July, 2026 || Volume 30 No.04
Volume 30(4) July 2026 (4-6)
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1
Shallow upper crustal structure of south Rewa Gondwana basin, Central India constrained from different geophysical studies
Sudeshna Moharana and Laxmidhar Behera
CSIR-National Geophysical Research Institute (CSIR-NGRI), Hyderabad - 500007, India Academy of Scientific and Innovative Research (AcSIR-NGRI), Ghaziabad -201002, India https://doi.org/10.71122/JIGU.30(4)2026.0024
ABSTRACT
The south Rewa basin is located in northern part of the Son-Mahanadi rift system in Central India, which has huge deposit of Gondwana rocks. The basin is affected by active tectonic activities towards north and ubiquitous presence of Deccan Traps in south, making this basin geologically complex to image the subsurface geological features. The hydrocarbon prospect of the Gondwana rocks in this sedimentary basin, has encouraged multidisciplinary geological and geophysical investigations. The upper-crustal ?-wave velocity model (??) of the south Rewa rift-basin is derived down to 10 km depth by using the 2-D traveltime inversion of both seismic refraction and long-offset reflection traveltime data along the 155 km long Hardi- Samatpur seismic profile. The model shows ?? variations of 3.2-3.5 km/s for the first-layer, which are mainly composed of complex mixtures of exposed upper-Gondwana rocks, basalts and dykes, as well as weathered rocks with alluviums. The second-layer mainly comprises high-velocity-layer (HVL) basalt with ?? varying from 4.9-5.1 km/s, corresponding to the Deccan volcanics that overly the low-velocity-layer (LVL) Gondwana rocks of velocity 4.0 km/s. The basement is highly undulated, forming horst and graben structures, having ?? variation from 5.9-6.1 km/s, showing significant upwarping on either side of the basin along the profile. The deep-basinal faults constrain the presence of Gondwana rocks below Deccan Traps in a graben structure that may have potential for hydrocarbon accumulation. The sub-basement, with ?? varying from 6.4-6.5 km/s, follows the basement geometry, showing significant upwarping towards the Narmada-Son-Lineament (NSL). The results obtained from the inversion of seismic data are further corroborated using residual-Bouguer-gravity anomaly, magneto-telluric (MT), well lithology, magnetic, and heat-flow studies over this region along the Hardi- Samatpur profile. The MT, magnetic, and gravity information suitably complement the seismic results corresponding to gravity-lows, conductive-zones, and low-velocity zones associated with deposition of the Gondwana rocks, confined by intra-basinal faults. Hence, with the help of suitable integration of different geophysical studies, we have obtained good constraints on the nature of subsurface geological features in the upper-crust in the south Rewa Gondwana sedimentary basin of Central India.
2
Hurst Exponent-driven magnetotelluric signal noise suppression using adaptive mode decomposition methods
B. Pradeep Naick*1, K. Naganjaneyulu2 and I. Santi Prabha3
1Instrumentation, Airborne & Engineering Geophysics, CSIR-National Geophysical Research Institute, Hyderabad 500 007, Telangana, India
2Magnetotellurics, CSIR-National Geophysical Research Institute, Hyderabad 500 007, Telangana, India
3Department of Electronics and Communication Engineering, JNTU Kakinada, Kakinada 533003, Andhra Pradesh, India
https://doi.org/10.71122/JIGU.30(4)2026.0025
ABSTRACT
The magnetotelluric (MT) method is a passive geophysical technique used to image the electrical resistivity structure of the Earth. MT signals are usually contaminated by various types of noise. The quality of the impedance tensor estimate is degraded by noise, which in turn influences subsurface models. In this paper, we present a noise suppression scheme based on the Hurst exponent by applying adaptive mode decomposition methods to MT signals. These methods are, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Improved CEEMDAN (ICEEMDAN). Each method decomposes the noisy MT signal into a set of intrinsic mode functions (IMFs), also called modes or components. We then compute the Hurst exponent for each mode; modes with low Hurst exponents, are considered noise-dominant, and those with high Hurst exponents, are classified as signal-dominant. The noise-suppressed or denoised signal is reconstructed by summing only the signal-dominant modes. We tested this approach on real MT field data collected from the Dharwar Craton, Karnataka State, and compared the performance of all four methods using performance indicators, which include signal-to-noise ratio and correlation coefficient. Results show that the Hurst exponent is an effective criterion for identifying and separating signal from noise in MT data. Among the four methods, ICEEMDAN provides the best noise suppression performance. This study offers a practical, adaptive approach to remove different types of noise from the MT signal.
