A Hybrid Monte Carlo Method Based Artificial Neural Networks Approach for Rock Boundaries Identification: A Case Study from the KTB Bore Hole

Identification of rock boundaries and structural features from well log response is a fundamental problem in geological field studies. However, in a complex geologic situation, such as in the presence of crystalline rocks where metamorphisms lead to facies changes, it is not easy to discern accurate information from well log data using conventional artificial neural network (ANN) methods. Moreover inferences drawn by such methods are also found to be ambiguous because of the strong overlapping of well log signals, which are generally tainted with deceptive noise. Here, we have developed an alternative ANN approach based on Bayesian statistics using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) inversion scheme for modeling the German Continental Deep Drilling Program (KTB) well log data. MCMC algorithm draws an independent and identically distributed (i.i.d) sample by Markov Chain simulation technique from posterior probability distribution using the principle of statistical mechanics in Hamiltonian dynamics. In this algorithm, each trajectory is updated by approximating the Hamiltonian differential equations through a leapfrog discrimination scheme. We examined the stability and efficiency of the HMC-based approach on “noisy” data assorted with different levels of colored noise. We also perform uncertainty analysis by estimating standard deviation (STD) error map of a posteriori covariance matrix at the network output of three types of lithofacies over the entire length of the litho section of KTB. Our analyses demonstrate that the HMC-based approach renders robust means for classification of complex lithofacies successions from the KTB borehole noisy signals, and hence may provide a useful guide for understanding the crustal inhomogeneity and structural discontinuity in many other tectonically critical and complex regions.     

Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP)

In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISS-III, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ??M??) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919.     

On Improving the Solution by Using Lower Bound Finite Elements Limit Analysis and Linear Programming

This study presents an improved technique for obtaining the collapse loads for any geotechnical stability problem with the application of the lower bound finite elements limit analysis in combination with linear programming. In the proposed method, a lower order polygon is initially used throughout the problem domain to model the Mohr–Coulomb yield function; the order of the polygon refers to its total number of sides. After obtaining the initial solution, the problem domain is then discretized into a number of zones in which a different order polygon is used to model the yield surface. It is noted that a higher order polygon needs to be chosen only in a region, not everywhere, where the stress state approaches towards the yield. The total memory usage as well as the computational time needed to solve the problem with the proposed technique becomes significantly smaller. In order to check the validity of the method, the bearing capacity factor N_γ was computed for smooth as well as rough strip footing and the obtained computational results were found to be quite encouraging.     

Facies analysis and depositional model of late Permian Raniganj formation: Study from Raniganj coal bed methane block

The Raniganj Formation (late Permian) forms the uppermost economic coal-bearing unit of the Gondwana succession. The dominant facies interpreted from analysis of cores from the Raniganj formation are classified as Sandstone dominated facies, Sandstone - shale heterolith facies, Shale facies and Coal facies. The natural Gamma response of Raniganj Formation shows predominance of repetitive fining upwards cycles. Integration of core analysis and geophysical log data of the Raniganj formation indicates meandering fluvial environment. The lower part of Raniganj Formation is channel dominated which corresponds to thick amalgamated sand bodies while the upper part represent overbank shows predominance of channel avulsion indicating a gradual change in accommodation space. Five major fining upward depositional sequences, bounded by sub-aerial unconformities (sequence boundaries) have been dentified in Raniganj formation, based on changes in depositional style that are correlated regionally. Each sequence comprises of Low accommodation system tract (LAST) at base and high accommodation system tract (HAST) at top. LAST is characterized by vertically stacked, multistory amalgamated channel sandstone dominated facies, while floodplain dominated facies characterize HAST. The coal seams deposited in LAST are thicker and relatively more continuous than the frequent thin seams of HAST. Such facies distribution study would be helpful for the development strategy for CBM blocks based on production priority.