ALBERT

Description: The seismic reflection data processing to identify thin coal beds and intrinsic fault structure associated with coalmines suffers from the coherent noise that arises due to interference and diffraction of seismic signals from adjacent horizontal boundaries on either sides of the fault structure. The amplitudes of the interfering reflections mislead the interpretation of geological features like faults, curved reflectors, etc. In particular, correlated and erratic noise create more severe problem than the random noise in the interpretation of such complex geological structures. Here, we employed Space Lagged Singular Spectral Analysis (SLSSA) algorithm, which decomposes the amplitudes from a constant time/depth to determine the original signal amplitude based on eigen properties of the signal. Thus, we can de-noise seismic signal to delineate the concealed discontinuities and to map the fault structures. Initially, we tested the algorithm on the synthetic data of fault structure embedded with complex mixed noise (random and colored) of known percentage. Finally, the method was employed on high-resolution seismic reflection observations recorded from Singareni coalfield, India. The SLSSA method reveals some significant kinematic fault structures in the coal-bearing zone, which agreed with regional fault structures in the PG basin and correlates well with available geological information in the area.

Description: A novel technique based on the Bayesian neural network (BNN) theory is developed and employed to model the temperature variation record from the Western Himalayas. In order to estimate an a posteriori probability function, the BNN is trained with the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulations algorithm. The efficacy of the new algorithm is tested on the well known chaotic, first order autoregressive (AR) and random models and then applied to model the temperature variation record decoded from the tree-ring widths of the Western Himalayas for the period spanning over 1226–2000 AD. For modeling the actual tree-ring temperature data, optimum network parameters are chosen appropriately and then cross-validation test is performed to ensure the generalization skill of the network on the new data set. Finally, prediction result based on the BNN model is compared with the conventional artificial neural network (ANN) and the AR linear models results. The comparative results show that the BNN based analysis makes better prediction than the ANN and the AR models. The new BNN modeling approach provides a viable tool for climate studies and could also be exploited for modeling other kinds of environmental data.

Description: In order to study the imprints of solar–ENSO–geomagnetic activity on the Indian Subcontinent, we have applied the Singular Spectral Analysis (SSA) and wavelet analysis to the tree ring temperature variability record from the western Himalayas. The data used in the present study are the Solar Sunspot Number (SSN), Geomagnetic Indices (aa Index), Southern Oscillation Index (SOI) and tree ring temperature record from western Himalayas (WH), for the period of 1876–2000. The SSA and wavelet spectra reveal the presence of 5 years short term ENSO variations to 11 year solar cycle indicating the influence of both the solar–geomagnetic and ENSO imprints in the tree ring data. The presence of 33-year cycle periodicity suggests the Sun-temperature variability probably involving the induced changes in the basic state of the atmosphere. Our wavelet analysis for the SSA reconstructed time series agrees with our previous results and also enhance the amplitude of the signals by removing the noise and showing a strong influence of solar–geomagnetic and ENSO patterns throughout the record. The solar flares are considered to be responsible for cause in the circulation patterns in the atmosphere. The net effect of solar–geomagnetic processes on temperature record thus appears to be the result of counteracting influences on shorter (about 5–6 years) and longer (about 11–12 years) time scales. The present analysis thus suggests that the influence of solar processes on Indian temperature variability operates in part indirectly through ENSO, but on more than one time scale. The analyses hence provides credible evidence for teleconnections of tropical pacific climatic variability with Indian climate ranging from interannual-decadal time scales and also demonstrate the possible role of exogenic triggering in reorganizing the global earth–ocean–atmospheric systems.

Description: Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M=6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN) theory using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme. The new method is applied to invert one and two-dimensional Direct Current (DC) vertical electrical sounding (VES) data acquired around the Koyna region in India. Prior to apply the method on actual resistivity data, the new method was tested for simulating synthetic signal. In this approach the objective/cost function is optimized following the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) sampling based algorithm and each trajectory was updated by approximating the Hamiltonian differential equations through a leapfrog discretization scheme. The stability of the new inversion technique was tested in presence of correlated red noise and uncertainty of the result was estimated using the BNN code. The estimated true resistivity distribution was compared with the results of singular value decomposition (SVD)-based conventional resistivity inversion results. Comparative results based on the HMC-based Bayesian Neural Network are in good agreement with the existing model results, however in some cases, it also provides more detail and precise results, which appears to be justified with local geological and structural details. The new BNN approach based on HMC is faster and proved to be a promising inversion scheme to interpret complex and non-linear resistivity problems. The HMC-based BNN results are quite useful for the interpretation of fractures and lineaments in seismically active region.

Description: We studied the complex and non-stationary records of global earthquake employing the robust statistical and spectral techniques to understand the patterns, processes and periodicity. Singular Spectral Analysis (SSA) and correlation methods are used to quantify the nature of principle dynamical processes of global annual earthquake rates. The SSA decomposes the principle component of earthquake rates (first mode), which suggests that there is a linear increase in the yearly earthquake number from 1975 to 2005 accounting for 93% variance and may be identified with the earth's internal dynamical processes. Superimposed on this monotonic trend, there is an 11 years cyclic variation (second and third modes) accounting for 5% variance, which may corresponds to the well-known solar cycle. The remaining 2% higher order fluctuating components appears to be associated with artificial recharge and natural triggering forces (reservoir, tidal triggering etc.). The correlation study indicates that there is strong positive and negative correlation among the global earthquake rates with surface air temperature and sunspot numbers respectively. Interesting coupling mechanisms do exist, in which atmospheric circulations perturbed by the abrupt temperature variability might change the torques/momentum of inertia (earth's angular momentum) of the earth and thereby may offer the required inputs to trigger earthquake activities at the "critical phases".

Description: The backpropagation (BP) artificial neural network (ANN) technique of optimization based on steepest descent algorithm is known to be inept for its poor performance and does not ensure global convergence. Nonlinear and complex DC resistivity data require efficient ANN model and more intensive optimization procedures for better results and interpretations. Improvements in the computational ANN modeling process are described with the goals of enhancing the optimization process and reducing ANN model complexity. Well-established optimization methods, such as Radial basis algorithm (RBA) and Levenberg-Marquardt algorithms (LMA) have frequently been used to deal with complexity and nonlinearity in such complex geophysical records. We examined here the efficiency of trained LMA and RB networks by using 2-D synthetic resistivity data and then finally applied to the actual field vertical electrical resistivity sounding (VES) data collected from the Puga Valley, Jammu and Kashmir, India. The resulting ANN reconstruction resistivity results are compared with the result of existing inversion approaches, which are in good agreement. The depths and resistivity structures obtained by the ANN methods also correlate well with the known drilling results and geologic boundaries. The application of the above ANN algorithms proves to be robust and could be used for fast estimation of resistive structures for other complex earth model also.