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Ojha M.,CSIR - Central Electrochemical Research Institute | Maiti S.,Indian Institute of Geomagnetism DST
Deep-Sea Research Part II: Topical Studies in Oceanography | Year: 2013

A novel approach based on the concept of Bayesian neural network (BNN) has been implemented for classifying sediment boundaries using downhole log data obtained during Integrated Ocean Drilling Program (IODP) Expedition 323 in the Bering Sea slope region. The Bayesian framework in conjunction with Markov Chain Monte Carlo (MCMC)/hybrid Monte Carlo (HMC) learning paradigm has been applied to constrain the lithology boundaries using density, density porosity, gamma ray, sonic P-wave velocity and electrical resistivity at the Hole U1344A. We have demonstrated the effectiveness of our supervised classification methodology by comparing our findings with a conventional neural network and a Bayesian neural network optimized by scaled conjugate gradient method (SCG), and tested the robustness of the algorithm in the presence of red noise in the data. The Bayesian results based on the HMC algorithm (BNN.HMC) resolve detailed finer structures at certain depths in addition to main lithology such as silty clay, diatom clayey silt and sandy silt. Our method also recovers the lithology information from a depth ranging between 615 and 655 m Wireline log Matched depth below Sea Floor of no core recovery zone. Our analyses demonstrate that the BNN based approach renders robust means for the classification of complex lithology successions at the Hole U1344A, which could be very useful for other studies and understanding the oceanic crustal inhomogeneity and structural discontinuities. © 2013 Elsevier Ltd. All rights reserved. Source

Tiwari R.K.,CSIR - Central Electrochemical Research Institute | Maiti S.,Indian Institute of Geomagnetism DST
Nonlinear Processes in Geophysics | Year: 2011

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. © Author(s) 2011. Source

Maiti S.,Indian Institute of Geomagnetism DST | Gupta G.,Indian Institute of Geomagnetism DST | Erram V.C.,Indian Institute of Geomagnetism DST | Tiwari R.K.,CSIR - Central Electrochemical Research Institute
Environmental Earth Sciences | Year: 2013

Modeling resistivity profiles, especially from hard rock areas, is of specific relevance for groundwater exploration. A method based on Bayesian neural network (BNN) theory using a Hybrid Monte Carlo (HMC) simulation scheme is applied to model and interpret direct current vertical electrical sounding measurements from 28 locations around the Malvan region, in the Sindhudurg district, southwest India. The modeling procedure revolves around optimizing the objective function using the HMC based sampling technique which is followed by updating each trajectory by integrating the Hamiltonian differential equations via a second order leapfrog discretization scheme. The inversion results suggest a high resistivity structure in the north-western part of the area, which correlates well with the presence of laterites. In the south-western part, a very high conductive zone is observed near the coast indicating an extensive influence of saltwater intrusion. Our results also show that the effect of intrusion of saline water diminishes from the south-western part to the north-eastern part of the region. Two dimensional modeling of four resistivity profiles shows that the groundwater flow is partly controlled by existing lineaments, fractures, and major joints. Groundwater occurs at a weathered/semi-weathered layer of laterite/clayey sand and the interface of overburden and crystalline basement. The presence of conduits is identified at a depth between 10 and 15 m along the Dhamapur-Kudal and Parule-Oros profiles, which seems to be potential zone for groundwater exploration. The NW-SE trending major lineaments and its criss-cross sections are indentified from the apparent and true resistivity surface map. The pseudo-section at different depths in the western part of the area, near Parule, shows extensive influence of saltwater intrusion and its impact reaching up to a depth of 50 m from the surface along the coastal area. Further, the deduced true electrical resistivity section against depth correlates well with available borehole lithology in the area. Present analyses suggest that HMC-based BNN method is robust for modeling resistivity data especially in hard rock terrains. These results are useful for interpreting fractures, major joints, and lineaments and crystalline basement rock and also for constraining the higher dimensional models. © 2012 Springer-Verlag. Source

Maiti S.,Indian Institute of Geomagnetism DST | Erram V.C.,Indian Institute of Geomagnetism DST | Gupta G.,Indian Institute of Geomagnetism DST | Tiwari R.K.,CSIR - Central Electrochemical Research Institute
Environmental Monitoring and Assessment | Year: 2013

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb's diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: "Very good", "Good", and "Unsuitable". The BNN model-based results suggest that groundwater quality falls mostly in the range of "Good" to "Very good" except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality. © 2012 Springer Science+Business Media B.V. Source

Maiti S.,Indian Institute of Geomagnetism DST | Gupta G.,Indian Institute of Geomagnetism DST | Erram V.C.,Indian Institute of Geomagnetism DST | Tiwari R.K.,CSIR - Central Electrochemical Research Institute
Nonlinear Processes in Geophysics | Year: 2011

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. © Author(s) 2011. Source

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