Enabling affordable omnidirectional subsurface extended image volumes via probing

Image gathers as a function of subsurface offset are an important tool for the inference of rock properties and velocity analysis in areas of complex geology. Traditionally, these gathers are thought of as multidimensional correlations of the source and receiver wavefields. The bottleneck in computing these gathers lies in the fact that one needs to store, compute, and correlate these wavefields for all shots in order to obtain the desired image gathers. Therefore, the image gathers are typically only computed for a limited number of subsurface points and for a limited range of subsurface offsets, which may cause problems in complex geological areas with large geologic dips. We overcome increasing computational and storage costs of extended image volumes by introducing a formulation that avoids explicit storage and removes the customary and expensive loop over shots found in conventional extended imaging. As a result, we end up with a matrix–vector formulation from which different image gathers can be formed and with which amplitude‐versus‐angle and wave‐equation migration velocity analysis can be performed without requiring prior information on the geologic dips. Aside from demonstrating the formation of two‐way extended image gathers for different purposes and at greatly reduced costs, we also present a new approach to conduct automatic wave‐equation‐based migration‐velocity analysis. Instead of focusing in particular offset directions and preselected subsets of subsurface points, our method focuses every subsurface point for all subsurface offset directions using a randomized probing technique. As a consequence, we obtain good velocity models at low cost for complex models without the need to provide information on the geologic dips.     

Investigation of Arsenotrophic Microbiome in Arsenic‐Affected Bangladesh Groundwater

Arsenotrophic bacteria contribute to the nutrient cycling in arsenic (As) affected groundwater. This study employed a culture‐independent and ‐dependent investigation of arsenotrophic microbiomes in As affected groundwater samples collected from Madhabpur, Sonatengra, and Union Porishod in Singair Upazila, Manikganj, Bangladesh. Total As contents, detected by Atomic Absorption Spectrophotometry (AAS) of the samples, were 47 µg/L (Madhabpur, SNGW‐1), 53 µg/L (Sonatengra, SNGW‐2), and 12 µg/L (Union porishod, SNGW‐3), whereas the control well (SNGW‐4; depths >150 m) showed As content of 6 µg/L. Denaturing Gradient Gel Electrophoresis (DGGE) analysis of the amplified 16S rRNA gene from As‐affected groundwater samples revealed the dominance of aerobic bacteria Pseudomonas within heterogeneous bacterial populations. DGGE of heterotrophic enrichments supplemented with arsenite [As (III)] for 4 weeks showed the dominance of Chryseobacterium, Flavobacterium, and Aquabacterium, whereas the dominant genera in that of autotrophic enrichments were Aeromonas, Acinetobacter, and Pseudomonas. Cultured bacteria retrieved from both autotrophic and heterotrophic enrichments were distinguished into nine genotypes belonging to Chryseobacterium, Acinetobacter, Escherichia, Pseudomonas, Stenotrophomonas, Janibacter, Staphylococcus, and Bacillus. They exhibited varying range of As(III) tolerance from 4 to 27 mM. As(III) transformation potential was confirmed within the isolates with oxidation rate as high as 0.143 mM/h for Pseudomonas sp. Sn 28. The arsenotrophic microbiome specifies their potential role in groundwater As‐cycling and their genetic information provide the scientific basis for As‐bioremediation.     

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer

Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.     

Extracting water-related features using reflectance data and principal component analysis of Landsat images

This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4–5–7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach.     

Impact of Indian ocean dipole on the coastal upwelling features off the southwest coast of India

A three-dimensional regional ocean model is used to examine the impact of positive Indian ocean dipole (pIOD) events on the coastal upwelling features at the southwest coast of India (SWCI). Two model experiments are carried out with different surface boundary conditions that prevailed in the normal and pIOD years from 1982 to 2010. Model experiments demonstrate the weakening of coastal upwelling at the SWCI in the pIOD years. The reduced southward meridional wind stress off the SWCI leads to comparatively lower offshore Ekman transport during August–October in the pIOD years to that in normal years. The suppressed coastal upwelling results in warmer sea surface temperature and deeper thermocline in the pIOD years during June–September. The offshore spatial extent of upwelled colder (<?22 °C) water was up to 75.5° E in August–September in normal years that was limited up to 76.2° E in pIOD years. The heat budget analysis reveals the decreased contribution of vertical entrainment process to the mixed layer cooling in pIOD years which is almost half of that of normal years in October. The net heat flux term shows warming tendency during May–November with a higher magnitude (+?0.4 °C day^?1) in normal years than pIOD years (+?0.28 °C day^?1). The biological productivity is found to reduce during the pIOD years as the concentration of phytoplankton and zooplankton decreases over the region of coastal upwelling at SWCI. Nitrate concentration in the pIOD years dropped by half during August–September and dropped by an order of magnitude in October as compared to its ambient concentration of 13 μmol L^?1 in normal years.     

Morphometry and Morphostructural Studies of the Parts of Gola River and Kalsa River Basins,Chanphi-Okhalkanda Region,Kumaun Lesser Himalaya,India

Geotectonics - Study of morphometric parameters such as stream length (Lu) and stream length ratios (Rl), bifurcation ratio (Rb), drainage density (D), form factor (Rf), circularity ratio (Rc),...     

Probabilistic seismic hazard assessment for some parts of the Indo-Gangetic plains,India

Natural Hazards - Indo-Gangetic plains are seismically most vulnerable due to the proximity of adjacent great Himalayan earthquakes and thick alluvium deposits of the Ganga River system. As the...     

Entropy-based fuzzy classification parameter optimization using uncertainty variation across spatial resolution

In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM). It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty.