Results 261 to 270 of about 15,893 (296)
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The Leading Edge, 2008
Many times we are faced with the business decision of whether or not to develop a sand that is at the limit of seismic resolution and near the noise level of the data. The critical issue is developing a reasonable certainty that there is enough volume of hydrocarbons to develop. A popular approach is to use Bayesian methods to determine the probability
Michael E. Glinsky +7 more
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Many times we are faced with the business decision of whether or not to develop a sand that is at the limit of seismic resolution and near the noise level of the data. The critical issue is developing a reasonable certainty that there is enough volume of hydrocarbons to develop. A popular approach is to use Bayesian methods to determine the probability
Michael E. Glinsky +7 more
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On Bayesian inference for the Inverse Gaussian distribution
Statistics & Probability Letters, 1991zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BETRO B, ROTONDI R
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Bayesian inference for inverse problems – statistical inversion
e & i Elektrotechnik und Informationstechnik, 2007Unlike deterministic inversion methods, statistical approaches are capable of taking into account inherent measurement and model uncertainties into the inverse problem solution in a very simple and natural way. Statistical inversion theory reformulates inverse problems as problems of Bayesian statistical inference. In this framework, the solution to an
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Bayesian Inversion for Geoacoustic Parameters from Ocean Bottom Reflection Loss
Journal of Computational Acoustics, 2017 Geoacoustic inversion is a very important issue in underwater acoustics, and the inversion method based on bottom reflection loss is a valid technique to invert bottom parameters.
Kunde Yang, Rui Duan, Yuanliang Ma
exaly +2 more sources
Bayesian Inversion of Traveltimes for Cavity Location
Symposium on the Application of Geophysics to Engineering and Environmental Problems 2008, 2008We present a case history for the location of a cavity in the subsurface by processing the signal generated inside the cavity by a seismic source. The signal emitted by the source is recorded by surface sensors and it is processed in order to obtain direct arrival traveltimes.
BERNASCONI, GIANCARLO, ROVETTA, DIEGO
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Bayesian geoacoustic inversion.
The Journal of the Acoustical Society of America, 2010This paper describes a general Bayesian approach to estimating seabed geoacoustic parameters from ocean acoustic data, which is also applicable to other inverse problems. Within a Bayesian formulation, the complete solution is given by the posterior probability density (PPD), which includes both data and prior information.
Stan E. Dosso, Jan Dettmer
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Bayesian Networks for Inverse Inference in Manufacturing Bayesian Networks
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017Physics based simulations of manufacturing processes are used for prediction of material properties and defects in a number of industrial applications. However, a practising engineer often requires the solution to an "inverse problem" - prediction of inputs for the desired outcome.
Avadhut Sardeshmukh +3 more
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Bayesian Gaussian Mixture Linear Inversion in Geophysical Inverse Problems
Proceedings, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dario Grana +2 more
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Bayesian inversion for optical diffraction tomography
Journal of Modern Optics, 2010In this paper, optical diffraction tomography is considered as a non-linear inverse scattering problem and tackled within the Bayesian estimation framework. The object under test is a man-made object known to be composed of compact regions made of a finite number of different homogeneous materials. This a priori knowledge is appropriately translated by a
Ayasso, H +2 more
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2015
This chapter provides a general introduction, at the high level, to the backward propagation of uncertainty/information in the solution of inverse problems, and specifically a Bayesian probabilistic perspective on such inverse problems. Under the umbrella of inverse problems, we consider parameter estimation and regression.
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This chapter provides a general introduction, at the high level, to the backward propagation of uncertainty/information in the solution of inverse problems, and specifically a Bayesian probabilistic perspective on such inverse problems. Under the umbrella of inverse problems, we consider parameter estimation and regression.
openaire +1 more source