Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

Bodin, T. and Sambridge, M., 2009. Seismic tomography with the reversible jump algorithm, Geophysical Journal International, 178(3), p.1411-1436.

Bodin, T., Sambridge, M., Tkalčić, H., Arroucau, P., Gallagher, K., and Rawlinson, N., 2012. Transdimensional inversion of receiver functions and surface wave dispersion, Journal of geophysical research: solid earth, 117(B2).

Bottero, A., Gesret, A., Romary, T., Noble, M., and Maisons, C., 2016. Stochastic seismic tomography by interacting Markov chains, Geophysical Journal International, 207(1), p.374-392.

Cho, Y., Gibson Jr, R.L., and Zhu, D., 2018. Quasi 3D transdimensional Markov-chain Monte Carlo for seismic impedance inversion and uncertainty analysis, Interpretation, 6(3), p.T613-T624.

Francis, A., 2005. Limitations of deterministic and advantages of stochastic seismic inversion, CSEG Recorder, 30(2), p.5-11.

Gelfand, A.E. and Smith, A.F., 1990. Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association, 85(410), p.398-409.

Izzatullah, M., Van Leeuwen, T., and Peter, D., 2021. Bayesian seismic inversion: a fast sampling Langevin dynamics Markov chain Monte Carlo method, Geophysical Journal International, 227(3), p.1523-1553.

Lee, H.N., 2023. Markov Chain Monte Carlo with MATLAB Code (in Korean), Vol. 1, Free academy, Seoul, Korea, 498p.

Lee, J.Y. and Lee, G.J., 2022. Bayesian Data Analysis, Vol. 1, KNOU press, Seoul, Korea, 371p.

Link, W.A. and Eaton, M.J., 2012. On thinning of chains in MCMC, Methods in Ecology and Evolution, 3(1), p.112-115.

Lorentzen, R.J., Bhakta, T., Grana, D., Luo, X., Valestrand, R., and Nævdal, G., 2020. Simultaneous assimilation of production and seismic data: application to the Norne field, Computational Geosciences, 24(2), p.907-920.

Moon, S., Cho, Y., Sim, Y., Lee, D., and Jun, H., 2024. Stochastic seismic acoustic impedance inversion via a Markov-chain Monte Carlo method using a single GPU card, Journal of Applied Geophysics, 224, p.105357.

Riabiz, M., Chen, W.Y., Cockayne, J., Swietach, P., Niederer, S.A., Mackey, L., and Oates, C.J., 2022. Optimal thinning of MCMC output, Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(4), p.1059-1081.

Rwechungura, R., Suwartadi, E., Dadashpour, M., Kleppe, J., and Foss, B., 2010, March. The Norne field case-A unique comparative case study, In SPE Intelligent Energy International Conference and Exhibition (p.SPE-127538), SPE.

Sambridge, M., Gallagher, K., Jackson, A., and Rickwood, P., 2006. Trans-dimensional inverse problems, model comparison and the evidence, Geophysical Journal International, 167(2), p.528-542.

Tarantola, A., 2005. Inverse problem theory and methods for model parameter estimation, Society for industrial and applied mathematics.

Torres-Verdin, C., Victoria, M., Merletti, G., and Pendrel, J., 1999. Trace-based and geostatistical inversion of 3-D seismic data for thin-sand delineation: An application in San Jorge Basin, Argentina, The Leading Edge, 18(9), p.1070-1077.

Zhang, R., Czado, C., and Sigloch, K., 2013. A Bayesian linear model for the high-dimensional inverse problem of seismic tomography, The Annals of Applied Statistics, p.1111-1138.

Zhang, R., Sen, M.K., Phan, S., and Srinivasan, S., 2012. Stochastic and deterministic seismic inversion methods for thin-bed resolution, Journal of Geophysics and Engineering, 9(5), p.611-618.

Zhu, D. and Gibson, R., 2016. Seismic inversion and uncertainty analysis using a transdimensional Markov chain Monte Carlo method. Society of Exploration Geophysicists, SEG Technical Program Expanded Abstracts 2016, p.3666-3671.