3
Prediction of pre-monsoon temperature of Varanasi using machine learning and deep learning techniques
R. Bhatla1*, Mohini Dangi2, Aashna Verma1and Manjari Gupta3
1 Department of Geophysics, Institute of Science, BHU, Varanasi-221005, India
2 DST- CIMS, Institute of Science, BHU, Varanasi-221005 , India
3 Department of Computer Science, Institute of Science, BHU, Varanasi-221005, India
https://doi.org/10.71122/JIGU.30(4)2026.0026
ABSTRACT
Accurate short-term temperature forecasting is essential for understanding climate changes and supporting agricultural planning during the pre-monsoon period in northern India, when temperatures rise sharply. Although machine learning approaches have been applied in several regions, their performance for short-lead prediction, using long historical observations in densely populated urban areas such as Varanasi, has received very limited attention. In this study, we evaluate four widely used machine learning and deep learning models, Support Vector Regression (SVR), Random Forest (RF), Multi- Layer Perceptron (MLP), and Long Short-Term Memory (LSTM), to predict daily maximum temperature using a univariate approach over Varanasi, based on 71 years of IMD observations. The purpose of adopting a univariate framework is to keep forecasting operationally simple, while capturing the short-term evolution of temperature patterns without additional climatic variables. Our results show that SVR achieved the highest R² score (0.801) and correlation (0.897), while LSTM produced the lowest RMSE (1.673) and MAE (1.213). Overall, LSTM is considered the best performing model because the study prioritizes minimizing short-term forecasting errors, even though SVR achieves the highest explained variance. The good performance of SVR and LSTM suggests that short-term temperature forecasts can be generated reliably with limited input information, which is useful for early warnings, heatwave preparedness, and agricultural decision making during the vulnerable pre-monsoon season in the region.
4
Magnetic investigation of structural controls on groundwater contamination and flow pathways along the Musi River, Ranga Reddy and Nalgonda districts, Telangana (India)
Udaya Laxmi G1, Blessy Ganduri1, Linga Swamy Jogu*1 and Naveen Kumar Gardas2
1Department of Geophysics, Osmania University, Hyderabad-500 007, India.
2Department of Applied Geochemistry, Osmania University, Hyderabad-500 007, India.
https://doi.org/10.71122/JIGU.30(4)2026.0027
ABSTRACT
Magnetic investigations were conducted along the Musi River corridor between Peerzadhiguda and Valigonda areas, encompassing portion of the Ranga Reddy and Nalgonda districts in the state of Telangana, India, to delineate subsurface structural controls governing groundwater flow and contaminant migration. A total of 1,260 magnetic measurements were acquired along twelve traverses at 100 m station intervals and processed using magnetic anomaly mapping, Reduction to the Pole (RTP), and Analytical Signal (AS) techniques. The magnetic anomaly analysis identified seven prominent magnetic highs and four magnetic lows, indicating structurally controlled subsurface heterogeneity. Radial Average Power Spectrum (RAPS) analysis revealed three characteristic depth interfaces at approximately ~0.1 km, ~0.5 km, and ~1.6 km corresponding to weathered, semiweathered and fractured granitic-gneissic basement. Two-dimensional magnetic modelling further constrained shallow structural bodies with depths ranging from ~0.12 – 0.22 km and lateral extents up to ~2 km, indicating broader and more complex fractured zones in the downstream sector. An integrated structural map revealed dominant NE–SW, NW–SE, and N–S lineament trends, with comparatively greater structural complexity in the downstream sector. The spatial correspondence between magnetic gradients, mapped geological structures, and modelled bodies indicates that faults, dykes, and fracture networks, act as preferential pathways of controlling groundwater flow and facilitating contaminant migration along the Musi River corridor.
5
Hydrothermal origin of pyrite in the Zawar Pb-Zn deposit, Aravalli Craton: Insights from trace element geochemistry and supervised machine learning algorithms
Sima Gorai*, Bulusu Sreenivas and T. Vijaya Kumar
CSIR-National Geophysical Research Institute, Uppal Road, Hyderabad 500007, India.
