Orbital evolution and orbital phase resolved spectroscopy of the HMXB pulsar 4U 1538-52 with RXTE-PCA and BeppoSAX

We report here results from detailed timing and spectral studies of the high mass X-ray binary pulsar 4U 1538-52 over several binary periods using observations made with the Rossi X-ray Timing Explorer (RXTE) and BeppoSAX satellites. Pulse timing analysis with the 2003 RXTE data over two binary orbits confirms an eccentric orbit of the system. Combining the orbitial parameters determined from this observation with the earlier measurements we did not find any evidence of orbital decay in this X-ray binary. We have carried out orbital phase resolved spectroscopy to measure changes in the spectral parameters with orbital phase, particularly the absorption column density and the iron line flux. The RXTE-PCA spectra in the 3–20 keV energy range were fitted ∼6.4 keV, whereas the BeppoSAX spectra needed only a power law and Gaussian emission line at ∼6.4 keV in the restricted energy range of 0.3–10.0 keV. An absorption along the line of sight was included for both the RXTE and BeppoSAX data. The variation of the free spectral parameters over the binary orbit was investigated and we found that the variation of the column density of absorbing material in the line of sight with orbital phase is in reasonable agreement with a simple model of a spherically symmetric stellar wind from the companion star.     

Committee neural networks for porosity and permeability prediction from well logs

Neural computing has moved beyond simple demonstration to more significant applications. Encouraged by recent developments in artificial neural network (ANN) modelling techniques, we have developed committee machine (CM) networks for converting well logs to porosity and permeability, and have applied the networks to real well data from the North Sea. Simple three‐layer back‐propagation ANNs constitute the blocks of a modular system where the porosity ANN uses sonic, density and resistivity logs for input. The permeability ANN is slightly more complex, with four inputs (density, gamma ray, neutron porosity and sonic). The optimum size of the hidden layer, the number of training data required, and alternative training techniques have been investigated using synthetic logs. For both networks an optimal number of neurons in the hidden layer is in the range 8–10. With a lower number of hidden units the network fails to represent the problem, and for higher complexity overfitting becomes a problem when data are noisy. A sufficient number of training samples for the porosity ANN is around 150, while the permeability ANN requires twice as many in order to keep network errors well below the errors in core data. For the porosity ANN the overtraining strategy is the suitable technique for bias reduction and an unconstrained optimal linear combination (OLC) is the best method of combining the CM output. For permeability, on the other hand, the combination of overtraining and OLC does not work. Error reduction by validation, simple averaging combined with range‐splitting provides the required accuracy. The accuracy of the resulting CM is restricted only by the accuracy of the real data. The ANN approach is shown to be superior to multiple linear regression techniques even with minor non‐linearity in the background model.     

Porosity and permeability prediction from wireline logs using artificial neural networks: a North Sea case study

Estimations of porosity and permeability from well logs are important yet difficult tasks encountered in geophysical formation evaluation and reservoir engineering. Motivated by recent results of artificial neural network (ANN) modelling offshore eastern Canada, we have developed neural nets for converting well logs in the North Sea to porosity and permeability. We use two separate back-propagation ANNs (BP-ANNs) to model porosity and permeability. The porosity ANN is a simple three-layer network using sonic, density and resistivity logs for input. The permeability ANN is slightly more complex with four inputs (density, gamma ray, neutron porosity and sonic) and more neurons in the hidden layer to account for the increased complexity in the relationships. The networks, initially developed for basin-scale problems, perform sufficiently accurately to meet normal requirements in reservoir engineering when applied to Jurassic reservoirs in the Viking Graben area. The mean difference between the predicted porosity and helium porosity from core plugs is less than 0.01 fractional units. For the permeability network a mean difference of approximately 400 mD is mainly due to minor core-log depth mismatch in the heterogeneous parts of the reservoir and lack of adequate overburden corrections to the core permeability. A major advantage is that no a priori knowledge of the rock material and pore fluids is required. Real-time conversion based on measurements while drilling (MWD) is thus an obvious application.     

A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

Flood is one of the major recurrent natural disasters faced by the state of Bihar in north India. In the present study the authors assess the severity of flood hazard in Bihar, using 128 decadal historical satellite datasets acquired during different flood magnitudes during 1998 to 2010. The satellite‐based observations have been analysed in conjunction with the hydrological data, for assessing the frequency of inundation, severity of flood hazard and cropped land under flood hazard. This study assesses the spatial distribution of flooding and creation of systematic flood hazard database, which can be analysed from a spatial dimension in GIS. It is observed that about 24.56 lakh ha of the state's area and about 15.85 lakh ha of the cropped area are vulnerable to flood hazard. North Bihar is more vulnerable to flooding; 8 of the 10 areas identified as worst flood‐affected districts lie in this region.     

