2024
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PublicationArticle Effect of Long-Term Use of Organics with Inorganic Fertilizers on Yield and Nutrients Uptake under Rice (Oryza sativa)-Wheat (Triticum aestivum) Cropping System in an Inceptisols of Varanasi(Indian journals, 2024) Munesh Kumar Shukla; A.P. Singh; P.K. SharmaThe long-term experiment has been continuing at research farm of the Institute of Agricultural Sciences, Banaras Hindu University, Varanasi since 1985-1986 on rice-wheat cropping system. Field experiment was conducted during Rabi (winter) season of 2016-17. The experiment was conducted with 12 treatments replicated three times employing randomized block design. Out of which 6 treatments comprised with integration of organics with inorganic fertilizer supply system, 4 treatments included only chemical source of nutrients, one was farmer’s practices and one control. The organic sources of nutrients included were (i) Farm Yard Manure (FYM) (ii) Green manure (GM) Sesbania aculeata (dhaincha) (iii) Crop residues (CR) i.e. wheat straw. Chemical sources of nutrients were urea, diammonium phosphate, muriate of potash as per the treatments. The recommended level of N was partially substituted by 25% or 50% N either through FYM or wheat straw or green manure. The result indicated that highest N, P, K and S content and uptake in grain and straw was recorded in plots receiving 50% NPK+50% through FYM. Treatments which included organic matter along with chemical fertilizers showed higher concentration and uptake of nutrients (N, P, K and S). Concentration of nutrient elements in straw and grain increased due to effect of organic matter in case of N, P and K but remained almost unchanged in case of S. All the organic sources were at par for grain yield of wheat. Continued use of organics with inorganic fertilizers in an integrated manner could sustain yields of wheat and maintained adequate supply of nutrients. © 2024, Indian journals. All rights reserved.PublicationArticle 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) Nitin Verma; S.P. Maurya; Ravi kant; K.H. Singh; Raghav Singh; A.P. Singh; G. Hema; M.K. Srivastava; Alok K. Tiwari; P.K. Kushwaha; Richa SinghSeismic 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.PublicationArticle Seismic Signature of the Super Cyclone Amphan in Bay of Bengal Using Coastal Observatories Operating Under National Seismological Network of India(John Wiley and Sons Inc, 2024) A.P. Singh; O.P. Mishra; Ajeet P. Pandey; Rajeev BhatlaWe examined the seismic noise data collected from coastal and inland observatories in India, affected by the super cyclonic storm Amphan in the Indian Ocean, to understand the storm dynamics. Prominent disturbances in the 0.05–0.50 Hz frequency range were observed at the seismic stations, arising due to ocean-continent interactions. The coastal stations displayed more pronounced ground motions contrary to the inland stations, with spindle-shaped seismic wave envelopes intensifying as Amphan approached. The maximum ground displacements and energy occurred hours after the cyclone's eye, with maximum wind speed, moved away from the stations and not when it was close to the station. We observed significant variations in primary (0.05–0.10 Hz) and secondary microseism (0.10–0.50 Hz) energy during Amphan's directional changes. Secondary microseisms in short and long periods were found at 0.20–0.50 Hz and 0.10–0.20 Hz, respectively. Primary microseisms exhibited a simple pattern and were the weakest among the three energy bands. The CAL seismic station's seismic wave envelope showed an en-echelon feature with increasing amplitude as Amphan approached, indicating the influence of ocean resonance and coastal wave reflection. This study demonstrates monitoring of the tropical cyclone paths based on seismic signatures obtained using microseisms recorded at seismic stations, a cost-effective tool. Integrating these seismic signals with atmospheric observations in near real-time would probably enable an effective monitoring of cyclones and timely issuance of their alerts. © 2024 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union.PublicationArticle 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) Nitin Verma; S.P. Maurya; Ravi Kant; K.H. Singh; Raghav Singh; A.P. Singh; G. Hema; M.K. Srivastava; Alok K Tiwari; P.K. Kushwaha; RichaIn 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.