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In the E&P industry, accurate lithology classification is an essential task for successful exploration and production. Geophysical logs provide high-resolution petrophysical properties, but core logging is expensive and traditional techniques may not accurately classify lithologies. We demonstrated a comparative analysis of six ML algorithms: k-nearest neighbor (kNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN) for the prediction of lithologies from geophysical logs. Here we analysed the wireline logs of eight wells associated with the petroliferous Lakadong-Therria formation of the Bhogpara oil field of the Assam-Arakan Basin. This formation contains eight typical lithologies, namely clay stone, sand stone, calcareous sandstone, shale, calcareous shale, carbonaceous shale, coal and limestone. Performance of the ML algorithms were evaluated through accuracy, precision, recall, F1-score and receiver operating characteristic (ROC) curve. During the training and test phases, the computed overall accuracy of the predicted ML modes exceeded 82% and 71%, respectively. The model accuracy hierarchy was ANN > XGBoost > RF > SVM > DT > kNN during training, and ANN/XGBoost > kNN > DT/RF > SVM during testing. This approach allows interpreters to select the most accurate ML model based on training phase performance. This study provided a clear insight towards generating a supplement for litholog sequence and improving the accuracy and efficiency of lithology prediction in a geologically complex petroleum reservoir using pre-received core derived litholog information at few wells.

Wadia Institute of Himalayan Geology (WIHG), Dehradun is a premier Geological institute involved in both basic and applied research to unravel the Geodynamics of the mighty Himalaya, which covers a wide spectrum of Geoscientific disciplines: petrology, geochemistry, structural geology, geophysics, sedimentology, biostratigraphy, earthquake geology, geomorphology, environment & engineering geology, quaternary geology, hydrology, glaciology, etc. The state-of-the-art sophisticated analytical laboratories strongly substantiate the field data for understanding the geodynamic evolution of the Himalaya, seismogenesis of the region, studying landslides and avalanches, characterization and mitigation of geohazards related to earthquakes, landslides, snow/ice avalanches, glacier/landslide lakes outbursts, exploration of natural resources (minerals/ore bodies, hydrocarbons, springs, geothermal, etc.), comprehending glacier dynamics and fluvial systems, etc. Additionally, sub-surface features such as crustal heterogeneities, accumulation of elastic strain and convergence rate, crust-mantle interaction, and shallow/deep earth processes are also being probed. Besides investigating basic scientific issues, the Institute provides geoscience support to other government agencies/bodies in understanding and mitigating several hazards-related programs like landslides, avalanches, earthquakes, and floods in the Himalaya. Research activities during 2020–2023 are centered on the major thrust area of “Characterization and Assessment of Surface and Subsurface Processes in Himalaya (CAP-Himalaya): Implications on Geodynamics, Seismogenesis, Bioevents, Paleo-climates, Natural Hazards, and Natural Resources for Sustainable Development”. The research program of the CAP Himalaya is accomplished through different activities. The major achievements in each activity are highlighted here.

Cryosphere all over the globe is thawing, be it the Arctic, Antarctic, Greenland or Himalaya. Studies have predicted that loss in the glacier mass over the Upper Indus Basin in Kashmir Himalaya would amount to 47 to 67% (Romshoo in 7th conference of Science and Geopolitics of Arctic and Antarctic, 2023). A similar trend prevails in the Bhaga basin of the Upper Chenab, where glaciers in size classes < 0.5 km2and 0.5–1km2show a higher relative loss of 25% (0.5% year−1) and 13% (0.3% year−1) respectively (Das et al. in Quat Sci Rev 316:108258; J Mt Sci 20:299–324, 2023). The loss in Eastern Himalaya (Sikkim) has been reported to range from 20 to 30% (Debnath in 7th conference of Science and Geopolitics of Arctic and Antarctic, 2023). On the basis of the compiled records of snout fluctuations of 285 glaciers and two regional means spanning 17 decades from the 1850s, it has been deduced that most of the Himalayan glaciers are retreating, and the retreat rates have accelerated in the past few decades, but the observed tendencies are not regionally uniform (Kulkarni et al. in Water Security 14(6557):100101; J Indian Soc Remote Sensing 49(8):1951–1963, 2021). The excessive ice mass loss has resulted in the formation and/or expansion of glacial melt water lakes posing a threat of Glacial Lake Outburst Flow (GLOF). The results of the studies on the response of climatic variability over the state of cryosphere, the role of debris on the melting of glaciers, changes in the winter precipitation pattern and consequent impact on the hydrological cycle, water availability and state of permafrost over Himalaya have also been a focus of studies, including developing models (Banerjee et al. in Geophys Res Lett 49:096989, 2020), in contemporary publications, enriching the data on the fundamental aspects of glacial processes that are of great societal relevance.

