Anisotropic seismic inversion using a multigrid Monte Carlo approach
نویسندگان
چکیده
منابع مشابه
A New Approach for Monte Carlo Simulation of RAFT Polymerization
In this work, based on experimental observations and exact theoretical predictions, the kinetic scheme of RAFT polymerization is extended to a wider range of reactions such as irreversible intermediate radical terminations and reversible transfer reactions. The reactions which have been labeled as kinetic scheme are the more probable existing reactions as the theoretical point of view. The ...
متن کاملTransdimensional Monte Carlo Inversion of AEM Data
The majority of existing methods used for the inversion of airborne electromagnetic (AEM) data use what are generally called gradient-based optimization techniques. They typically minimize an objective function comprised of data misfit (e.g. least squares) and model regularization (e.g. roughness) terms. Since the problem is non-linear, an iterative search involving the matrix solution of equat...
متن کاملMonte Carlo Matrix Inversion Policy Evaluation
In 1950, Forsythe and Leibler (1950) introduced a statistical technique for finding the inverse of a matrix by characterizing the elements of the matrix inverse as expected values of a sequence of random walks. Barto and Duff (1994) subsequently showed relations between this technique and standard dynamic programming and temporal differencing methods. The advantage of the Monte Carlo matrix inv...
متن کاملMonte Carlo Quasi-heatbath by Approximate Inversion
When sampling the distribution P (~ φ) ∝ exp(−|A~ φ|2), a global heatbath normally proceeds by solving the linear system A~ φ = ~η, where ~η is a normal Gaussian vector, exactly. This paper shows how to preserve the distribution P (~ φ) while solving the linear system with arbitrarily low accuracy. Generalizations are presented. PACS numbers: 02.70.L, 02.50.N, 52.65.P, 11.15.H, 12.38.G In Monte...
متن کاملMonte Carlo Matrix Inversion and Reinforcement Learning
We describe the relationship between certain reinforcement learning (RL) methods based on dynamic programming (DP) and a class of unorthodox Monte Carlo methods for solving systems of linear equations proposed in the 1950's. These methods recast the solution of the linear system as the expected value of a statistic suitably defined over sample paths of a Markov chain. The significance of our ob...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Geophysical Journal International
سال: 2010
ISSN: 0956-540X
DOI: 10.1111/j.1365-246x.2010.04707.x