Seismic tomography with the reversible jump algorithm
نویسندگان
چکیده
S U M M A R Y The reversible jump algorithm is a statistical method for Bayesian inference with a variable number of unknowns. Here, we apply this method to the seismic tomography problem. The approach lets us consider the issue of model parametrization (i.e. the way of discretizing the velocity field) as part of the inversion process. The model is parametrized using Voronoi cells with mobile geometry and number. The size, position and shape of the cells defining the velocity model are directly determined by the data. The inverse problem is tackled within a Bayesian framework and explicit regularization of model parameters is not required. The mobile position and number of cells means that global damping procedures, controlled by an optimal regularization parameter, are avoided. Many velocity models with variable numbers of cells are generated via a transdimensional Markov chain and information is extracted from the ensemble as a whole. As an aid to interpretation we visualize the expected earth model that is obtained via Monte Carlo integration in a straightforward manner. The procedure is particularly adept at imaging rapid changes or discontinuities in wave speed. While each velocity model in the final ensemble consists of many discontinuities at cell boundaries, these are smoothed out in the averaged ensemble solution while those required by the data are reinforced. The ensemble of models can also be used to produce uncertainty estimates and experiments with synthetic data suggest that they represent actual uncertainty surprisingly well. We use the fast marching method in order to iteratively update the ray geometry and account for the non-linearity of the problem. The method is tested here with synthetic data in a 2-D application and compared with a subspace method that is a more standard matrix-based inversion scheme. Preliminary results illustrate the advantages of the reversible jump algorithm. A real data example is also shown where a tomographic image of Rayleigh wave group velocity for the Australian continent is constructed together with uncertainty estimates.
منابع مشابه
Multi-scale Bayesian Based Horizon Matchings Across Faults in 3d Seismic Data
Oil and gas exploration decisions are made based on inferences obtained from seismic data interpretation. While 3D seismic data become widespread and the data-sets get larger, the demand for automation to speed up the seismic interpretation process is increasing as well. However, the development of intelligent tools which can do more to assist interpreters has been difficult due to low informat...
متن کاملBayesian deconvolution of noisy filtered point processes
The detection and estimation of filtered point processes using noisy data is an essential requirement in many seismic, ultrasonic, and nuclear applications. In this paper, we address this joint detection/estimation problem using a Bayesian approach, which allows us to easily include any relevant prior information. Performing Bayesian inference for such a complex model is a challenging computati...
متن کاملA comparison of reversible jump MCMC algorithms for DNA sequence segmentation using hidden Markov models
This paper describes a Bayesian approach to determining the number of hidden states in a hidden Markov model (HMM) via reversible jump Markov chain Monte Carlo (MCMC) methods. Acceptance rates for these algorithms can be quite low, resulting in slow exploration of the posterior distribution. We consider a variety of reversible jump strategies which allow inferences to be made in discretely obse...
متن کاملOptimizing the actuation of musculoskeletal model by genetic algorithm to simulate the vertical jump
In human body movement simulation such as vertical jump by a forward dynamic model, optimal control theories must be used. In the recent years, new methods were created for solving optimization problems which they were adopted from animal behaviors and environment events such as Genetic algorithm, Particle swarm and Imperialism competitive. In this work, the skeletal model was constructed by Ne...
متن کاملEfficient Bayes factor estimation from the reversible jump output
We propose a class of estimators of the Bayes factor which is based on an extension of the bridge sampling identity of Meng & Wong (1996) and makes use of the output of the reversible jump algorithm of Green (1995). Within this class we give the optimal estimator and also a suboptimal one which may be simply computed on the basis of the acceptance probabilities used within the reversible jump a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009