Using airborne geophysical data in identifying tectonic lineaments in east of Iran

In this paper we tried to identify the main tectonic lineaments in Eastern Iran including Lut block and Sistan suture zone from the airborne geomagnetic data together with tilt filter. As the map of obtained lineaments from airborne geomagnetic data has been studied, four distinct set of lineaments has been identified: (i) north–south, (ii) east–west, (iii) northeast–southwest, and (iv) northwest–southwest that are concurrently with structural zones and area’s big faults. New faults which have been identified in this investigation are lineaments with trend northeast–southwest and east–west. The depth of these lineaments has been calculated through Euler modeling. Magnetic lineaments trending east–west have the most depth, so these lineaments are related to basement faults.     

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.     

Mélange development in the Neyriz region of Zagros Orogen,Iran: Record of convergence and collision in the Neotethyan Realm

Mélanges are formed by sedimentary, tectonic and diapiric processes and are generally found in collisional belts. The Zagros Orogeny provides an intriguing geological laboratory for the study of mélange-forming processes during the progressive tectonic evolution of the Neotethys Ocean. Different types of tectonic and sedimentary mélanges occur in specific structural positions within the Zagros orogenic belt in the Neyriz Region (Iran). Based on their block-in-matrix fabrics, and tectonostratigraphic positions, we differentiated 14 different mélange types, which mark different episodes of the tectonic evolution of the Neyriz Region from the Cretaceous subduction to the Miocene collision. The Cretaceous subduction stage is recorded by volcanic-sedimentary mélanges (Mv). Sedimentary mélanges characterized by megabreccia from the Cretaceous limestone (Ms1) and Eocene polymictic megabreccia (Ms2) represent epi-nappe mélanges formed during the Palaeocene–Eocene in wedge-top basins. The ophiolite emplacement in the Oligocene resulted in local extensional tectonics in the upper part of the ophiolitic nappe, and deposition of a polymictic megabreccia (Ms3, Ms4). As the final production of the Neotethys Ocean closure and the Eurasian-Arabian collision, the sedimentary mélanges characterized by different types of chaotic rock units (Ms5, Ms6, Ms7 and Ms8 facies) were developed in front of the Cretaceous–Eocene nappes due to growth of the orogenic wedge in the Miocene. Our findings indicate that the recognition and distinction of different types of mélange may provide additional constraints for a better understanding of the tectono-sedimentary evolution of the Neotethyan region.     

Iteratively re‐weighted and refined least squares algorithm for robust inversion of geophysical data

A robust metric of data misfit such as the ?₁‐norm is required for geophysical parameter estimation when the data are contaminated by erratic noise. Recently, the iteratively re‐weighted and refined least‐squares algorithm was introduced for efficient solution of geophysical inverse problems in the presence of additive Gaussian noise in the data. We extend the algorithm in two practically important directions to make it applicable to data with non‐Gaussian noise and to make its regularisation parameter tuning more efficient and automatic. The regularisation parameter in iteratively reweighted and refined least‐squares algorithm varies with iteration, allowing the efficient solution of constrained problems. A technique is proposed based on the secant method for root finding to concentrate on finding a solution that satisfies the constraint, either fitting to a target misfit (if a bound on the noise is available) or having a target size (if a bound on the solution is available). This technique leads to an automatic update of the regularisation parameter at each and every iteration. We further propose a simple and efficient scheme that tunes the regularisation parameter without requiring target bounds. This is of great importance for the field data inversion where there is no information about the size of the noise and the solution. Numerical examples from non‐stationary seismic deconvolution and velocity‐stack inversion show that the proposed algorithm is efficient, stable, and robust and outperforms the conventional and state‐of‐the‐art methods.     

