subsurface cavities from residual gravity anomalies using feed-forward back-propagation neural networks

We develop a new method of using feed-forward back-propagation (FFBP) neural networks to simultaneously estimate shape factor and depth of gravity anomalies. The advantages compared to neural network methods are the following: no pre-assumptions are made on source shape, the FFBP neural network estimates both depth and shape factor of source bodies and, once trained, works well for any new data in the training space, without repeating the initial calculations.     

Estimation of groundwater storage from seismic data using deep learning

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic–elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.     

An alternative direct method towards mean dynamic topography computations

An alternative “direct method” to “mean dynamic topography” (MDT) computations using satellite altimetry-derived “mean sea surface” (MSS) and “global geopotential model” (GGM), without direct application of the geoid, is devised. The developed approach, which is based on derivation of an equipotential surface of the gravity field of the Earth that fits to global MSS in least squares sense, is formulated via a constrained optimization problem. The validity of our method is numerically tested by computing a global MDT model based on DNSC08 MSS model and EGM2008 GGM as input data.     

Strong ground motion from the November 12, 2017, <Emphasis Type="Bold">M</Emphasis> 7.3 Kermanshah earthquake in western Iran

In this paper, we analyzed the strong ground motion from the November 12, 2017, Kermanshah earthquake in western Iran with moment magnitude (M) of 7.3. Nonlinear and linear amplification of ground motion amplitudes were observed at stations with soft soil condition at hypocentral distances below and above 100 km, respectively. Observation of large ground motion amplitudes dominated with long-period pulses on the strike-normal component of the velocity time series suggests a right-lateral component of movement and propagation of rupture towards southeast. Comparison of the horizontal peak ground acceleration (PGA) from the M 7.3 earthquake with global PGA values showed a similar decay in ground motion amplitudes, although it seems that PGA from the M 7.3 Kermanshah earthquake is higher than global values for NEHRP site class B. We also found that the bracketed duration (D_b) was higher in the velocity domain than in the acceleration domain for the same modified Mercalli intensity (MMI) threshold. For example, D_b reached ~?30 s at the maximum PGA while it was ~?50 s at the maximum peak ground velocity above the threshold of MMI?=?5. Although the standard design spectrum from Iranian Code of Practice for Seismic Resistant Design of Buildings (standard No. 2800) seems to include appropriate values for the design of structures with fundamental period of 1 s and higher, it is underestimated for near-field ground motions at lower periods.