A carbonate build-up or reef is a thick carbonate deposit consisting of mainly skeletal remains of organisms that can be large enough to develop a favourable topography. Delineation of such geologic features provides important input in understanding the basin's evolution and petroleum prospects. Here, we introduce a new attribute called the Reef Cube (RC) meta-attribute that has been computed by fusing several other seismic attributes that are characteristics of the reef through a supervised machine-learning algorithm. The neural learning resulted in a minimum nRMS error of 0.28 and 0.30 and a misclassification percentage of 1.13% and 1.06% for the train and test data sets. The Reef Cube meta-attribute has efficiently captured the anatomy of carbonate reef buried at ∼450 m below the seafloor from high-resolution 3D seismic data in the NW shelf of Australia. The novel approach not only picks up the subsurface architecture of the carbonate reef accurately but also accelerates the process of interpretation with a much-reduced intervention of human analysts. This can be efficiently suited for delimiting any subsurface geologic feature from a large volume of surface seismic data.

Empirical mode decomposition (EMD) is an effective tool for signal analysis that splits the data into individual modes, called the intrinsic mode functions, which are associated with symmetric and narrow-band waveform ensuring that the instantaneous frequencies are smooth and positive. However, some negative features encumber its direct application namely the mode mixing and splitting, aliasing and endpoint artefacts. Two variants, ensemble EMD (EEMD) and complete ensemble EMD (CEEMD) have been recently introduced to overcome these problems. We intend to show the application of the EMD for demarcating the zones of gas-hydrates and free-gas bearing sediments. Gas-hydrates are ice-like crystalline substances that occur in shallow sediments along the outer continental margins and in the permafrost regions, and are considered as viable major future energy resources of the world. Gas-hydrates in marine environment are generally identified by an anomalous reflector, known as the bottom simulating reflector, on seismic section. The present study demonstrates that the EMD can be effectively utilised in demarcating the zones of gas-hydrates and free-gas bearing sediments with a field example in the Mahanadi basin of the eastern Indian margin.

Petrophysical properties are the least explored parameters in the Mahanadi basin in the eastern Indian margin. We have used 2D seismic reflection data along a profile AC with available well logs of the Expedition-01 of the Indian National Gas Hydrates Program (NGHP) for the evaluation of lateral variation of porosity – a petrophysical property. The seismic line passes through the Sites NGHP-01-19B in the AB section, and NGHP-01-09A in the BC section. The porosity is estimated using two different approaches based on acoustic impedance (AI) transformation and direct seismic inversion. The AI transformation approach fails to decipher consistent porosity distributions whereas the direct seismic inversion approach provides satisfactory results along seismic line AC (AB and BC) in the Mahanadi Offshore region. The AI transformed porosity section illustrates erroneous porosity values within the target zone, irrespective of a strong coefficient of determination (R2) of 0.82 and 0.85 between AI and porosity at sites NGHP-01-19B and NGHP-01-9A, respectively. The accuracy of the porosity results obtained from the direct inversion approach depends on the best linear regression fit established between acoustic and porosity reflectivity-based models with a coefficient of determination as 0.91 at Site NGHP-01-19B and 0.94 at Site NGHP-01-9A. The porosity distributions are also corroborated with the density-porosity log values, and it is observed that porosity from direct seismic inversion is consistent with the porosity log information at respective sites. The porosity results at the two sites signify the overall good performance of the direct seismic inversion method and also indicate that the probability of free-gas occurrences in shallow sediments is higher compared to that of gas-hydrates.

