An input-output-based Bayesian neural network method for analyzing carbon reduction potential: A case study of Guangdong province
Economic development, population growth, industrialization and urbanization have led to
large increases in anthropogenic carbon emission that has caused a variety of negative …
large increases in anthropogenic carbon emission that has caused a variety of negative …
Bayesian geophysical inversion using invertible neural networks
Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …
[HTML][HTML] A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques
Determination of pore pressure (PP), a key reservoir parameter that is beneficial for
evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields …
evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields …
An efficient model for predicting the train-induced ground-borne vibration and uncertainty quantification based on Bayesian neural network
R Liang, W Liu, M Ma, W Liu - Journal of Sound and Vibration, 2021 - Elsevier
The uncertainty in the prediction of train-induced ground-borne vibration is mainly attributed
to the randomness of excitation, the variability of soil, the uncertainty of models, etc …
to the randomness of excitation, the variability of soil, the uncertainty of models, etc …
[HTML][HTML] Gradient boosting Bayesian neural networks via Langevin MCMC
G Bai, R Chandra - Neurocomputing, 2023 - Elsevier
Bayesian neural networks harness the power of Bayesian inference which provides an
approach to neural learning that not only focuses on accuracy but also uncertainty …
approach to neural learning that not only focuses on accuracy but also uncertainty …
Research progress of machine-learning algorithm for formation pore pressure prediction
H Pan, S Deng, C Li, Y Sun, Y Zhao… - Petroleum Science and …, 2023 - Taylor & Francis
Formation pore pressure is one of the most important basic data in petroleum exploration
and development. The traditional prediction model of formation pore pressure relies on …
and development. The traditional prediction model of formation pore pressure relies on …
A deep CNN-LSTM model for predicting interface depth from gravity data over thrust and fold belts of North East India
S Maiti, RK Chiluvuru - Journal of Asian Earth Sciences, 2024 - Elsevier
Geological interface depth modeling from the gravity field data is crucial for the exploration
of oil and gas, mapping of sediment-basement interfaces and many other geological …
of oil and gas, mapping of sediment-basement interfaces and many other geological …
Monitoring of Caspian Sea-level changes using deep learning-based 3D reconstruction of GRACE signal
The gravity recovery and climate experiment (GRACE) satellites detect changes in the
distribution of water on the Earth surface based on the gravity anomaly. A correct …
distribution of water on the Earth surface based on the gravity anomaly. A correct …
Precise geopressure predictions in active foreland basins: An application of deep feedforward neural networks
Precise geopressure predictions in younger foreland basins of the world is a challenging
job. Well based pressure prediction gives only one-dimensional information about the …
job. Well based pressure prediction gives only one-dimensional information about the …
Pore type identification in carbonate rocks using convolutional neural network based on acoustic logging data
Existing methods of well logging interpretation often contain uncertainties in the exploration
and evaluation of carbonate reservoirs due to the complex pore types. Based on the time …
and evaluation of carbonate reservoirs due to the complex pore types. Based on the time …