Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery

Reconstruction of seismic data is routinely used to improve the quality and resolution of seismic data from incomplete acquired seismic recordings. Curvelet‐based Recovery by Sparsity‐promoting Inversion, adapted from the recently‐developed theory of compressive sensing, is one such kind of reconstruction, especially good for recovery of undersampled seismic data. Like traditional Fourier‐based methods, it performs best when used in conjunction with randomized subsampling, which converts aliases from the usual regular periodic subsampling into easy‐to‐eliminate noise. By virtue of its ability to control gap size, along with the random and irregular nature of its sampling pattern, jittered (sub)sampling is one proven method that has been used successfully for the determination of geophone positions along a seismic line. In this paper, we extend jittered sampling to two‐dimensional acquisition design, a more difficult problem, with both underlying Cartesian and hexagonal grids. We also study what we term separable and non‐separable two‐dimensional jittered samplings. We find hexagonal jittered sampling performs better than Cartesian jittered sampling, while fully non‐separable jittered sampling performs better than separable jittered sampling. Two other 2D randomized sampling methods, Poisson Disk sampling and Farthest Point sampling, both known to possess blue‐noise spectra, are also shown to perform well.     

Application of singular value decomposition (SVD) and semi-discrete decomposition (SDD) techniques in clustering of geochemical data: an environmental study in central Iran

Common multivariate clustering techniques are ineffective in identifying subtle patterns of correlation, and clustering of variables or samples within complex geochemical datasets. This study compares the combination of singular value decomposition (SVD) and semi discrete decomposition (SDD), with that of hierarchical cluster analysis (HCA), to examine patterns within a multielement soil geochemical dataset from an agricultural area in the vicinity of Pb–Zn mining operations in central Iran. SVD was used to both identify patterns of correlation between variables and samples and to “denoise” the data, and SDD to simultaneously cluster the samples and variables. The results reveal various spatial associations of mining waste-associated metals As, Ba, Pb and Zn, and within the remaining elements whose distribution is largely controlled by the major oxides. SVD–SDD was found to be superior to HCA, in its ability to detect subtle clusters in soil geochemistry indicative of mine-related contamination in the study area.     

Effect of <Emphasis Type="Italic">q</Emphasis>-nonextensive distribution of electrons on electron acoustic solitons

Ion acoustic shock waves (IASWs) are studied in a plasma consisting of electrons, positrons and ions. Boltzmann distributed positrons and superthermal electrons are considered in the plasma. The dissipation is taken into account the kinematic viscosity among the plasma constituents. The Korteweg–de Vries–Burgers (KdV–Burgers) equation is derived by reductive perturbation method. Shock waves are solutions of KdV–Burgers equation. It is observed that an increasing positron concentration decreases the amplitude of the waves. Furthermore, in the existence of the kinematic viscosity among the plasma, the shock wave structure appears. The effects of ion kinematic viscosity (η ₀) and the superthermal parameter (k) on the ion acoustic waves are found.     

Correction to: Nexus between vulnerability and adaptive capacity of drought-prone rural households in northern Bangladesh

Natural Hazards - The article was published with a spelling error in one of the co-author names. The correct spelling is reflected in this correction, and the original work has been updated to...     

Developing the Rule of Thumb for Evaluating Penetration Rate of TBM,Using Binary Classification

Geotechnical and Geological Engineering - Using the tunnel boring machine (TBM) in tunneling projects contributes significantly to increased efficiency and reducing the time of project...     

Environmental characterization of a karst polje: an example from Izeh polje,southwest Iran

Groundwater contamination is a well-known phenomenon, which occurs on local and regional scales in Izeh polje. The aims of this paper are investigation of the impact of human activities on the polje ecosystem, determination of the vulnerability of ground water, and to solve environmental problems. Nitrate contamination of groundwater in the Izeh polje was predicted using a solute transport model. The nitrate concentration in groundwater in most parts of Izeh polje is greater than maximum concentration permissible for drinking water, i.e., 45 mg/l. The main source of nitrate in the eastern underground areas of Izeh city is the domestic sewage. Bacterial pollution of shallow ground water in Izeh polje is severe and widespread. About 45% of ground water samples in May and September 2001 have positive MPN coliforms. Infiltration of polluted surface waters and decrease of water table depth, have lead to bacterial pollution of 80% of ground water samples in January 2002. The northeast, south and southwest areas of Izeh polje have higher pollution potential rather than its middle parts. The aquifer vulnerability indices in the middle, eastern, and northern parts of the polje are moderately lower as a result of decreased sediment size of the aquifer. The pollution in the polje depends on the amount and presence of pollutants. If they do exist, the possibility of pollution is considerable due to the coarseness of materials and shallow depth of groundwater table.     

The solar lithium abundance

A detailed observational study of the solar photospheric lithium feature has been carried out with emphasis on center-limb observations, continuum location, possible effects of telluric lines, effects of blending by atomic and molecular lines, and decomposition of the solar spectrum around λ6707 Å.     

Online Multi-step Ahead Prediction of Time-Varying Solar and Geomagnetic Activity Indices via Adaptive Neurofuzzy Modeling and Recursive Spectral Analysis

The time-varying Sun as the main source of space weather affects the Earth??s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices.     

Distributed empirical algorithms to estimate catchment scale sediment connectivity and yield in a subtropical region

The intensity of soil loss and sediment delivery, representing hydrologic and geomorphic processes within a catchment, accelerates with rapid changes in land cover and rainfall events. An underlying component of sustainable management of water resources is an understanding of spatial and temporal variability and the adverse influences of regional parameters involved in generating sediment following widespread changes in land cover. A calibrated algorithm of soil loss coupled with a sediment delivery ratio (SDR) was applied in raster data layers to improve the capability of a combined model to estimate annual variability in sediment yields related to changes in vegetation cover identified by analyses of SPOT imagery. Four catchments in Kangaroo River State forest were assessed for annual changes in sediment yields. Two catchments were selectively logged in 2007, while the two other sites remained undisturbed. Results of SDR estimates indicated that only a small proportion of total eroded sediment from hillslopes is transported to catchment outlets. Larger SDR values were estimated in regions close to catchment outlets, and the SDR reduced sharply on hillslopes further than 200–300 m from these areas. Estimated sediment yield increased by up to 30% two years after land cover change (logging) in 2009 when more storm events were recorded, despite the moderate density of vegetation cover in 2009 having almost recovered to its initial pre‐logging (2005) condition. Rainfall had the most significant influence on streamflow and sediment delivery in all catchments, with steeply sloping areas contributing large amounts of sediment during moderate and high rainfall years in 2007 and 2009. It is concluded that the current scenario of single‐tree selection logging utilized in the study area is an acceptable and environmentally sound land management strategy for preservation of soil and water resources. Copyright © 2013 John Wiley & Sons, Ltd.