The Indo-Gangetic Plain (IGP) is characterized by thick sediments, predominantly comprising alluvial deposits, which can amplify seismic waves generated by earthquakes in the Himalayan region located to the north of the plain. The presence of loose sediments can indeed pose significant seismic hazards, mainly due to phenomena like soil liquefaction. These sediments pose a threat to densely populated Delhi and NCR regions, which are 200 km away from the plate boundary of India and the Eurasian plate. Scientists are concerned about people’s safety in mitigating damage caused by high-rise buildings and loose sediments in the IGP region. Reliable knowledge of the sedimentary layer’s thickness and velocity structure is crucial for investigating buried active faults, understanding significant destruction, and risk assessment. Sedimentary basins are also crucial for geo-resources such as hydrocarbon and geothermal energy. This research estimated the structure of the sedimentary layer beneath four stations in the Chandigarh–Ambala region in IGP using the high-frequency receiver function (PRF) technique. The study found that the sedimentary layer thickness varies significantly, with values from 2.0 to 3.0 km beneath the IGP and increasing northward. Shallow shear velocity (Sv) in the column of sediments below the Siwalik Himalaya ranges from 2.8 to 2.9 km/s, which can be utilized for assessing earthquake ground-motion sites. The study provides new perceptions of the geodynamic processes and seismotectonic structure of the Himalayan region, allowing for better identification of the earthquake hypocenter and assessment of seismic hazards. The shear wave velocity models estimated from this research can also be beneficial for assessing seismic hazards and earthquake-resistant construction. Estimates of the crustal thickness values from waveform inversion of the PRF at individual stations reveal that the Moho depth varies between 44 and 50 km in the Indo-Gangetic Plain. From Siwalik Himalaya to the higher Himalaya, it ranges from 44 to 65 km. The depth of Moho increases from the Indo-Gangetic plain towards the lesser Himalaya.

This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This method assesses persistence and allows for combinatorial probability estimations such as initial and transitional probabilities. The hydrologic data was generated (in-situ) and received from Uttarakhand Jal Vidut Nigam Limited (UJVNL), and meteorological data was acquired from NASA's archives MERRA-2 product. A total of sixteen years (2005–2020) of data was used to foresee daily Precipitation from 2020 to 2022. MERRA-2 products are utilized as observed and forecast values for daily Precipitation throughout the monsoon season, which runs from July to September. Markov Chain and Long Short-Term Memory (LSTM) findings for 2020, 2021, and 2022 were observed, and anticipated values for daily rainfall during the monsoon season between July and September. According to test findings, the artificial intelligence technique cannot anticipate future regional meteorological formations; the correlation coefficient R2is around 0.12. According to the randomly verified precipitation data findings, the Markov Chain model has a success rate of 79.17 percent. The results suggest that extended return periods should be a warning sign for drought and flood risk in the Himalayan region. This study gives a better knowledge of the water budget, climate change variability, and impact of global warming, ultimately leading to improved water resource management and better emergency planning to the establishment of the Early Warning System (EWS) for extreme occurrences such as cloudbursts, flash floods, landslides hazards in the complex Himalayan region.

Porosity is an important petrophysical parameter that determines the amount of fluid, including oil, water, and gas contained within the rock. In petroleum exploration, it plays a crucial role in reservoir characterization, petrophysical studies, and geological analysis. However, in practice, porosity logs do not enumerate in the recorded logs for the entire well, either due to instrumental error or borehole environmental issues. Thus, the prediction of the porosity logs is essential to perform better petrophysical analysis and reservoir characterization. In the present study, we emphasize the implementation of machine learning (ML) approaches in predicting porosity logs at the missing data interval. Here, we predicted the porosity log from the sonic, bulk density, and gamma log. Initially, using the existing data from the well, we prepared the training sample. Further, using the extreme gradient boosting (XGBoost) and random forest (RF) ML approaches, we obtained the mapping function between the input logs (bulk density, gamma, and p-wave velocity logs) and output log (neutron porosity) of the training sample. Subsequently, using the mapping functions of the trained RF and XGBoost models, we obtained the porosity at the test wells at the missing data intervals. The outcome of the present study is satisfactory enough as we have seen the correlation coefficient values of the actual and predicted porosity vary between 0.8 and 0.9. Thus, ensemble machine learning approaches could provide other petrophysical parameters at the missing data interval.

