Results 111 to 120 of about 15,893 (296)

Bayesian variational time-lapse full waveform inversion [PDF]

Time-lapse seismic full-waveform inversion (FWI) provides estimates of dynamic changes in the Earth's subsurface by performing multiple seismic surveys at different times.
Zhang, Xin, Curtis, Andrew; id_orcid
core   +1 more source

ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

Advanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng, Wenfeng Fan, Feng Li, Jian Wu, Wei Quan   +4 more
wiley   +1 more source

Bayesian Variational Time-lapse Full-waveform Inversion

, 2023
Time-lapse seismic full-waveform inversion (FWI) provides estimates of dynamic changes in the subsurface by performing multiple seismic surveys at different times.
Zhang, Xin, Curtis, Andrew
core  

Bayesian evidential learning : an alternative to hydrogeophysical coupled inversion

, 2021
Deterministic geophysical inversion suffers from a lack of realism because of the regularization, while stochastic inversion allowing for uncertainty quantification is computationally expensive. In this contribution, we propose to use Bayesian Evidential
Hermans, Thomas, Thomas Hermans, Thibaut, Robin, Compaire, Nicolas, Nicolas Compaire, Nolwenn Lesparre, Lesparre, Nolwenn, Robin Thibaut   +7 more
core   +1 more source

Seismic Prediction of Porosity in the Norne Field: Utilizing Support Vector Regression and Empirical Models Driven by Bayesian Linearized Inversion

Applied Sciences
This work aims to improve the characterization of petrophysical properties by accurately estimating subsurface porosity using seismic and well data. The study includes Bayesian Linearized Inversion to obtain elastic parameters (e.g., compressional e ...
Jorge A. Teruya Monroe, Jose J. S. de Figueiredo, Carlos E. S. Amanajas   +2 more
doaj   +1 more source

Prediction of Refracturing Effect of Tight Gas Reservoirs Based on Bayesian Inversion Algorithm. [PDF]

Comput Intell Neurosci, 2022
Lin H, Zhou F, Tian Y, Wang Y.
europepmc   +1 more source

Bayesian inverse problems and seismic inversion

, 2016
The Bayesian formulation for inverse problems gives a way of making inferences about unknown quantities not directly observable. The application of Bayes' Theorem combines the prior information and the observation to give a posterior measure, which contains information about the quantity we are trying to estimate. In this thesis, we review a particular
openaire   +2 more sources

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source

Bayesian inversion of a diffusion model with application to biology. [PDF]

J Math Biol, 2021
Croix JC, Durrande N, Alvarez MA.
europepmc   +1 more source

Bayesian Inversion of 4D Seismic Data with a Machine Learning Prior: Application to the Catcher Fields

, 2023
We present a workflow that integrates machine learning and Bayesian inversion methods to estimate reservoir pressure and saturation changes from 4D seismic data.
MacBeth, C., Côrte, G.; id_orcid, Tian, S., Marsden, G.   +3 more
core   +1 more source