https://doi.org/10.71122/JIGU.30(4)2026.0028
ABSTRACT
The application of machine learning algorithms on geochemical datasets is emerging as a powerful tool for characterising ore deposits. In this work, we integrate the trace element geochemistry of different generations of pyrite with machine learning algorithms to understand the nature of ore deposition in the Zawar Pb–Zn deposit. Pyrites from Zawar have long been debated to have either sedimentary exhalative (SEDEX) or hydrothermal origins, making them ideal for testing classification approaches. We framed this as a binary classification problem and employed three ML algorithms, Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), well-suited for recognising subtle geochemical patterns. A global dataset comprising 727 SEDEX and 577 hydrothermal pyrite samples, characterised by trace elements, such as Co, Ni, Cu, Zn, As, Ag, Sb, and the Co:Ni ratio, provided the training base for these models. Applying the trained models to in-situ Laser Ablation–Inductively Coupled Plasma–Mass Spectrometry (LA-ICP-MS) data from Zawar pyrites, yielded high accuracies: 97.24% for RF, 96.93% for GB, and 96.63% for AB. The classifications overwhelmingly support a hydrothermal origin, for the deposit. These results not only validate earlier geological interpretations but also demonstrate the integrated use of oregeochemistry on machine learning for understanding the ore-forming processes, ultimately strengthening exploration techniques and mineral deposit models in economic geology
6
Tree ring-width study of conifers from the western Himalaya (India) and its relationship with climate fluctuations
Somaru Ram1*, N. Bharti1, V.S. Parvathy1, B. Preethi1 and Manoj K. Srivastava2
1Centre for Climate Change Research, IITM, Ministry of Earth Sciences, Pune - 411008
2 Department of Geophysics, Banaras Hindu University, Varanasi-221005, India
https://doi.org/10.71122/JIGU.30(4)2026.0029
ABSTRACT
Tree-ring width studies have been carried out in different locations of the western Himalayas in relation to climate variability. The significant relationship between tree ring chronology and monthly climate variable, indicates that rising potential evapotranspiration over the region, may lead to insufficient moisture supply, resulting in increased moisture stress conditions over the region for trees, whereas, increased rainfall and frequent wet day frequency may work as a booster to sufficient moisture supply which promotes tree growth during the subsequent growing season. The study highlights an important role of spring season moisture availability in the development of annual ring width. The analysis of the relationship between tree growth and climate shows that the potential evapotranspiration (PET) over the region, has a stronger limiting impact on the development of tree growth than the temperature.
7
Study of air quality and aerosol over Indian region: A case study from COVID-19 period
Km Swarnima1 and Sanjay Kumar1&2*
1Department of Physics, Nehru Gram Bharti University, Kotwa-Jamunipur-Dubawal, Prayagraj-221505, India
2*Department of Physics, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur-273009, India
https://doi.org/10.71122/JIGU.30(4)2026.0030
ABSTRACT
Pandemic from CoronaVirus Disease 2019 (COVID-19, caused by SARS-CoV-19), remains a serious hazard to human health and life, which also led to significant economic losses across the world. In some cases, it resulted in loss of a large number of human life. The associated ambient air pollutants: (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO), black carbon (BC) and particulate matter (PM), are directly linked to the enhanced risk of stroke, heart disease, asthma, and lung cancer. In order to h ave quantitative estimate, a comparative study of these pollutants, aerosol optical depth (AOD), surface temperature, ozone, carbon monoxide (CO) and NO2, aerosol size distribution over India, have been analysed for the months March- June during the lockdown period of 2020 which are compared with the averages during 2015-2019. The aerosol optical depth (AOD) from MODIS satellite, show a decrease in AOD during the lockdown period by 40% over the Indian region, compared to 5-year mean level (2015-2019), whereas the ground based AOD from AERONET (Aerosol Robotic Network), was reduced to 75 % at Kanpur, and 74 % at Gandhi College (Ballia) in India. The drop in AOD observed in lockdown, is a clearcut indication of reduced level of air pollution. Peak of aerosol size distribution over Kanpur and Gandhi College, has also been analysed which show a reduction by 33% to 50 % from the average level. Enhancement in total ozone column of ~8% from the average level, is noticed during the lockdown period, which is attributed to the suppression in NO2 and CO concentration that are supposed to destroy the formation of ozone through chemical reactions. The black carbon (BC) oncentration during the lockdown period, also got reduced and found maximum at New Delhi (80%). The comparative analysis of other pollutants between all the three cities of India is also made. The decrease in temperature during the lockdown over New Delhi, was found to be greater than that observed over Kanpur and the Gandhi College in Ballia.
8
Impact related deformation within and around the proposed Kaveri Crater, southern India.
A701, Century Central Apartments, Kanakapura Main Road, Bengaluru- 560111, India.
https://doi.org/10.71122/JIGU.30(4)2026.0031
ABSTRACT
Occurrence of an extraterrestrial impact crater, named as Kaveri crater, has been proposed from the southern Indian peninsula. This crater, with a diameter of about 120 km, could be the fourth largest on the surface of the Earth. Initial findings regarding this crater have already been published (Subrahmanya and Prakash Narasimha, 2017). In the present work, we provide additional primary and supporting evidences. Shatter cones are observed within the crater, while the radial and concentric fractures are present outside the crater rim. Besides, Planar Deformation Features are found to be present in quartz and plagioclase. Indirect evidences point its formation around Neoproterozoic – Cambrian boundary.