Abstract: Identification and characterisation of reservoir facies is a prime factor in delimiting the hydrocarbon potential zones of a reservoir for hydrocarbon exploration. The geophysical logs, which are physical parameters of reservoir facies measured in the vicinity of boreholes, play a crucial role in the interpretation of reservoir facies. The present study deals with the identification of the lithology of the Limbodara oil field in the Cambay basin using machine learning (ML) techniques on geophysical logs. The supervised techniques of machine learning, such as support vector machines (SVM), artificial neural networks (ANN), and k-nearest neighbours (kNN), are used as nonlinear classifiers for the identification of lithology from nonlinear geophysical logs. The hyperparameters of the ML model are optimised using the grid search cross-validation (CV) method to increase the performance of the model, as evaluated by confusion matrix, area under receiver operating characteristics curve (AUC), precision, recall, and F1 score. The ML model used five geophysical parameters of two wells with four known distinguished lithologies (Class-A, Class-B, Class-C, and Class-D) for optimisation and training of the model. The optimised and trained model for each lithology for kNN, SVM, and ANN shows an overall correct prediction of true values with 85.4, 87.0, and 88.9%, respectively, from the confusion matrix. Apart from this, the receiver operative characteristics (ROC) also show that the overall area under the curve for each lithology is greater than 90%, and other evaluation parameters such as precision, recall, and F1 score show accuracy greater than 84%, except for the cases of Class C and Class D from SVM and ANN. Thus, the accuracy of each model from evaluation parameters suggests that the combined analysis of different ML models offers to select the optimised ML model for better results and validation to achieve and model the lithology with better precision. Highlights:

A way out for obtaining litholog supplements at uncored section in boreholes

Established ML assisted mapping function between wireline logs and lithologs

Predicted litholog sequence with secure level of accuracy (>80%)

•Monitoring glacier changes and associated hazards is crucial in the Himalaya.•Topography and climate control the distribution of snow cover and rainfall.•Real-time acquisition of hydro-meteorological and seismic data is important.•Integration of multiple data sets are required for the development of IMWS.•Development of IMWS for HEPs in headwater streams is essential in the IHR.

The present work demonstrates a case study in the Mahanadi offshore basin in the eastern margin of India, where gas hydrates have been delineated by seismic experiment, mainly by identifying a bottom simulating reflector or BSR on seismic section, based on its characteristic features. The BSR represents an acoustic impedance contrast between the high-velocity gas hydrates bearing sediments above and low-velocity gas-bearing or water-saturated sediments below. At some places along the continental margins of the world, the BSR has not been identified but gas hydrates were recovered by drilling, whereas at some other places BSR was identified but no gas hydrates were found. Thus, we need to find out an attribute that can be used to characterise gas hydrates and free gas reservoirs. The purpose of this work is to demonstrate this through spectral decomposition in the time–frequency domain. The study based on continuous wavelet transform (CWT) approach, which uses a mathematical mother wavelet, shows the illuminated zone with higher energy low-frequency anomaly, associated with free gas below the BSR. The study demonstrates that the CWT technique provides higher spectral-spatial resolution than that obtained by the short-term Fourier transform technique.

