Rajesh R Nair

Nondestructive measurements and evaluation has great significance in various domains of science, engineering and technology. The objective of this research is to investigate the anisotropic behavior in a sandstone sample from non-invasive tests using Laser Doppler Vibrometer coupled with Piezoelectric Transducer and to validate these results using a laboratory scale controlled hydraulic fracturing arrangement. High-resolution 3-component single-point seismograms were generated for the core sample using a combination of 1 MHz Piezoelectric Transducer as a source of elastic waves that travel within the reservoir rock sample and Laser Doppler Vibrometer as the receiver. Hilbert transforms of the 3-component data were calculated to obtain the complex signal. Shear wave splitting phenomenon due to anisotropy in rock was examined and the resultant S H and S V wave polarizations were measured. Elastic tensor for the core sample was subsequently determined from the velocity picks within the Hilbert energy envelope followed by the estimation of Thomsen’s parameters. The hodogram analysis was performed to assess the process of shear wave splitting in the rock sample that detects the anisotropy of the medium and this, in turn, specifies the characteristics of weakness planes. Laboratory scale controlled hydraulic fracturing was performed to verify whether the fractures propagate along the anisotropic planes of weakness. Real-time fracture detection was carried out during this process and its propagation features were studied. The fractured core sample was imaged under the slice Computed Tomography scan machine to perceive the mode and propagation of fractures in the rock specimen.

This work deals with a methodology applied to seismic early warning systems which are designed to provide real-time estimation of the magnitude of an event. We will reappraise the work of Simons et al. (2006), who on the basis of wavelet approach predicted a magnitude error of ±1. We will verify and improve upon the methodology of Simons et al. (2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source-receiver distance of up to 150 km during the period 1998-2011. We applied a wavelet transform on the seismogram data and calculating scale-dependent threshold wavelet coefficients. These coefficients were then classified into low magnitude and high magnitude events by constructing a maximum margin hyperplane between the two classes, which forms the essence of SVMs. Further, the classified events from both the classes were picked up and linear regressions were plotted to determine the relationship between wavelet coefficient magnitude and earthquake magnitude, which in turn helped us to estimate the earthquake magnitude of an event given its threshold wavelet coefficient. At wavelet scale number 7, we predicted the earthquake magnitude of an event within 2.7 seconds. This means that a magnitude determination is available within 2.7 s after the initial onset of the P-wave. These results shed light on the application of SVM as a way to choose the optimal regression function to estimate the magnitude from a few seconds of an incoming seismogram. This would improve the approaches from Simons et al. (2006) which use an average of the two regression functions to estimate the magnitude. © Indian Academy of Sciences.

Abstract: This paper describes a case study that converts pre-stack seismic data into meaningful rock properties by employing non-parametric probability density functions through a probabilistic modelling approach. This study used the simultaneous pre-stack inversion method to transform pre-stack seismic data into seismic attributes like compressional impedance, shear impedance, density, and VP/VS ratio. Then cross plot analysis was conducted on selected wireline log data to identify reservoir lithofacies zones based on the ranges of properties like P-impedance and VP/VS ratio. Hydrocarbon zone was identified with the range of VP/VS ratio between 1.15 and 1.82 and ZP from 3800 to 12400 ((m/s) × (g/cc)). Water bearing sand zone was separated with VP/VS ratio with 1.85–2.12 and ZP with 3500–14900 ((m/s) × (g/cc)), and 3500–14900 ((m/s) × (g/cc)) of ZP, VP/VS ratio between 2.14 and 3.1 was used to characterize the shale zone. A non-parametric kernel density estimator is used on cross-plot data points to generate a probability density function for each lithofacies. These non-parametric PDFs were incorporated with seismic attributes using a probabilistic modelling approach based on Bayes' classification to generate a lithofacies model. The application of methodology provides a better insight into predicting and discriminating lithofacies in the study area. Highlights: Applied seismic inversion to obtain seismic elastic attributes such as compressional impedance (ZP), shear impedance (ZS), VP/VS ratio, and density.Shale, water-bearing zone, and hydrocarbon zone were identified using the cross plot analysis of well log data.Probability density functions (PDFs) for lithologies were generated on cross-plot space using the non-parameter statistical classification.Finally, hydrocarbon zones were identified using the Bayes' rule by combining the seismic data with PDFs.

