Browsing by Author "Srivastava M.K."

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    Association of lightning with LCL, EL, humidity at 850 mb and at 200 mb during various CAPE, over northern India
    (Elsevier B.V., 2024) Lal D.M.; Umakanth N.; Domkawale M.A.; Gopalakrishnan V.; Srivastava M.K.; Pawar S.D.
    Association of lightning with Lifting Condensation Level (LCL), Equilibrium Level (EL), K Index, and humidity at 850 mb and 200 mb in 2019 and 2020 over National Capital Region (Delhi) (Lat: 27�N -29�N, Lon: 76�E-78�E) is investigated using in-situ observation data. Study shows high lightning activity during low LCL, and vice versa. This high lighting and low LCL is associated with high relative humidity at 850 mb, and high �K� index. Low LCL and high relative humidity (low dew point depression) at 850 mb helps in generating super cell thunderstorms with spinning/tornado updraft. It is seen that asymmetric LCL height and relative humidity at 850 mb is the prime causes for uneven seasonal lightning in 2019 and 2020 over the region, despite more or less same seasonal aerosol and relative humidity. Anvil clouds behave uneven with time, despite, unanimous cloud top glaciation. � 2024 Elsevier B.V.
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    Comparison of neural networks techniques to predict subsurface parameters based on seismic inversion: a machine learning approach
    (Springer Science and Business Media Deutschland GmbH, 2024) Verma N.; Maurya S.P.; kant R.; Singh K.H.; Singh R.; Singh A.P.; Hema G.; Srivastava M.K.; Tiwari A.K.; Kushwaha P.K.; Singh R.
    Seismic inversion, complemented by machine learning algorithms, significantly improves the accuracy and efficiency of subsurface parameter estimation from seismic data. In this comprehensive study, a comparative analysis of machine learning techniques is conducted to predict subsurface parameters within the inter-well region. The objective involves employing three separate machine learning algorithms namely Probabilistic Neural Network (PNN), multilayer feedforward neural network (MLFNN), and Radial Basis Function Neural Network (RBFNN). The study commences by generating synthetic data, which is then subjected to machine learning techniques for inversion into subsurface parameters. The results unveil exceptionally detailed subsurface information across various methods. Subsequently, these algorithms are applied to real data from the Blackfoot field in Canada to predict porosity, density, and P-wave velocity within the inter-well region. The inverted results exhibit a remarkable alignment with well-log parameters, achieving an average correlation of 0.75, 0.77, and 0.86 for MLFNN, RBFNN, and PNN algorithms, respectively. The inverted volumes portray a consistent pattern of impedance variations spanning 7000�18000�m/s*g/cc, porosity ranging from 5 to 20%, and density within the range of 1.9�2.9�g/cc across the region. Importantly, all these methods yield mutually corroborative results, with PNN displaying a slight edge in estimation precision. Additionally, the interpretation of the inverted findings highlights anomalous zones characterized by low impedance, low density, and high porosity, seamlessly aligning with well-log data and being identified as sand channel. This study underscores the potential for seismic inversion, driven by machine learning techniques, to swiftly and cost-effectively determine critical subsurface parameters like acoustic impedance and porosity. � The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    Diurnal Variation of Rain Drop Size Distribution over the Western Ghats of India
    (National Institute of Science Communication and Policy Research, 2024) Kumar A.; Srivastava A.K.; Chakravarty K.; Srivastava M.K.
    Joss-Waldvogel Disdrometer (JWD) measurements at the High-Altitude Cloud Physics Laboratory (HACPL: 17.56�N, 73.4�E, above 1373 m MSL), Mahabaleshwar were investigated for determining the diurnality of the drop size distribution (DSD) associated with the precipitation characteristics over the Western Ghats of India. The JWD data for the period from 2015 to 2019 were collected and examined during the Indian Summer Monsoon (ISM) season. The number concentration of rain droplets of various diameters is considerably varying with the rain rate (R) and type of precipitating cloud. With increasing the value of R, rain droplets having larger diameter concentration significantly increases, and the distribution tail moves towards the biggest droplets. The average value of reflectivity (Z), R, liquid water content (LWC), mass-weighted mean diameter (Dm), and normalized intercept parameter (log10Nw) was found to be higher for the heavy rainfall (Rhigh ?10 mm h-1) as compared to the low rainfall (Rlow < 10 mm h-1) during the entire study period. The gamma distribution of DSD shows significant differences during the low and heavy precipitation on different time periods (e.g., 00-06, 06-12, 12-18, 18-23 LST). The number of rain events contributing to the total accumulated rain varies with time. The maximum number of rain events occurred during 12-18 LST, with 23.6 % rain events of low rainfall and 4.9% of heavy rainfall. The bimodality is observed in the diurnal variation of Dm, R, and Z, with the largest peak recorded in the late afternoon hour (13-16 LST) and the second crest in the early morning hour (05 LST). At the same time, the log10Nw value drops down, indicating the lowest concentration of rain droplets. � 2024 National Institute of Science Communication and Policy Research. All rights reserved.
