Monte Carlo inversion of 3-D magnetic resonance measurements
نویسندگان
چکیده
منابع مشابه
Transdimensional 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 کاملMagnetic Hamiltonian Monte Carlo
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits non-canonical Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3 dimensions a subset of the dynamics map onto the mechanics of a charged particle coupled to a magnetic field. W...
متن کاملMonte Carlo study of the magnetic properties of the 3 D Hubbard Model
We investigate numerically the magnetic properties of the 3D Isotropic and Anisotropic Hubbard model at half-filling. The behavior of the transition temperature as a function of the anisotropic hopping parameter is qualitatively described. In the Isotropic model we measure the scaling properties of the susceptibility finding agreement with the magnetic critical exponents of the 3D Heisenberg mo...
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 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: 2014
ISSN: 1365-246X,0956-540X
DOI: 10.1093/gji/ggu091