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Introduction Lithology rock types mapping from downhole geophysical log acquired over various tectonic setting around the world is of importance to understand the crustal heterogeneity evolution and tectonics of the area More recently neural network learning in Bayesian framework BNN has been extensively used for the well log data Maiti and Tiwari 2009 2010a b Ojha and Maiti 2016 BNN offers a platform where many shortcomings of ANN can be avoided including over fitting The objective of the present study is to use BNN HMC supervised classification scheme for classification of lithology rock types for large and non simulated new ocean drilling data of Integrated Ocean Drilling Program IODP Expedition 323 project Study area Site U1339D of IODP expedition is used as study area The site is dominated by the mixture of three components biogenic mainly diatom frustules with varying proportions of nannofossils foraminifers silicoflagellates and radiolarians volcaniclastic mainly fine ash and siliciclastic clay to pebble sized clasts Takahashi et al 2011a b Wehrmann et al 2011 Detailed information about the study area downhole data can be found in Takahashi et al 2011a b and Wehrmann et al 2011 Here in this study sonic P wave velocity density density porosity electrical resistivity and gamma ray logs from the IODP hole number U1339D have been used for the analysis of rock type classification Figure 1 Location map of the site U1339 of IODP Expedition 323 drilling and coring sites shown by yellow circle Theory and Methods The BNN is a special configuration of an ANN where the network s weight update is followed by a Bayesian learning Bishop 1995 Ultimately the BNN emulates the functional relationship between an input well log and an output space domain lithology class from a finite data set by adjusting network parameters weight and biases