Abstract: The seismological data recorded at our four semi-permanent broadband seismographs installed by the CSIR-NGRI, Hyderabad, Telangana in the Aravalli mobile belt (AMB) and Rajasthan craton in the northwest Indian shield during the period 2014–2016 are used in the study. The 16 selected regional Indian earthquakes of Mw5.5–7.8 that occurred in the North India region recorded at the four seismic stations are used to compute the surface wave (both Rayleigh and Love waves) group velocity dispersion characteristics and average 1-D regional shear wave velocity (Vs) structure beneath the North India which covers the northern part of India between the whole Himalayan region and Rajasthan craton including AMB. First, we compute Rayleigh waves (at 7–87 s) dispersion curves and Love waves (at 7–82 s) group velocity dispersion curves by utilizing the standard frequency time analysis (FTAN) of 3-component broadband data recorded at these four 3-component broadband seismic stations. After that, using the logarithmic stacking technique, we compute the final average stacked fundamental mode dispersion curves of surface waves (both Love and Rayleigh waves) for our study area. Then, we finally invert the final average dispersion curves of surface waves (both Love and Rayleigh waves) to compute the average one-dimensional regional shear velocity structure beneath North India. Our modelling result in North India suggests a two-layered crustal structure with a crustal thickness of 39 km. The thickness of the first crustal layer (upper crust) is 20 km with a shear wave velocity (Vs) of 3.30 km/s. On the other hand, the second crustal layer (lower crust) marks a 19-km thickness with a Vsof 3.70 km/s. Our modelling result suggests that the Vsof the upper mantle is found to be 4.35 km/s below the study region. Research highlights:

Modelled average one-dimensional regional Vsstructure below northern part of India.

A 2-layered crustal velocity model below North India.

Upper crust at 20 km thick with Vsof 3.30 km/s.

And, lower crust at 19 km thick with Vsof 3.70 km/s.

The Moho depth is 39 km and Vsbelow the Moho depth is found to be 4.35 km.

•Evaluates the tectonic subsidence history of the Upper Assam Basin.•Elucidates the growth and evolution of the basin through Cenozoic time.•Deciphers the tectonic controls that the basin experienced over its lifespan.

The estimation of accurate reservoir parameters is essential for conventional and non-conventional hydrocarbon prospects. An artificial neural network has been developed to predict the reservoir parameters (porosity and saturation of gas hydrates) in a silty-sand, sandy-silt and pelagic-poor clay reservoir at two neighbour wells using the petrophysical information at another well in the Krishna–Godavari basin. The well log data were acquired during the Expedition-02 of Indian National Gas Hydrates Program (NGHP Exp-02). The estimation of gas hydrate saturation using Archie’s equation may be erroneous, as it is valid for the quantification of conventional hydrocarbons in the clean sand reservoir. Since the study area is clay dominated, it is subjective to adjust Archie’s exponents so that it matches with the saturation, measured from the core data. To overcome this problem of estimating the reservoir parameters in such a scenario, first of all we have derived porosity from the density log data and estimated saturation by employing modified Archie’s equation to the resistivity log data at one well. In order to train the network, the log data at one well are taken as inputs and corresponding porosity and saturation are taken as outputs. The reservoir parameters are then predicted at two neighbour wells using the wireline log data as input in those two wells. The predicted porosity and saturation of gas hydrates are alike to the traditionally estimated porosity and saturation at the neighbour wells. The predicted porosity in the studied region varies between 33 and 76%, whereas the saturation of gas hydrates ranges between 3.39 and 86.92%. This shows that the designed network can be used to estimate the reservoir parameters directly from the well log data in the same reservoirs.

The Himalayan region witnesses several natural hazards like earthquakes and landslides due to the continental collisions between the Indian and Eurasian plates. This has given rise to extreme topographic variations throughout the Himalayan belt. The Kumaun–Garhwal region is a classic example of such geological consequences and is prone to several earthquakes. High-quality three-component teleseismic waveform data recorded at seven seismological stations operated by the Wadia Institute of Himalayan Geology (WIHG) are used to investigate the detailed subsurface structure of the crust, the intra-crustal low-velocity layer (LVL), and the upper mantle discontinuities beneath the Kumaun–Garhwal, north-west Himalaya. The results, derived from the inversion of individual station's stacked P-receiver functions (PRFs) using the neighbourhood algorithm approach, show that the crustal thickness varies from 44 to 54 km beneath the study region. The depth of LVL observed beneath six stations from individual and stacked PRFs, varies from 9 to 24 km. The LVL zone with a high Vp/Vsratio may be due to fluid or partial melt, leading to shallow seismic activity within the study region. The presence of fluid or partial melts in the LVL may be due to the shear heating, cooling, and decompression. The 2D PRF migration image depicts a thick mantle transition zone due to the elevated 410 km discontinuity with respect to the global average values predicted by the IASP91 velocity model. The present research suggests that this might be due to the colder transition zone in this region, indicating the cool underthrust Indian plate with respect to the ambient mantle has reached down to the upper mantle transition zone.