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    Hyperspectral remote sensing: Potential prospects in water quality monitoring and assessment
    (Elsevier, 2024) Srivastava M.K.; Gaur S.; Ohri A.; Srivastava P.K.; Chaturvedi S.
    In recent decades, the field of remote sensing has made significant progress, especially in hyperspectral imaging, which has become an essential tool for civil, commercial, medical, and military applications. Hyperspectral sensors are capable of estimating physical parameters of complex surfaces and identifying visually similar materials with fine spectral signatures. This article focuses on the use of hyperspectral remote sensing, particularly in water quality assessment and monitoring. It highlights the importance of hyperspectral imageries in recent studies and discusses the working and types of hyperspectral data, as well as various space-borne and airborne sensors currently in use. Additionally, the article reviews various techniques and methods that researchers around the world have employed to use hyperspectral data for water quality applications. Lastly, the article discusses the advantages and challenges inherent in hyperspectral remote sensing. This chapter aims to serve as a comprehensive guide for those interested in hyperspectral remote sensing and its applications in water quality monitoring and assessment. � 2025 Elsevier Ltd. All rights reserved.
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    Implementing 4D seismic inversion based on Linear Programming techniques for CO2 monitoring at the Sleipner field CCS site in the North Sea, Norway
    (Springer Science and Business Media Deutschland GmbH, 2024) Singh A.P.; Maurya S.P.; Kant R.; Singh K.H.; Singh R.; Srivastava M.K.; Hema G.; Verma N.
    This article provides a comprehensive analysis of CO2 injection monitoring in the Sleipner Field. Ensuring the safe storage and containment of CO2 in geological formations or assigned storage sites, especially in the carbon capture and storage (CCS) projects. In this study, a seismic inversion method incorporating linear programming sparse spike inversion was employed to observe and analyze the CO2 plume in the Sleipner field, Norway. This approach enhances the understanding of the dynamics and behavior of the CO2 injection, providing valuable insights into the monitoring and assessment of CCS operations in the Sleipner field. The foundational dataset includes 3D post-stack seismic data from the year 1994, with special emphasis on the monitoring data collected in 1999, following four years of CO2 sequestration. The analysis utilized synthetic data to investigate alterations in seismic amplitude, highlighting that amplitude variations were more prominent compared to variations in velocity and density. The findings highlight noticeable shifts in P-wave velocity, signifying a significant 29% reduction, with the most substantial decrease occurring within the 0 to 30% CO2 saturation range. Correspondingly, density changes align with trace variations, demonstrating only a 2�3% reduction in density as gas saturation increases from 0 to 30%. Beyond 30% saturation, density exhibits a further decrease of 30%. The traces collectively reveal a consistent trend, showcasing a 32% reduction in impedance as CO2 saturation levels rise. Through the cross-equalization process, it was observed that the initial data repeatability was low, indicated by a normalized root mean square (NRMS) value of 0.6508. However, significant improvement was achieved, bringing the NRMS value to a more satisfactory level of 0.5581. This improvement underscored the alignment of features both above and below the reservoir, underscoring the efficacy of the cross-equalization technique. The outcomes of the 4D inversion provided insights into the distribution of CO2 within the reservoir, revealing upward migration. Importantly, the results confirmed the secure storage of CO2 within the reservoir, affirming the integrity of the overlying cap layer. � The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2024.
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    Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study
    (Springer Science and Business Media B.V., 2024) Singh R.; Srivastava A.; Kant R.; Maurya S.P.; Mahadasu P.; Verma N.; Hema G.; Kushwaha P.K.; Richa; Singh K.H.; Singh A.P.; Srivastava M.K.; Sarkar P.
    Understanding subterranean reservoirs, geological characteristics, fluid composition, and hydrocarbon potential strongly relies on precise reservoir characterization. Seismic inversion is a key method in reservoir characterization to approximate the acoustic impedance and porosity of underlying rock formations using seismic and well-log data. A sparse layer reflectivity (SLR) post-stack inversion method approach is used in this study to make thin layers more visible. To generate an impedance volume, it uses a predetermined wavelet library, an objective function, and a regularization parameter, the regularization parameter is a tunable parameter used to control the balance between fitting the data closely (minimizing the misfit) and ensuring a smooth and stable model for and sparseness computed coefficients. This study uses Blackfoot data to estimate the density, velocity, impedance, and porosity of a particular region using the SLR and Radial Basis Function Neural Network (RBFNN). According to the interpretation of the impedance section, a low impedance anomaly zone with an impedance range of (8500�9000) m/s*g/cc is present at a time of (1040�1065) ms. The low impedance zone is classified as a clastic glauconitic sand channel (reservoir zone) based on the correlation between seismic and borehole data. Further, a Radial Basis Function Neural Network (RBFNN) has been applied to the data to estimate porosity volume and to conduct a more thorough examination of the reservoir zone and cross-validate inverted results. The research demonstrates that the high porosity zone, low velocity, and density zone are discovered by the RBFNN technique, and the low impedance zone interpreted in inversion findings are correlating, which confirms the existence of the glauconitic sand channel. This research is crucial for understanding how well SLR, RBFNN, and multi-attribute analysis work to define sand channels. � 2024, The Author(s), under exclusive licence to Springer Nature B.V.