The increase of rainfall erosivity and initial soil erosion processes due to rainfall acidification

The drastic growth of population in highly industrialized urban areas, as well as fossil fuel use, is increasing levels of airborne pollutants and enhancing acid rain. In rapidly developing countries such as Iran, the occurrence of acid rain has also increased. Acid rain is a driving factor of erosion due to the destructive effects on biota and aggregate stability; however, little is known about its impact on specific rates of erosion at the pedon scale. Thus, the present study aimed to investigate the effect of acid rain at pH levels of 5.25, 4.25, and 3.75 for rainfall intensities of 40, 60, and 80 mm h^?1 on initial soil erosion processes under dry and saturated soil conditions using rainfall simulations. The results were compared using a two‐way ANOVA and Duncan tests and showed that initial soil erosion rates with acidic rain and non‐acidic rain under dry soil conditions were significantly different. The highest levels of soil particle loss due to splash effects in all rainfall intensities were observed with the most acidic rain (pH = 3.75), reaching maximum values of 16 g m^?2 min^?1. The lowest levels of particle losses were observed in the control plot where non‐acidic rain was used, with values ranging from 3.8 to 8.1 g m^?2 min^?1. Similarly, under saturated soil conditions, the lowest level of soil particle loss was observed in the control plot, and the highest peaks of soil loss were observed for the most acidic rains (pH = 3.75 and pH = 4.25), reaching maximum average values of 40 g m^?2 min^?1. However, for saturated soils with acidic water but with non‐acidic rain, the highest soil particle loss was observed for the control plot for all the rainfall intensities. In conclusion, acidic rain has a negative impact on soils, which can be more intense with a concomitant increase in rainfall intensity. Rapid solutions, therefore, need to be found to reduce the emission of pollutants into the air, otherwise, rainfall erosivity may drastically increase.     

Aeolian sediment fingerprinting using a Bayesian mixing model

Identifying sand provenance in depositional aeolian environments (e.g. dunefields) can elucidate sediment pathways and fluxes, and inform potential land management strategies where windblown sand and dust is a hazard to health and infrastructure. However, the complexity of these pathways typically makes this a challenging proposition, and uncertainties on the composition of mixed‐source sediments are often not reported. This study demonstrates that a quantitative fingerprinting method within the Bayesian Markov Chain Monte Carlo (MCMC) framework offers great potential for exploring the provenance and uncertainties associated with aeolian sands. Eight samples were taken from dunes of the small (~58 km²) Ashkzar erg, central Iran, and 49 from three distinct potential sediment sources in the surrounding area. These were analyzed for 61 tracers including 53 geochemical elements (trace, major and rare earth elements (REE)) and eight REE ratios. Kruskal–Wallis H‐tests and stepwise discriminant function analysis (DFA) allowed the identification of an optimum composite fingerprint based on six tracers (Rb, Sr, ⁸⁷Sr, (La/Yb)_n, Ga and δCe), and a Bayesian mixing model was applied to derive the source apportionment estimates within an uncertainty framework. There is substantial variation in the uncertainties in the fingerprinting results, with some samples yielding clear discrimination of components, and some with less clear fingerprints. Quaternary terraces and fans contribute the largest component to the dunes, but they are also the most extensive surrounding unit; clay flats and marls, however, contribute out of proportion to their small outcrop extent. The successful application of these methods to aeolian sediment deposits demonstrates their potential for providing quantitative estimates of aeolian sediment provenances in other mixed‐source arid settings, and may prove especially beneficial where sediment is derived from multiple sources, or where other methods of provenance (e.g. detrital zircon U–Pb dating) are not possible due to mineralogical constraints. Copyright © 2017 John Wiley & Sons, Ltd.     

Design,manufacture and calibration of the SARI portable rainfall simulator for field and laboratory experiments

The Simulator of Artificial RaInfall (SARI) rainfall simulator (RS) is a newly designed, constructed and calibrated, portable, two-nozzle RS with low water consumption, accurate measurement, easy management and low cost. The raindrop size distribution and velocity and mean rainfall intensity were measured. The best rainfall spatial distribution was achieved with nozzles separated by 50, 60 and 70 cm, and with oscillation angles of 30, 45 and 60°, at a pressure of 60 kPa. The uniformity coefficient varied from 57 to 61% and rainfall intensity from 48 to 101 mm h^?1. The raindrop diameter varied from 0.2 to 9.9 mm. The raindrop velocity at the optimum pressure of 60 kPa, which was measured with high-speed photography, ranged from 1.1 to 7.1 m s^?1. Comparison with other RSs shows that the SARI simulator is a suitable apparatus to research soil erosion and runoff generation under laboratory and field conditions.     

