Mamun, Abdullah-alPereira, FelipeRahunanthan, A.2019-07-022019-07-022018-07-049783319951645https://hdl.handle.net/10735.1/6673Full text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided link to the article). Non UTD affiliates will find the web address for this item by clicking the Show full item record link and copying the "relation.uri" metadata.In subsurface characterization using a history matching algorithm subsurface properties are reconstructed with a set of limited data. Here we focus on the characterization of the permeability field in an aquifer using Markov Chain Monte Carlo (MCMC) algorithms, which are reliable procedures for such reconstruction. The MCMC method is serial in nature due to its Markovian property. Moreover, the calculation of the likelihood information in the MCMC is computationally expensive for subsurface flow problems. Running a long MCMC chain for a very long period makes the method less attractive for the characterization of subsurface. In contrast, several shorter MCMC chains can substantially reduce computation time and can make the framework more suitable to subsurface flows. However, the convergence of such MCMC chains should be carefully studied. In this paper, we consider multi-MCMC chains for a single–phase flow problem and analyze the chains aiming at a reliable characterization.en©2018 Springer International Publishing AG, part of Springer NatureNumerical analysis--Acceleration of convergenceMarkov processesAquifers--Mathematical modelsHidden Markov modelsStochastic modelsMonte Carlo methodPermeable reactive barriersAlgorithmsarticleMamun, A., F. Pereira, and A. Rahunanthan. 2018. "Convergence analysis of MCMC methods for subsurface flow problems." Lecture Notes In Computer Science 10961: 305-317 doi:10.1007/978-3-319-95165-2_2210961