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• W2904704617 abstract "Pore pressure (PP) study can provide insightful information about evolution history and/or geological process taking place over a region. Conventional methods, mostly are of deterministic, and they do not allow considering underlying variability and uncertainty. Here we implement Bayesian neural networks (BNN) optimized by Scaled Conjugate Gradient (SCG) and Hybrid Monte Carlo (HMC) approach to model the PP and estimate the uncertainty in prediction from well log data of well U1343E located at Bering sea slope region of the IODP Expedition 323. In the first step, to create representative samples of well log and corresponding PP samples, Eaton’s and porosity methods are employed to estimate PP empirically from well log data (e.g., gamma ray, sonic velocity, bulk density and sonic derived porosity). In the second step, in total 357 representative samples are used to build a statistical model in Bayesian framework to model the PP against depth. Prior to actual data analysis, we conducted a series of experiments combining with auto-correlation function (ACF) and/or partial autocorrelation function (PACF) analysis to fix network structure (e.g., input lag, number of hidden node) and the bounds of network hyper-parameter. In contrast to previous approach, we seek to develop a mechanism which allows to explore the link between past PP and/or well log history and present PP under rapid sedimentation rate and changing environment. The model exhibits excellent performance between predicted and computed PP with Pearson's correlation coefficient, reduction of error (RE) (RESCG-BNN ∼ 0.99; REHMC-BNN ∼ 0.99), and index of agreement (IA) (IASCG-BNN ∼ 0.99; IAHMC-BNN ∼ 0.98). Comparison based on coefficient of determination, R2, it is obtained that BNN produced superior results than the conventional artificial neural networks (ANNs). Moreover, at 530 mbsf (meter below sea floor), abrupt PP change could be linked to the transition from Pliocene to Pleistocene. The approach used here, could be useful to identify overpressure zones (OPZ) and to understand the role of past PP/well log to present PP history in many other complex geo-environmental applications." @default.
• W2904704617 created "2018-12-22" @default.
• W2904704617 creator A5082589248 @default.
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• W2904704617 date "2019-03-01" @default.
• W2904704617 modified "2023-09-30" @default.
• W2904704617 title "Short term memory efficient pore pressure prediction via Bayesian neural networks at Bering Sea slope of IODP expedition 323" @default.
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• W2904704617 doi "https://doi.org/10.1016/j.measurement.2018.12.034" @default.
• W2904704617 hasPublicationYear "2019" @default.
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