• A K SINGH

      Articles written in Journal of Earth System Science

    • A study on precursors leading to geomagnetic storms using artificial neural network

      Gaurav Singh A K Singh

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      Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagneticstorms on Earth. In this paper, we discuss the development of an artificial neural network-basedmodel to study the precursor leading to intense and moderate geomagnetic storms, following halo coronalmass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-daytime window after the commencement of storm. The artificial neural network (ANN) model training,testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo andtheir properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. Thegeomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

    • Frequency characteristics of geomagnetic induction anomalies in Saurashtra region

      P V Vijaya Kumar P B V Subba Rao C K Rao A K Singh P Rama Rao

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      Magnetovariational studies were carried out along four different EW profiles in Saurashtra region in different phases, during January 2007–March 2012. Transient geomagnetic field variations (X, Y horizontal field and Z vertical field components) recorded along these profiles are analyzed to infer the electrical conductivity distribution of the region. The vertical field transfer functions which depict the characteristics of electrical conductivity distribution are presented in the form of induction arrows. From the spatial distribution of these arrows, it is inferred that the sediments filling the offshore basins have more conductivity than those basins in Saurashtra region. Z/H pseudo sections along the four profiles in conjunction with tectonics and other geophysical methods permit to infer that the conductivity anomaly in the eastern part of the profiles is associated with the crustal/lithosphere thinning. The possible cause for these anomalies may be explained in terms of partial melts associated with mafic intrusions, related to Deccan and pre-Deccan volcanism. High resistive block related to underplating mantle material has been reflected in 1D models of long period magnetotelluric data and its thickness reduces from west to east. Lithosphere–asthenosphere boundary varies from 80 to 100 km.

    • Performance of water vapour retrieval from MODIS and ECMWF and their validation with ground based GPS measurements over Varanasi

      AKHILESH KUMAR SANJAY KUMAR VINEET PRATAP A K SINGH

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      Water vapour is highly variable over tropical region and sensitive to weather condition, monsoon onset, green house effect, and pollution level in Ganga River. In the present study, variability in water vapour derived from Global Positioning System (GPS) over Varanasi (25$^{\circ}$20$^{\prime}$N, 82$^{\circ}$59$^{\prime}$E) during the period 2007–2010 has been studied. The GPS-derived water vapour (WV) has been compared with those retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) and ECMWF. The GPS-WV data concurrent to MODIS and ECMWF timing has been correlated to perform further analysis. To study the accuracy of water vapour retrieved from the MODIS and ECMWF, root mean square error (RMSE), absolute error (AE), correlation and standard deviation in it are computed with respect to GPS-derived water vapour. Analysis shows an annual correlation $R^{2}$ = 86%, RMSE = 9.5 mm and AE (MODIS–GPS) = 7.0 mm in MODIS retrieval and annual correlation R$^{2}$ = 86%, RMSE = 6.1 mm and AE (ECMWF–GPS) = 2.4 mm in ECMWF reanalysis retrieval. Correlation of ECMWF and MODIS datasets with the GPS datasets are found to vary significantly with seasons. The correlation is high during monsoon season and low during spring season. Water vapour is found to be an indicator for the onset of monsoon.

      $\bf{Highlights}$

      $\bullet$ Accuracy of water vapor (WV) retrieved from the MODIS and ECMWF with respect to GPS WV.

      $\bullet$ High Annual correlation of $R^{2}$ = 0.86 between both MODIS–GPS and ECMWF–GPS.

      $\bullet$ The correlation is high during monsoon season and low during spring season.

      $\bullet$ The performance of ECMWF is found to be better than that of MODIS.

    • Dimensionality and directionality analysis of magnetotelluric data by using different techniques: A case study from northern part of Saurashtra region, India

      P V VIJAYA KUMAR P B V SUBBA RAO A K SINGH AMIT KUMAR P RAMA RAO

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      Magnetotelluric (MT) data has been collected along 32 stations along E–W profile in northern part and eight LMT (long period MT) stations in north-central part of Saurashtra region. Dimensionality analysis is carried out prior to MT modelling for obtaining the subsurface dimension as well as the direction of the underlying substructures. To estimate the subsurface dimensionality from MT data, different techniques Swift skew, Bhar’s skew, normalized weights, phase tensor (PT) analysis and Wall’s rotational invariant approach have been applied. These results suggest 1D structure for lower periods (0.01–1 s) and 3D structure for higher periods (1–10000 s) along two different profiles indicating that the study area is highly heterogeneous. Regional strike has been estimated through phase tensor (PT) and Groom–Bailey (GB) techniques suggests N40$^{\circ}$ E regional strike direction that correlates well with the Delhi–Aravalli tectonic trend. 2D modelling of MT/LMT data sets brings out different resistivity and conductivity blocks. Basaltic magmatic intrusion and its recrystallization have resulted in resistivity blocks with conductivity anomalies (trapped fluids) in between them. It has been reflected as 3D structures at higher periods. Different sedimentary basins at shallow depth are observed as 1D structure in dimensionality analysis.

      $\bf{Highlights}$

      $\bullet$ Magnetotelluric (MT)/long period Magnetotelluric (LMT) survey is carried out in northern part of Saurashtra. Different dimensionality techniques were used to assess the structural dimensionality of the electrical conductivity of the earth and were compared.

      $\bullet$ Analysis of MT sites by using various methods indicates the electrical conductivity structure is less complex at the shallowest depths with mixed 1D and 2D cases that are affected by galvanic distortion. Both MT/LMT denote complex 3D nature from middle and lower depths.2D inversion of MT/LMT data brings out large-scale heterogeneities in the crust. This is attributed to different resistive and conductive blocks present at mid-crustal depths and extending up to lower crustal depths and correlates with dimensionality analysis.

    • Ionospheric and atmospheric perturbations due to two major earthquakes (M > 7.0)

      SANJAY KUMAR PRASHANT KUMAR SINGH ROHTASH KUMAR A K SINGH R P SINGH

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      The perturbation produced in the atmosphere/ionosphere associated with earthquake precursors during seismic activity of two major earthquakes which occurred on (1) 24 June 2019 in Indonesia (M = 7.3) and (2) on 19 August 2018 at Ndoi, Fiji (M = 8.2), are studied. Based on statistical analysis of total electron content (TEC) data, the presence of ionospheric perturbations 5 days before and after the main shock are found, which depends on the distance as well as direction of observation point from the epicentre. In general, ionospheric perturbations after the EQ at all the stations are found larger than that before the EQ. Probable mechanisms behind these perturbations associated with EQ are also being discussed. The ionospheric perturbations are observed at stations which are at larger distances from the epicentre, but not observed over other stations in different directions which are comparatively closer to the epicentre. These results suggest that seismic induced ionospheric anomaly is not isotropic in nature. Ozone data from three satellites: AIRS, OMI, and TOMS-like and MERRA-2 model are also analyzed 5 days before the EQ day and compared to the monthly average level. A strong link between anomalous variation in ionospheric TEC and atmospheric ozone data prior to both the EQs is noticed.

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