Fast hyperbolic deconvolutive Radon transform using generalized Fourier slice theorem

The hyperbolic Radon transform has a long history of applications in seismic data processing because of its ability to focus/sparsify the data in the transform domain. Recently, deconvolutive Radon transform has also been proposed with an improved time resolution which provides improved processing results. The basis functions of the (deconvolutive) Radon transform, however, are time-variant, making the classical Fourier based algorithms ineffective to carry out the required computations. A direct implementation of the associated summations in the time–space domain is also computationally expensive, thus limiting the application of the transform on large data sets. In this paper, we present a new method for fast computation of the hyperbolic (deconvolutive) Radon transform. The method is based on the recently proposed generalized Fourier slice theorem which establishes an analytic expression between the Fourier transforms associated with the data and Radon plane. This allows very fast computations of the forward and inverse transforms simply using fast Fourier transform and interpolation procedures. These canonical transforms are used within an efficient iterative method for sparse solution of (deconvolutive) Radon transform. Numerical examples from synthetic and field seismic data confirm high performance of the proposed fast algorithm for filling in the large gaps in seismic data, separating primaries from multiple reflections, and performing high-quality stretch-free stacking.     

A review of the application of the MUSLE model worldwide

Abstract

The sediment yield model of the MUSLE (modified universal soil loss equation) is applied extensively throughout the world, but different performances have been reported of its success relative to measured data. A review of all the available literature is presented to assess the application of the model under different conditions and, ultimately, make a comprehensive judgement on the different aspects to allow readers to adjust their further research. A review of 49 papers showed the variable accuracy of the model, which depends on the manner of calculation and determination of the input and output, and the study time and space scales. There were differences in land use, in correspondence of the physiographic characteristics with those of the original conditions of model development, and even in the experience of researchers in applying the model. The results also show the need to consider the original application of the model, as proposed by its developers, to achieve comparable results.

Editor Z.W. Kundzewicz

Citation Sadeghi, S.H.R., et al., 2014. A review of the application of the MUSLE model worldwide. Hydrological Sciences Journal, 59 (2), 365–375.     

Regularization of geophysical ill-posed problems by iteratively re-weighted and refined least squares

The iteratively re-weighted least squares (IRLS) is a commonly used algorithm which has received significant attention in geophysics and other fields of scientific computing for regularization of discrete ill-posed problems. The IRLS replaces a difficult optimization problem by a sequence of weighted linear systems. The optimum solution of the original problem is usually determined by computing the solution for various regularization parameters λ, each needing several re-weighted iterations (usually 10–15). In this paper, in order to decrease the required computation time (iterations) while maintaining good properties of the algorithm such as edge-preserving, the IRLS is augmented with a refinement strategy and the value of λ is progressively updated in a geometrical form during the iterations. The new algorithm, called iteratively re-weighted and refined least squares (IRRLS), can be interpreted as a Landweber iteration with a non-stationary shaping matrix which is updated based on the solution obtained from previous iteration. Two main properties of IRRLS are (1) the regularization parameter is the stopping iteration and (2) it is equipped with a tuning parameter which makes it flexible for recovering models with different smoothness. We show numerically that both the residual and regularization norms are monotone functions of iteration and hence well behaved for automatic determination of stopping parameter. The Stain’s unbiased risk estimate (SURE), generalized cross validation (GCV), L-curve analysis, and discrepancy principle (DCP) techniques are employed for automatic determination of optimum iteration. Experimental results from seismic deconvolution and seismic tomography are included showing that the proposed methodology outperforms the conventional IRLS with significantly lower computational burden.