Seismic ground-roll noise attenuation using deep learning

We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach.     

Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)

Theoretical and Applied Climatology - In the seamless forecast paradigm, it is hypothesized that the reduction in initial error in the dynamical model forecast would help to reduce forecast error...     

PCA–based composite drought index for drought assessment in Marathwada region of Maharashtra state,India

Theoretical and Applied Climatology - This paper presents a composite approach for drought characterization and monitoring using in situ and remote sensing-based drought indicators. The study was...     

Zircon ages of late Palaeoproterozoic (ca. 1.72–1.70 Ga) extension-related granitoids in NE Rajasthan,India: Regional and tectonic significance

The Khetri region forms a late Palaeoproterozoic igneous–metamorphic complex in NE Rajasthan, India. Seven granitoid plutons of the Khetri complex have been studied for zircon U–Pb and Pb–Pb dating along with whole-rock and Nd–Sr isotope geochemistry to provide new constraints on the Palaeoproterozoic magmatic activity in the Aravalli orogen of northwestern India. Most intrusives show evidence of moderate to extreme albitisation forming microcline–albite granite and albite granite, respectively. The rocks are metaluminous to weakly peraluminous, largely ferroan and intraplate A-type granites. The U–Pb zircon ages for four plutons cover a time span of 1732–1682 Ma, whereas Pb–Pb zircon evaporation data for three intrusives indicate minimum emplacement ages between 1671 and 1537 Ma. The Nd–Sr isotopic systematics suggest the involvement of Neoarchaean to Palaeoproterozoic crustal components in the petrogenesis of these granitoids. A regional survey of late Palaeoproterozoic ages in the Aravalli orogen provides evidence for a geographically widespread extension-related event in the northwestern Indian shield about 1720–1700 Ma ago. The record of comparable ages and the magmatic history reported in parts of North America and the North China Craton may indicate the significance of this event for the rift tectonics of the hypothetical supercontinent Columbia.     

Geochemistry of Renuka Lake and wetland sediments,Lesser Himalaya (India): implications for source-area weathering,provenance, and tectonic setting

The geochemical investigation of sediments deposited in the Renuka Lake basin and its adjoining wetland has shown variation in the distribution and concentration of major, trace and REEs. The major elements are depleted in the lake in relation to wetland and that of Post Archaean Australian, Shale (PAAS), except for CaO which is strikingly in excess and has a dilution effect on SiO₂ and other oxides and trace elements. The Wetland sediments, on the other hand, are enriched in Al₂O₃, Fe₂O₃, K₂O and TiO₂ and the latter three show a positive correlation with Al₂O₃ in both wetland and lake sediments suggesting their association with phyllosilicates and similar source rocks. The enrichment of Y, Zr, Ni, Th, U and Nb in wetland compared to lake and their similarity with PAAS in the former, suggests more clay fractions in the wetland. A high Zr/Hf ratio in wetland and lake sediments and a positive correlation of Zr with Y and HREE indicate Zr control on HREEs. However, higher Zr/Yb and Zr/Th ratios in wetland compared to lake indicate mineral sorting during the process of lighter particles (clays) being trapped in wetland soil. This is also reflected from negative correlation of Gd_N/Yb_N with Al₂O₃ and a strong positive correlation with SiO₂ in wetland sediments. The wetland in this context has a control on lake sediment chemistry. The chondrite normalized REE patterns are essentially the same for lake as well as wetland sediments but abundance decreases in the former. The similarity of pattern with that of PAAS and negative Eu anomaly indicates a cratonic source of sediments. In a plot of the individual samples, wetland samples cluster while lake samples are separated indicating fractionation of lake sediments. A strong positive correlation of La_N/Yb_N with Al₂O₃ and a positive correlation of Zr-∑LREE and Zr-La_N/Yb_N suggest that LREEs are controlled by both phyllosilicates and zircon. The chemical index of alteration (CIA) indices in lake sediments and in wetland are higher than PAAS indicating moderate chemical weathering in the source area. The petrography, lack of felsic magmatic rock fragments, and negative correlation between Zr-(Gd/Yb)_C indicate sedimentary source rocks for the detritus. This is in conformity with the Lesser Himalayan sedimentary sequence belonging to neo-Proterozoic–Proterozoic age and constituting lake catchment of Renuka. The tectonic delineation and discriminant function plots of lake and wetland sediments indicate their cratonic and/or quartzose sedimentary orogenic terrain source that has been deposited in a passive margin setting.     

Cloud classification in NOAA AVHRR imageries using spectral and textural features

Clouds contribute significantly to the formation of many of the natural hazards. Hence cloud mapping and its classification becomes a major component of the various physical models which are used for forecasting natural hazards. The problem of cloud data classification from NOAA AVHRR (advance very high resolution radiometer) satellite imagery using image transformation techniques is considered in this paper. The singular value decomposition (SVD) scheme is used to extract the salient spectral and textural features attributed to satellite snow and cloud data in visible and IR channels. The goals of this paper are to discriminate between clear sky and clouds in an 8 × 8 pixel array of 1.1 km AVHRR data. If clouds are present then classify them as low, medium or high range. This scheme can effectively segregate clouds and non-cloud features in the visible and IR bands of the imagery. It can also classify clouds as low, medium or high range with a success rate of 70–90%. Computer-based snow and cloud discrimination and automatic cloud classification system will help the forecaster in various climatological applications, viz., energy balance estimation, precipitation forecasting, landslide forecasting, weather forecasting and avalanche forecasting etc.     

A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya

Natural Hazards - Avalanche forecasting is carried out using physical as well as statistical models. All these models have certain limitations associated with their mathematical formulation that...     

Evaluation of time scale of meteorological,hydrological and agricultural drought indices

Natural Hazards - The present study was carried out to characterize drought in the Marathwada region of Maharashtra, which experiences recurring droughts, through meteorological, hydrological and...     

Chandra and XMM–Newton observations of the low-luminosity X-ray pulsators SAX J1324.4−6200 and SAX J1452.8−5949

We present results from our Chandra and XMM–Newton observations of two low-luminosity X-ray pulsators  SAX J1324.4−6200  and  SAX J1452.8−5949  which have spin periods of 172 and 437 s, respectively. The XMM–Newton spectra for both sources can be fitted well with a simple power-law model of photon index,  Γ∼ 1.0  . A blackbody model can equally well fit the spectra with a temperature,   kT ∼  2 keV, for both sources. During our XMM–Newton observations,  SAX J1324.4−6200  is detected with coherent X-ray pulsations at a period of 172.86 ± 0.02 s while no pulsations with a pulse fraction greater than 18 per cent (at 95 per cent confidence level) in 0.2–12 keV energy band are detected in  SAX J1452.8−5949  . The spin period of  SAX J1324.4−6200  is found to be increasing on a time-scale of     which would suggest that the accretor is a neutron star and not a white dwarf. Using subarcsec spatial resolution of the Chandra telescope, possible counterparts are seen for both sources in the near-infrared images obtained with the son of infrared spectrometer and array camera (SOFI) instrument on the New Technology Telescope. The X-ray and near-infrared properties of  SAX J1324.4−6200  suggest it to be a persistent high-mass accreting X-ray pulsar at a distance  ≤8 kpc  . We identify the near-infrared counterpart of  SAX J1452.8−5949  to be a late-type main-sequence star at a distance ≤10 kpc, thus ruling out  SAX J1452.8−5949  to be a high-mass X-ray binary. However, with the present X-ray and near-infrared observations, we cannot make any further conclusive conclusion about the nature of  SAX J1452.8−5949  .