Sedimentological analysis of a Late Quaternary coastal dune system: An example from Gopnath,south‐east Saurashtra,Western India

Coastal dune systems consisting of allochemical grains are important sedimentary archives of Pleistocene age in both of the hemispheres between the latitudes of 20° to 40°. The south Saurashtra coast in western India exhibits a large section of Middle Pleistocene aeolianites in the form of coastal cliffs, which is famous as ‘Miliolite’. Miliolites of Gopnath in south‐east Saurashtra are the oldest known coastal aeolianite deposits (age >156 ka which corresponds to Marine Isotope Stage 6) in western India. Aeolian deposits of similar ages have also been reported from the Thar Desert in north‐west India and from Southern Arabia which were largely controlled by the south‐west monsoon wind system that affects the entire belt corresponding to Sahara–Sahel, the Arabian Peninsula and north‐western India. Miliolite deposits in Gopnath are characterized by grainfall, grainflow and wind ripple laminations. At least three types of aeolian bounding surfaces have been identified. Five major facies have been identified which represent the dune and interdune relationship within the coastal aeolian system. The major dune bodies are identified as transverse dune types. The Gopnath aeolianites were deposited under dominantly dry aeolian conditions. Facies association reveals two different phases of aeolian accumulation, namely initiation of aeolian sedimentation after a prolonged hiatus and the establishment of a regularized aeolian sedimentation system. While initiation of aeolian sedimentation is marked by vast stretches of sheet sand with occasional dune bodies, the overlying thick, tabular, laterally extensive cross‐stratified units manifest regular aeolian sedimentation. However, the dune building events in Gopnath were interrupted by development of laterally extensive palaeosol horizons. Eustasy and climate exerted the major allogenic controls on the aeolian sedimentation by affecting the sediment budget as well as influencing the sedimentation pattern.     

Rice-acreage estimation for Orrisa using remotely sensed data

Rice is one of the most important foodgrains grown in India. Attempts have been made to estimate kharif rice acreage of Orissa state since 1986 using digital remote sensing data from Landsat MSS/TM and/or IRS-1A. Accuracies of the estimates obtained have been evaluated against BES (Bureau of Economics and Statistics) estimate. This paper describes the methodology adopted for rice acreage estimation of Orissa state, the results obtained for three years, i.e. 1986–87, 1988–89 and 1989–90, and their accuracy.     

Least Square Support Vector Machine Applied to Slope Reliability Analysis

This paper investigates the feasibility of Least square support vector machine (LSSVM) model to cope the problem of implicit performance function during first order second moment (FOSM) method based slope reliability analysis. LSSVM is firmly based on the theory of statistical learning. In LSSVM, Vapnik’s ε -insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression. Here, LSSVM has been used as a regression technique to approximate implicit performance functions. A slope example has been presented for illustrating the applicability of LSSVM based FOSM method. The developed LSSVM based FOSM has been compared with the artificial neural network (ANN) and least square method. The result shows that the approximation of LSSVM can be used in the FOSM method for slope reliability analysis.     

Study of seasonal variability and heat budget of the East Australian Current using two eddy-resolving ocean circulation models

Two mutually exclusive ocean models, Ocean general circulation model for the Earth Simulator (OFES) and the Bluelink ReANalysis (version 2.1; BRAN2.1), and the spin-up model (SPINUP4) of BRAN2.1 were used to investigate seasonal variability of the East Australian Current (EAC). These model outputs were tested against satellite and in situ data. The seasonally averaged sea surface temperature (SST) in the OFES and SPINUP4 shows a negative bias of 1 °C. However, the OFES, SPINUP4, and BRAN2.1 have a similar seasonal cycle in SST. The annual mean EAC transport computed at 28°S from the three models shows a good agreement with annual mean transport computed using the in situ data. However, they have considerable differences in terms of annual cycle. A better performance of the BRAN2.1 in simulating the temperature field is a result of data assimilation. The advection of heat across the open boundaries contributes ～50 % of the heat content change in the region. This study suggests that the advection by the EAC plays a significant role in heat content change of the region.     

Prediction of the Maximum Amplitude and Timing of Sunspot Cycle 24

Precursor techniques, in particular those using geomagnetic indices, often are used in the prediction of the maximum amplitude for a sunspot cycle. Here, the year 2008 is taken as being the sunspot minimum year for cycle 24. Based on the average aa index value for the year of the sunspot minimum and the preceding four years, we estimate the expected annual maximum amplitude for cycle 24 to be about 92.8±19.6 (1-sigma accuracy), indicating a somewhat weaker cycle 24 as compared to cycles 21 – 23. Presuming a smoothed monthly mean sunspot number minimum in August 2008, a smoothed monthly mean sunspot number maximum is expected about October 2012±4 months (1-sigma accuracy).     

North Atlantic warming: patterns of long-term trend and multidecadal variability

Climate fluctuations in the North Atlantic Ocean have wide-spread implications for Europe, Africa, and the Americas. This study assesses the relative contribution of the long-term trend and variability of North Atlantic warming using EOF analysis of deep-ocean and near-surface observations. Our analysis demonstrates that the recent warming over the North Atlantic is linked to both long-term (including anthropogenic and natural) climate change and multidecadal variability (MDV, ~50–80 years). Our results suggest a general warming trend of 0.031 ± 0.006°C/decade in the upper 2,000 m North Atlantic over the last 80 years of the twentieth century, although during this time there are periods in which short-term trends were strongly amplified by MDV. For example, MDV accounts for ~60% of North Atlantic warming since 1970. The single-sign basin-scale pattern of MDV with prolonged periods of warming (cooling) in the upper ocean layer and opposite tendency in the lower layer is evident from observations. This pattern is associated with a slowdown (enhancement) of the North Atlantic thermohaline overturning circulation during negative (positive) MDV phases. In contrast, the long-term trend exhibits warming in tropical and mid-latitude North Atlantic and a pattern of cooling in regions associated with major northward heat transports, consistent with a slowdown of the North Atlantic circulation as evident from observations and confirmed by selected modeling results. This localized cooling has been masked in recent decades by warming during the positive phase of MDV. Finally, since the North Atlantic Ocean plays a crucial role in establishing and regulating the global thermohaline circulation, the multidecadal fluctuations discussed here should be considered when assessing long-term climate change and variability, both in the North Atlantic and at global scales.