Abstract: During 2017–2019, a temporary seismic network of fifty-five 3-component broadband seismometers was installed by CSIR-NGRI, Hyderabad, Telangana, in the Uttarakhand Himalayan region, India. We used digital waveform data of 21 local earthquakes of Mw2.9–4.3 from nineteen 3-component broadband seismic stations of the above seismic network for computing source parameters of local earthquakes, through simultaneous inversion of shear wave (S-wave) spectra. Here, we used the ωsquare circular source spectral model of Brune (1970) to formulate the iterative inversion scheme to compute earthquake source parameters. The modelled corner-frequency (fc), source radius (r), seismic moment (Mo) and stress-drop (Δσ) vary from 2.4 to 4.0 Hz, 247–693 m, 2.4 × 1013to 6.6 × 1015N-m, 0.2–5.4 MPa, respectively. The maximum stress-drop (Δσmax) is computed to be 5.4 MPa for an event of Mw4.3 while the minimum stress drop (Δσmin) is computed to be 0.2 MPa for an event of Mw2.9. The scaling relations between Moand fcis obtained as Mo= 3.2E + 17fc−5.5, while Moand Δσare modelled to be related as Mo= 4.59E + 13∆σ2.5. This relationship could be very useful for the future seismic-hazard assessment of the Uttarakhand Himalayan region. Research Highlights:

Modelled source parameters of local earthquakes and scaling relationship in the Uttarakhand Himalayan region.

Local earthquakes magnitude range of 2.9–4.3.

M o, r, fc, and ∆σare 2.4×1013–6.6×1015N-m. 247.0–693.0 m, 2.4–4.0 Hz, 0.2–5.4 MPa, respectively.

M o f c 5.5 $\propto$ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\propto$$\end{document} constant & Mo∆σ2.5$\propto$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\propto$$\end{document}constant.

The study investigates a shallow ice core (IND–25/B5) drilled near Humboldt Mountain in Dronning Maud Land (DML) region, during the 25th Indian Antarctic Expedition (2005-2006), to understand the variability in microparticle input to the region, and their characterization under the Scanning Electron Microscope-Electron Dispersive Spectroscopy (SEM-EDS). Also, the volcanic chronology of the ice core was established using the presence of volcanic shards at various depths of the ice core. The results suggest that the dust input to the study area has drastically increased since 1980, indicating changes in the atmospheric circulation pattern, local environmental conditions, increased global aridity and/or expansion of the ice-free oasis in the DML region. Silica (Volcanic and Mineral dust), Carbon (Calcareous and Organic) and other microparticles are three major types of particles observed in the ice core. Volcanic ash microparticles are observed at various depths, which depict glassy structures with conchoidal fractures and high SiO2concentrations (>50%). The major volcanic events traced include Krakatoa, Indonesia (2001); Mount Pinatubo, Phillipines (1991); Mount Agung, Indonesia (1963) and Cerro Azul, Chile (1916-1929).

