Suitability of MUSLT for storm sediment yield prediction in Chehelgazi watershed,Iran

Abstract

The Chehelgazi watershed of Gheshlagh Dam in western Iran was selected to check the capability of the MUSLT (Theoretical Modified Universal Soil Loss Equation) model for estimating sediment yield during storms. The efficiency of MUSLT for sediment yield prediction was assessed using observed sediment data recorded for 11 storm events between October 2006 and April 2007. The results showed that MUSLT overestimated sediment yield with a high coefficient of determination (R² = 0.636 and p < 0.05), and it was then calibrated by examining regression models. The developed calibrated model (C-MUSLT) performed well, with a coefficient of determination of 0.739 (p < 0.05) and relative estimation and verification errors of 49.36 and 25.18%, respectively. The results of comparison between observed and estimated values, obtained by applying the calibrated model, confirmed that the difference was significant with a t value of 1.453 (p?=?0.05).

Citation Sadeghi, S.H.R., Gholami, L., and Khaledi Darvishan, A.V., 2013. Suitability of MUSLT for storm sediment yield prediction in Chehelgazi watershed, Iran. Hydrological Sciences Journal, 58 (4), 892–897.     

Shear wave velocity prediction using seismic attributes and well log data

Formation’s properties can be estimated indirectly using joint analysis of compressional and shear wave velocities. Shear wave data is not usually acquired during well logging, which is most likely for cost saving purposes. Even if shear data is available, the logging programs provide only sparsely sampled one-dimensional measurements: this information is inadequate to estimate reservoir rock properties. Thus, if the shear wave data can be obtained using seismic methods, the results can be used across the field to estimate reservoir properties. The aim of this paper is to use seismic attributes for prediction of shear wave velocity in a field located in southern part of Iran. Independent component analysis (ICA) was used to select the most relevant attributes to shear velocity data. Considering the nonlinear relationship between seismic attributes and shear wave velocity, multi-layer feed forward neural network was used for prediction of shear wave velocity and promising results were presented.     

Non‐convex compressed sensing with frequency mask for seismic data reconstruction and denoising

Compressed Sensing has recently proved itself as a successful tool to help address the challenges of acquisition and processing seismic data sets. Compressed sensing shows that the information contained in sparse signals can be recovered accurately from a small number of linear measurements using a sparsity‐promoting regularization. This paper investigates two aspects of compressed sensing in seismic exploration: (i) using a general non‐convex regularizer instead of the conventional one‐norm minimization for sparsity promotion and (ii) using a frequency mask to additionally subsample the acquired traces in the frequency‐space () domain. The proposed non‐convex regularizer has better sparse recovery performance compared with one‐norm minimization and the additional frequency mask allows us to incorporate a priori information about the events contained in the wavefields into the reconstruction. For example, (i) seismic data are band‐limited; therefore one can use only a partial set of frequency coefficients in the range of reflections band, where the signal‐to‐noise ratio is high and spatial aliasing is low, to reconstruct the original wavefield, and (ii) low‐frequency characteristics of the coherent ground rolls allow direct elimination of them during reconstruction by disregarding the corresponding frequency coefficients (usually bellow 10 Hz) via a frequency mask. The results of this paper show that some challenges of reconstruction and denoising in seismic exploration can be addressed under a unified formulation. It is illustrated numerically that the compressed sensing performance for seismic data interpolation is improved significantly when an additional coherent subsampling is performed in the domain compared with the domain case. Numerical experiments from both simulated and real field data are included to illustrate the effectiveness of the presented method.     

Assessing the Performance of Independent Component Analysis in Remote Sensing Data Processing

Independent component analysis (ICA) is a novel method to be considered as a powerful type of analysis in the process of source signal separation. Based on the capabilities of this particular analysis, there will be a hypothesis of applying ICA in the image processing of remote sensing data. This paper aims to introduce the ability of ICA in contrasting and highlighting some area with potential of mineralization. Considering and applying ICA transformation on the ETM⁺ image of southern Masule, Iran has resulted in finding some favorable points for further investigation. Moreover, sampling program on the indicated area has led to identify some huge, unexpected lithology and dikes. ICA analysis is a robust method even in remote sensing data processing with the high speed and capabilities in separating source signals from noise.     

Transpression and Exhumation of Granitoid Plutons along the Northern Part of the Nehbandan Fault System in the Sistan Suture Zone,Eastern Iran

Geotectonics - Geometry and kinematics of deformation across the Nehbandan Fault System (NFS) have been studied in the Bibimaryam and Damdameh areas in the Sistan Suture zone (SSZ). Nearly vertical...     

Statistical fusion of two-scale images of porous media

The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.     