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    Long-Term Trend in Black Carbon Mass Concentration Over Central Indo-Gangetic Plain Location: Understanding the Implied Change in Radiative Forcing
    (John Wiley and Sons Inc, 2024) Mehrotra B.J.; Srivastava A.K.; Singh A.; Parashar D.; Majumder N.; Singh R.S.; Choudhary A.; Srivastava M.K.
    For the first-time, analysis of a decade long measurement of Black Carbon mass concentration (BC) was carried out at a representative central Indo-Gangetic Plain (IGP) location, Varanasi (25.30�N, 83.03�E, 79�m asl), from 2009 to 2021 to understand its physical, optical, and radiative impacts. During the 13-year study period, the daily BC mass concentration was found to vary between 0.07 and 46.23�?g�m?3 (mean 9.18���6.53�?g�m?3) and showed a strong inter-annual and intra-annual variations. The inter-annual variability of BC showed a significant decreasing trend (?0.47�?g�m?3�yr?1), with a maximum during the post-monsoon (?1.86�?g�m?3�yr?1) and minimum during the pre-monsoon season (?0.31�?g�m?3�yr?1). The Black Carbon Aerosol Radiative Forcing (BC-ARF) at the top of the atmosphere (BC-ARFT), surface (BC-ARFS), and within the atmosphere (BC-ARFA) was found to be 10.3���6.4, ?30.1���18.9, and 40.5���25.2�W�m?2, respectively. BC-ARF also showed a strong inter-annual variability with a decreasing trend for BC-ARFT (?0.47�W�m?2�yr?1) and BC-ARFA (?1.94�W�m?2�yr?1), while it showed an increasing trend for BC-ARFS (1.33�W�m?2�yr?1). Concentrated weighted trajectories (CWT) and potential source contribution function (PSCF) analyses were performed at the station to determine the potential source sectors and transport routes of BC aerosols. These analyses revealed that the long-range source of BC at Varanasi originates from the upper and lower IGP, central highlands, southern peninsular region, Pakistan, and even from the Central East Asia region. � 2024. American Geophysical Union. All Rights Reserved.
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    Reservoir characterisation using hybrid optimisation of genetic algorithm and pattern search to estimate porosity and impedance volume from post-stack seismic data: A case study
    (Springer, 2024) Verma N.; Maurya S.P.; Kant R.; Singh K.H.; Singh R.; Singh A.P.; Hema G.; Srivastava M.K.; Tiwari A.K.; Kushwaha P.K.; Richa
    In the current study, a seismic inversion based on a hybrid optimisation of genetic algorithm (GA) and pattern search (PS) is carried out. The GA is an approach to global optimisation technique that always converges to the global optimum solution but takes much time to converge. On the other hand, the PS is a local optimisation technique and can converge at local or global optimum solution depending on the starting model. If these two techniques are used together (here termed hybrid optimisation), they can enhance one's benefit and reduce the drawbacks of others. The present study developed a methodology to combine GA and PS in a single flowchart and utilise seismic reflection data exclusively to predict porosity and impedance volume in inter-well regions. The algorithms are initially tested on synthetically created data based on the wedge model, the coal coking model, and the 1D convolution model. The performance of the algorithm is remarkably acceptable, according to the error analysis and statistical analysis between the inverted and the anticipated results. After that, the field post-stack seismic data from the Blackfoot field, Canada, is transformed into impedance and porosity using a developed hybrid optimisation technique. The inverted/predicted sections show very high-resolution subsurface information with impedance varying from 6000 to 14000 m/s�g/cc and porosity varying from 5 to 40% in the region. The error decreases from 1.0 to 0.5 for impedance inversion, whereas it varies from 1.4 to 0.5 for porosity inversion within 3000 iterations, which cannot be achieved by a single optimisation technique. The findings also demonstrated a sand channel (reservoir) anomaly with low impedance (6000�9000 m/s�g/cc) and high porosity (12�20%) in between 1040 and 1060 ms time intervals. This study provides evidence that subsurface parameters like acoustic impedance or porosity may be promptly and affordably determined using seismic inversion based on hybrid optimisation. The developed methodology is very helpful in finding subsurface parameters in a limited time and cost, which cannot be achieved only by global or local optimisation. � Indian Academy of Sciences 2024.