An earthquake early warning alert system with a high degree of accuracy and precision and measured in seconds is a necessity to save many lives in Japan, one of the most tectonically active countries of the world. In this paper, we estimate the potential value for early warning of crustal and intraslab earthquakes recorded at the KiK-Net network. The work reported here is similar to that of the early warning analysis system of California and uses new comprehensive data from northern and eastern Japan (Hokkaido and Tohoko). An attempt was also made to analyze whether the slopes of the regression lines obtained for Californian events (all are crustal events) are similar to those obtained for Hokkaido and Tohoko crustal events. We also analyzed whether there was any difference between the slopes obtained from Japanese crustal and intraslab earthquakes. The poor correlation of the higher magnitude (>5.5) with predominant period implied quite a limited predictability of predominant frequency. This was evident from the scattered plot obtained for higher magnitudes. These limitations led us to use a discrete wavelet transform-automated algorithm based on the bi-orthogonal construction with two and four vanishing moments for the primal and dual wavelets.

The microscale interfacial characteristics of coal are studied based on the analysis of mechanical behavior of individual elements contributing to coal heterogeneity with respect to high volatile bituminous A of coal, from Singrauli coal field, Madhya Pradesh, India. Micro-scale delineation of elastic plastic properties of coal by grid micro-indentation test and fracture toughness was accomplished by micro-scratch test on collotenite maceral. Micro-scratch test was performed in different directions on and critical points of failure were diagnosed by measuring acoustic emissions and tangential force. The 3D heterogeneity of coal is studied based on the damages at certain point of loads known as critical points with respect to distribution of macerals derived from coal petrography. Damages were observed at higher critical loads on samples collected from 332.9-335.6 m depth compared to 329.7-330.9 m depth samples owing to less heterogeneity and increased compaction. Raman stress mapping at critical points of scratch track revealed a spectral shift due to stress inversion. Shift from 1576-1593 cm-1 for graphite band (G), and from 1344-1357 cm-1 for disordered Carbon (D) band indicate the nature of stress and the deformation occurred for coal bulk sample. Raman spectra variation for maceral collotelinite is studied and compared with coal bulk samples behaviour a shift from 1574-1597 cm-1 for G, and from 1346-1356 cm-1 for D.

Stepwise linear regression, multi-layer feed forward neural (MLFN) network method was used to predict the 2D distribution of P-wave velocity, resistivity, porosity, and gas hydrate saturation throughout seismic section NGHP-01 in the Krishna-Godavari basin. Log prediction process, with uncertainties based on root mean square error properties, was implemented by way of a multi-layer feed forward neural network. The log properties were merged with seismic data by applying a non-linear transform to the seismic attributes. Gas hydrate saturation estimates show an average saturation of 41 % between common depth point (CDP) 600 and 700 and an average saturation of 35 % for CDP 300–400 and 700–800, respectively. High gas hydrate saturation corresponds to high P-wave velocity and high resistivity except in a few spots, which could be due to local variation of permeability, temperature, fractures, etc.

Characterizing complex fluvial-deltaic deposits is a challenging task for finding hydrocarbon discoveries. We described a methodology for predicting the hydrocarbon zones from complex well-log and prestack seismic data. In this current study, data analysis involves an integrated framework based on Simultaneous prestack seismic inversion (SPSI), target correlation coefficient analysis (TCCA), Poisson impedance inversion, and non-parametric statistical analysis, and Bayesian classification. First, seismic elastic attributes from prestack seismic data were estimated. They can provide the spatial distribution of petrophysical properties of seismic data. Then target correlation coefficient analysis (TCCA) was estimated roration factor “c” from well-log data. Using the seismic elastic attributes and rotation factor “c”, Poisson impedance inversion was performed to predict the Poisson impedance volume. Finally, Bayesian classification integrated the Poisson impedance volume with non-parametric probabilistic density functions (PDFs) to estimate the spatial distribution of lithofacies. Despite complex characteristics in the elastic properties, the current study successfully delineated the complex fluvial-details deposits. These results were verified with conventional findings through numerical analysis.

