Multifrequency electromagnetic data interpretation using stochastic Markov-chain Monte Carlo and simulated annealing methods

2021 ◽  
pp. 311-330
Author(s):  
Yuteng Jin ◽  
Siddharth Misra ◽  
Yifu Han
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Simulated Annealing ◽  
Markov Chain Monte Carlo ◽  
Data Interpretation ◽  
Electromagnetic Data ◽  
Stochastic Markov Chain

Velocity log upscaling based on reversible jump Markov chain Monte Carlo simulated annealing

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. R293-R305 ◽  
Author(s):  
Sireesh Dadi ◽  
Richard Gibson ◽  
Kainan Wang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Simulated Annealing ◽  
Markov Chain Monte Carlo ◽  
Random Perturbation ◽  
Current Model ◽  
Sampling Technique ◽  
Maximum Deviation ◽  
Reversible Jump ◽  
Layer Boundary

Upscaling log measurements acquired at high frequencies and correlating them with corresponding low-frequency values from surface seismic and vertical seismic profile data is a challenging task. We have applied a sampling technique called the reversible jump Markov chain Monte Carlo (RJMCMC) method to this problem. A key property of our approach is that it treats the number of unknowns itself as a parameter to be determined. Specifically, we have considered upscaling as an inverse problem in which we considered the number of coarse layers, layer boundary depths, and material properties as the unknowns. The method applies Bayesian inversion, with RJMCMC sampling and uses simulated annealing to guide the optimization. At each iteration, the algorithm will randomly move a boundary in the current model, add a new boundary, or delete an existing boundary. In each case, a random perturbation is applied to Backus-average values. We have developed examples showing that the mismatch between seismograms computed from the upscaled model and log velocities improves by 89% compared to the case in which the algorithm is allowed to move boundaries only. The layer boundary distributions after running the RJMCMC algorithm can represent sharp and gradual changes in lithology. The maximum deviation of upscaled velocities from Backus-average values is less than 10% with most of the values close to zero.

scholarly journals Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling

2017 ◽  
Vol 147 ◽  
pp. 68-80 ◽  
Author(s):  
Huiying Ren ◽  
Jaideep Ray ◽  
Zhangshuan Hou ◽  
Maoyi Huang ◽  
Jie Bao ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Reservoir Characterization ◽  
Monte Carlo Sampling ◽  
Bayesian Inversion ◽  
Gas Reservoir ◽  
Electromagnetic Data

scholarly journals Schedules of lectures and Monte Carlo method

2011 ◽  
Vol 52 ◽  
Author(s):  
Nikolaj Grigorjev ◽  
Gediminas Stepanauskas
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Simulated Annealing ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Simulated Annealing Algorithm ◽  
Annealing Algorithm

In this paper the problem of the construction of schedule of lectures is considered. The Markov chain Monte Carlo method is used. A particular program based on simulated annealing algorithm was created.  

scholarly journals A Technique to Accelerate Stochastic Markov Chain Monte Carlo Simulations of Calcium-Induced Calcium Release in Cardiac Myocytes

2010 ◽  
Vol 98 (3) ◽  
pp. 295a
Author(s):  
George Williams ◽  
Aristide Chikando ◽  
Gregory Smith ◽  
Mohsin Saleet Jafri
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Simulations ◽  
Cardiac Myocytes ◽  
Calcium Release ◽  
Calcium Induced Calcium Release ◽  
Stochastic Markov Chain

High-depth-resolution Rutherford backscattering data and error analysis of SiGe systems using the simulated annealing and Markov chain Monte Carlo algorithms

1999 ◽  
Vol 59 (7) ◽  
pp. 5097-5105 ◽  
Author(s):  
N. P. Barradas ◽  
A. P. Knights ◽  
C. Jeynes ◽  
O. A. Mironov ◽  
T. J. Grasby ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Simulated Annealing ◽  
Markov Chain Monte Carlo ◽  
Error Analysis ◽  
Depth Resolution ◽  
Rutherford Backscattering ◽  
Monte Carlo Algorithms ◽  
High Depth

Markov Chain Monte Carlo Methods for Reserves Estimation

2005 ◽  
Author(s):  
Andrew W. Wadsley
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Methods
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