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    Tree ring oxygen isotope (?18O) variations from western Himalaya and it linkage with vapor pressure and runoff water in India
    (Springer, 2024) Ram S.; Pandey U.; Srivastava M.K.
    A 242-year regional oxygen isotope (?18O) ratio has been prepared based on distinct oxygen isotopes collected from different species from different locations of the western Himalayas for a better understanding of climate fluctuations. The strongest negative correlation coefficient (-0.40) of ?18O was observed with runoff during 1902�2006. The result indicates that ?18O is controlled by runoff water from June-August. Increasing/decreasing runoff over the region may be in favour for decreasing/increasing the oxygen isotope. Whereas Vapor pressure shows a positive correlation (0.38) with ?18O from June to August across the western Himalayas. The positive relationship of ?18O with vapor pressure indicates that warmer conditions over the region may enhance the evaporation from soil moisture increasing ?18O in the water. It means that increasing/decreasing temperature might be associated with increasing/decreasing oxygen isotope (?18O) ratio in tree ring cellulose. The correlation coefficient of temperature and vapor pressure with runoff water is -0.41 and ? 0.35. Both are statistically significant at 0.1% level. � The Author(s), under exclusive licence to Indian Academy of Wood Science 2024.
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    Unveiling the elemental composition, sources and health impacts of PM10 over the central Indo-Gangetic plain (IGP) of India
    (Elsevier Ltd, 2024) Tiwari P.; Mehrotra B.J.; Gupta S.; Srivastava M.K.; Kumar M.; Vijayan N.; Choudhary A.; Sharma S.K.
    This study investigates the PM10 pollution in the central Indo?Gangetic plain (IGP) of India from February 2018 to December 2019, revealing an annual average PM10 concentration of 193�65 ?g m?�. Seasonal concentrations peaked in winter season followed by summer, post-monsoon and monsoon seasons. WD?XRF identifying 35 elements, including major elements like Si, Al, Ca, and Fe, which contributed 18% to PM10 concentrations. The enrichment factor analysis indicates that the Rb, Sr, and Na originate from natural sources, while Fe, Al, Mn, K, Ca, Mg, and Zr have both natural and anthropogenic sources. The remaining elements are primarily of anthropogenic origin. Source apportionment through Principal Component Analysis (PCA) revealed the six key PM10 sources: mixed type (dust+biomass burning, 42%), vehicular (24%), industrial (15%), combustion (7%), agricultural activities (6%), and fossil fuel combustion (6%). Local sources from the northwest (NW) and west (W) directions were dominant, with smaller contributions from trans-boundary regions like Afghanistan and Pakistan. Health risk assessments highlighted non-carcinogenic risks from Mn for adults and children, with additional risks from Al and Cr for children, and carcinogenic risks from Cr for adults. The risk of Al, Cr and Mn in Varanasi are likely driven by combustion related activities, as Cr and Mn in PM10 are commonly associated with industrial and vehicular emission sources. � 2024 Elsevier Ltd
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    Upper tropospheric moistening during the Asian summer monsoon in a changing climate
    (Springer Science and Business Media Deutschland GmbH, 2024) Singh B.B.; Krishnan R.; Sabin T.P.; Vellore R.K.; Ganeshi N.; Srivastava M.K.
    This study investigates the projected changes in the upper troposphere and lower stratosphere (UTLS) water vapor over the Asian summer monsoon (ASM) region based on satellite records, numerical simulations using variable-resolution global climate model focused over south Asia (HIST-natural and anthropogenic forcing in the historical period, and FUT-following RCP4.5�in future), and Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets. The simulations generally reproduced the seasonal cycle in the UTLS water vapor and regional water vapor maximum. With progressive warming in future, excessive upper tropospheric moistening is noted over the ASM region in far-future (2070�2095) climate against the HIST climate (1980�2005) with water vapor mixing ratio increasing to ~ 7.5 ppmv relative to ~ 5 ppmv noted in the HIST. It is further noted that projected changes in water vapor are linked to anomalous warming (~ 1�4�K) in the upper tropospheric layers juxtaposed with zonally elongated ASM anticyclone and enhanced water vapor flux divergence by amplifications in rotational winds. Further, the simulations indicate robust increase in ASM upper tropospheric water vapor as compared to those at mid- and lower- troposphere in accordance with the Clausius�Clapeyron temperature dependence of moisture response to warming and amplified troposphere warming with altitude. A simple comparison between the ASM and the entire globe indicates that upper tropospheric water vapor-temperature relationship has a similar response, however, the projected variability in temperature and moisture is significantly larger (about twice) over the ASM region highlighting strong regional influence. Nonetheless, the projections indicate that ASM is a potential regional source in modulating UTLS water vapor budget in a warming climate. � 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.