Stable isotopes of benthic foraminifera have widely been applied in micropalaeontological research to understand vital effects in foraminifera. Isotopic fractionations are mainly controlled by ontogeny, bottom/pore water chemistry, habitat preference, kinetic effect and respiration. Discontinuous abundance of a species for isotopic analysis has forced us to select multiple species from down-core samples. Thus standardisation factors are required to convert isotopic values of one species with respect to other species. The present study is pursued on isotopic values of different pairs of benthic foraminifera from the Krishna–Godavari basin and Peru offshore to understand habitat-wise isotopic variation and estimation of isotopic correction factors for the paired species (Cibicides wuellerstorfi–Bulimina marginata, Ammoniaspp.–Loxostomum amygdalaeformisand Bolivina seminuda–Nonionella auris). Infaunal species (B. marginata, Ammoniaspp. and N. auris) show a lighter carbon isotopic excursion with respect to the epifaunal to shallow infaunal forms (C. wuellerstorfi, L. amygdalaeformisand B. seminuda). These lighter $$\updelta ^{13}$$ δ13 $$\hbox {C}$$ C values are related to utilisation of $$\hbox {CO}_{2}$$ CO2 produced by anaerobic remineralisation of organic matter. However, enrichment of $$\updelta ^{18}$$ δ18 $$\hbox {O}$$ O for the deeper microhabitat (bearing lower pH and decreased $${\hbox {CO}_{3}}^{2-})$$ CO32-) is only recorded in case of B. marginata. It is reverse in case of N. aurisand related to utilisation of respiratory $$\hbox {CO}_{2}$$ CO2 and internal dissolve inorganic carbon pool. Estimation of interspecies isotopic correction factors for the species pairs ($$\updelta ^{13}$$ δ13 $$\hbox {C}$$ C of C. wuellerstorfi–B. marginata, L. amygdalaeformis–Ammoniaspp., N. auris–B. seminuda) and $$\updelta ^{18}$$ δ18 $$\hbox {O}$$ O of C. wuellerstorfi–B. marginataare statistically reliable and may be used in palaeoecological studies.

•δβMin the Gujarat region varies from ∼0.15 to 0.86 km/s.•The lowest values of δβM(∼0.15 km/s) coincide with lower crustal seismicity.•Higher values of δβMascribed to the pockets of trapped magmatic material or fluid.•Mafic/ultramafic rocks with fluid filled fractures correlated to seismicity.

Abstract: Horizontal stresses are key parameters of reservoir geomechanics and wellbore stability modeling. For scientific well drilling, where direct measurements are not always available, modeling of horizontal stresses is challenging, especially for highly porous un-compacted gas hydrate-bearing sediments below the seafloor. We have estimated the minimum (Sh) and maximum (SH) horizontal stresses by using rock poro-elastic models based on three wells data which are situated at the national gas hydrate program (NGHP)-01 sites of the offshore Mahanadi basin. The stress magnitudes are validated by wellbore breakout. We have computed the stress magnitudes using 2D seismic for mapping the gas hydrate-bearing sediments. The average gradients of SHand Sh(10.58 MPa/km and 10.48 MPa/km) are less than the gradient of vertical stress, SV(10.67 MPa/km). The present-day stress distribution at the NGHP-01 site is principally a normal faulting (SV> SH> Sh) regime as obtained from stress polygons. The breakouts identified from both formation image and caliper data, suggest a NNW-SSE orientation for SHin the Pleistocene age, which is slightly anti-clock wise relative to the northward oriented of the Indian sub-continent. This change in the orientation of SHcould be due to a local structure/fault system cross-cutting the bottom simulating reflector, and mass sliding/slumping on the seafloor. The orientation of SHvaries from N11.25°W to N25.7°W of D-quality. We have analysed wellbore stability using the Mohr–Coulomb circle and hoop stress techniques. These results will enable numerical modeling of production from gas hydrate reservoirs planned for the future. Article Highlights: Identification of breakout from formation image and caliper log data and hence, orientation of horizontal stress in the gas hydrates-bearing sediments of the offshore Mahanadi basin. Magnitude of the maximum horizontal stress at breakout intervals and the continuous profile of the horizontal stresses using both well data at NGHP-01 sites and multi-channel seismic data. Analysis of stress polygons, Mohr circles and hoop stress distributions at the selected depth intervals near the gas hydrate-bearing sediments.

Wide-angle reflections are now routinely recorded in high resolution explosion seismics to study the crustal structure. Use of Dix's hyperbolic approximation to the nonhyperbolic wide-angle reflection travel times causes major errors in the determination of interval velocities and layer thicknesses of a stack of horizontal velocity layers. Here we propose a layer stripping method to directly calculate the interval velocities and layer thicknesses in a vertically heterogeneous earth from the strong and reliable wide-angle reflected events. Synthetic reflection travel times, at wide-angle range, for a given velocity model, contaminated by some random errors, have been used to demonstrate the reliability of the algorithms to determine the interval velocities and thicknesses of various layers. The method has also been tested on two field examples along two deep seismic sounding (DSS) profiles with well identified wide-angle reflection travel times, which illustrates the practical feasibility of the proposed method.