Organic rich soils (peat) store significant amount of global soil carbon (C) in the form of methane (CH4) and carbon dioxide (CO2). Peat soils act as sinks and are a source of greenhouse gases (GHG) which occur in the form of Free Phase Gas in coastal landforms. South-West India has an extensive wetland system and is the primary source of GHG emissions, and CH4, in particular, has high potential to contribute to global climate change. In this scenario, this study sheds light on how SW Indian peatlands contribute to the global carbon cycle. The soil C stock and GHG spatial distribution in three distinct topographic coastal landforms within the peatland region were investigated: Site 1, Muthukulam in the wetland boundary; site 2, Ramapuram in the paleo-sand ridge, and site 3, Eruva in the paleodrainage channel. The combination of non-destructive Ground Penetrating Radar (GPR) and direct measurement (coring) in conjunction with the C core analysis helped in building the relationship between the GHG storage in the landforms of varying C stocks. Moreover, Common offset GPR has the ability to image subsurface features, lithological boundaries, coastal landforms, and peat-forming environments. The result from this study depicts the importance of different landforms in the storage of C and GHG in SW Indian peatlands.

Indian Ocean subduction zone is one of the most active plate margins of the globe as evident from its vast record of great magnitude earthquake and tsunami events. We use Bouguer admittance (Morlet isostatic response function) in Sumatra-Java subduction zones comprising both the subduction and over-riding plates to determine the lithospheric mechanical strength variations. We determine effective elastic thickness (Te) for five oceanic windows (size 990 × 990 km2) by analyzing the admittance using Bouguer gravity and bathymetry data. The results show bimodal Te values < 20 km for Sumatra and 20-40 km for Java. The lower bimodal values obtained for Sumatra appears to correlate well with the zones of historical seismicity. This is in sharp contrast with Java subduction zone, which shows higher Te values (20-40 km) and apparently associated with low magnitude earthquakes. We suggest a strong and wide interseismic coupling for Sumatra between the subducting and over-riding plates, and deeper mantle contributing to low strength, shallow focus - high magnitude seismicity and vice versa for Java, leading to their seismogenic zonation. © Indian Academy of Sciences.

The refraction of the 2004 Indian Ocean tsunami waves caused drastic devastation along East coast of India, mainly in the area of investigation. Here, we appraise and integrate the sedimentary characteristics and microfossil studies of the area. The gigantic tsunami waves caused the landward fining of sediments that were carried as suspended load. Tsunami sediments have distinctive characteristics, like fine-to-medium grained sand, moderately to poorly sorted sediments which indicating sudden winnowing followed by tranquil flood. Positively skewed grain size distribution indicating unidirectional transport, and mesokurtic to platykurtic character implying poorly sorted single source origin. The species of benthonic foraminifers and ostracods reside in marine environment indicating shallow water origin of sediments. The onshore deposits are vertically divided into three depositional units interpreted from Ground Penetrating Radar (GPR) signatures-Unit 1 is a relatively continuous parallel layer indicative of calm environment; Unit 2 has paleochannels and burial scarps, seen as oblique reflections that might be indicative of an intense erosional environment; Unit 3 is interpreted as 2004 tsunami layer, has three subunits. Each main units have been separated by Heavy Mineral Concentrated (HMC) layers, deposited by continuous wave action (~ 20 cm) and by the tsunami (> 30 cm) activity, evidenced by low magnetic susceptibility values at the bottom compared to the top of the HMC layers. GPR has been effectively utilized in this paper as subsurface imaging tool for the interpretation and reconstruction of stratigraphy, and also helped to unearth the erosional and depositional environments. © 2012 Springer Science+Business Media Dordrecht.