Characterization of alkanes, hopanes, and polycyclic aromatic hydrocarbons (PAHs) in tar-balls collected from the East Coast of Peninsular Malaysia

The East Coast of Peninsular Malaysia faces the South China Sea and is vulnerable to oil pollution because of intense petroleum production activities in the area. The South China Sea is also a favored route for supertankers carrying crude oil to the Far East. Consequently, oil spills can occur, causing pollution and contamination in the surrounding areas. Residual oil spills stranded on coastal beaches usually end up as tar-balls. Elucidating the sources of tar-balls using a molecular marker approach is essential in assessing environmental impacts and perhaps settling legal liabilities for affected parties. This study utilizes a multimodal molecular marker approach through the use of diagnostic ratios of alkanes, hopanes, and polycyclic aromatic hydrocarbons (PAHs) to determine the source, distribution and weathering of tar-balls. Hopane ratios (e.g., C29/C30, and summation C31-C35/C30 ratios) were used to identify the sources of tar-balls. The weathering effects were distinguished by using alkanes, namely the unresolved complex mixture (UCM) and low molecular weight/high molecular weight (L/H) ratios. Similarly, PAHs were also used for the determination of weathering processes undergone by the tar-balls. This multimodal molecular marker gave a very strong indication of the sources of tar-balls in this study. For example, 16 out of 17 samples originated from South East Asian Crude Oil (SEACO) with one sample from Merang, Terengganu originating from North Sea Oil (Troll). The TRME-2 sample may have come from a supertanker's ballast water discharge. The second possibility is that the tar-ball may have been transported via oceanographic currents. All 'weathered' sample characterizations were based on the presence of UCM and other ratios. The multimodal molecular marker approach applied in this study has enabled us to partially understand the transport behavior of tar-balls in the marine environment and has revealed insights into the weathering process of tar-balls.     

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Predicting the geometry of channels and alluvial rivers is of primary importance in river engineering science. Appropriately designing channels and predicting stable river cross‐sections can decrease costs and prevent the destruction of installations and agricultural land by rivers. Consequently, researchers have applied different empirical and regression methods to achieve relations for predicting stable channel and river geometry. In this study, Group Method of Data Handling ]GMDH) models are used to predict three geometric variables of stable channels, namely width (w), depth (h) and slope (s). The effect of different input parameters, such discharge (Q), median grain size (d₅₀) and the Shields parameter (τ^*) on the GMDH models is assessed with regard to predicting stable channel geometry. The results indicate that the GMDH model with mean absolute percentage error (MAPE) of 5.53%, 4.05% and 4.89% for channel width, depth and slope prediction respectively, exhibits good accuracy. Moreover, a comparison of the GMDH models with previous theoretical equations (based on regression analysis) indicates the superiority of GMDH model performance, with error reductions of one‐fifth, one‐eighth and one‐sixth compared with the regression equations for channel width, depth and slope prediction, respectively. Copyright © 2016 John Wiley & Sons, Ltd.     

Investigation of the Relationship between Sediment Graph and Hydrograph of Flood Events (Case Study: Gharachay River Tributaries,Arak, Iran)

Water Resources - In the comprehensive management of watersheds, the knowledge of the sedimentation process in flood conditions is considered as an important and challenging issue. The aim of this...     

Framework and modeling structure of Exploration and Mining Cadastre system in Iran

In this paper we introduce a framework for developing the Exploration and Mining Cadastre System (EMCS), which involves all activities related to exploration areas, including registration and subsequent legal issues in a broader sense. It is built with two main purposes in mind: (1) enable necessary surveys, observe applicant’s reserved parcel, register the parcel online, and issue and follow up necessary permits/licenses in the field of exploration, (2) provide a mutual platform for data exchange among various service systems in the field of exploration and mining in Iran. The EMCS model is designed in Unified Modeling Language (UML) with three main sets of classes: (1) RegisterObject (2) ActivityLicense, and (3) Person. Other relevant features of the model are also specified at the class level. These include: conditional registered parcels, unregisterable areas such as prohibited or protected areas, legal borders around registered or serving parcels, reference documents such as legal survey results documents, responsibilities, and restrictions. The model also considers issues related to exploration cost, parcel code, tax, royalty, commodity, priorities, facilitations, evaluation, transfer of ownership, changing parameters, and temporal registration. The temporal aspects of the classes are also supported by the model to take care of the time periods that involve the general procedures in the registration of exploration and mining properties. We anticipate that the framework and model structure provide a platform for data storage, improve the supervision of exploration and mining process, change manual into online registration system, prevent administrative corruption, and reduce bureaucracy.