The necessary condition for the seismic refraction method to succeed is that the refracted first arrivals from each layer in a multilayered earth system should be detected on a seismogram as first arrivals, and this is possible only when velocities of all underlying layers are successively greater. The usual procedure to interpret the refraction travel times is to fit such a data set with several intersecting straight lines by employing a visual technique which may lead to errors of subjective judgment, as the velocity model depends on the selection of various line segments through the data. To remove the visual fit we propose here a layer stripping method based on minimum intercept time, apparent velocity, rms residual, and maximum data points by least-squares fitting to yield several intersecting straight lines. Once data are segmented out, the conventional equations can be used to determine the velocity structure.

Identification of thin stratigraphic beds is often dealt with using the scale of core data analysis. The downhole wireline log data also provide accurate information on the characteristics of lithological boundaries. In the present study, we have used a Walsh low pass filter and bed boundary detection algorithm to develop an automated bed boundary identification approach. Initially, we ran a Walsh low pass filter against the wireline log responses. Afterwards, a bed boundary detection algorithm has been applied to the Walsh low pass filtered version of the wireline log data. The Walsh domain filter creates a stepped version of the well log data having a constant step width over the entire low pass version of the log signal. As the Walsh function basically represents the binary waveforms, the Walsh low pass filtering can appropriately identify the thin lithological units within a complex succession of sedimentary strata. Further, the proposed approach is an efficient way of resolving and understanding subsurface inhomogeneity from wireline log data. The feasibility of the technique is successfully tested on the downhole wireline log data acquired from the Krishna–Godavari basin during the Expedition 02 of Indian National Gas Hydrate Program (MGHP).

Wide-angle seismic reflection traveltimes generated by large explosions are used extensively to obtain both velocity and regional structure of the crust and upper mantle. The method most commonly used to calculate interval velocities and thicknesses (layer parameters) of a multilayered earth, supposed to consist of homogeneous, isotropic and horizontal layers, is based on Dix's (1955) interval velocity formula, which needs a prior estimate of the rms velocity and traveltimes at zero offset. Use of Dix's hyperbolic approximation to the non-hyperbolic wide-angle reflection times causes large errors in the determination of layer parameters. Currently, no direct method exists to extract the velocity information from such wide-angle seismic data. Here we propose a layer-stripping method, using the ray parameter, to calculate the layer parameters directly in a vertically heterogeneous earth from a set of wide-angle seismic reflection data. Synthetic reflection times contaminated by some realistic errors have been used to demonstrate the efficiency and reliability of the algorithms. Field examples using well-identified wide-angle reflection times illustrate the practical feasibility of the proposed method.

In a multilayered earth system no refraction signal will be generated from the top of a layer that has a seismic velocity less than that of the layer immediately above. The interpretation of refraction data in such a situation cannot provide any information about the low-speed layer (LSL), and its presence leads to the overestimation of depths to the bottom of the overlying or cap layer and all subsequent underlying layers. This is the velocity inversion (VI) problem of seismic refraction work. Other geological/geophysical information is required to solve the VI problem. In this respect, wide-angle reflections from the bottom of the LSL may constitute the most reliable information, since they are observed as very strong arrivals on the seismogram after first arrivals. This paper shows that, even if reflections from the bottom of the LSL are used, the solution (i.e. the thickness of the cap layer, and the thickness and velocity of the low-speed layer) to the VI problem is still ambiguous. However, use of the wide-angle reflected phase limits the number of solutions. A method of joint inversion of traveltimes for refractions and wide-angle reflections from the bottom of the LSL is proposed to calculate the thickness of the cap layer and that of the LSL, the velocity of the LSL having been constrained from other information. The reliability of the method has been tested on synthetic data using various initial models. Application to field data demonstrates the practical feasibility of the method by comparing the result with the available solution for the region.