Titlebook: Statistical Inversion of Electromagnetic Logging Data; Qiuyang Shen,Jiefu Chen,Zhu Han Book 2021 The Author(s), under exclusive license to

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Introduction,as the journey into the earth can never penetrate even the shell. For centuries, geoscientists have been practicing new technologies adopted from inter-disciplines of mechanics, physics, chemistry, and mathematics to improve the understanding of inside earth. As a planetary science, geology concerns

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Bayesian Inversion and Sampling Methods,r an ensemble of solutions instead of a unique one via a sampling process. The probabilistic equation is governed by the rule of Bayesian inference. In this chapter, we will introduce those fundamental concepts including Bayesian inference, Markov chain Monte Carlo method, as well as Metropolis-Hast

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Beyond the Random-Walk: A Hybrid Monte Carlo Sampling,es the sampling efficiency a big obstacle for any real-time data processing workflow. On the contrary, many deterministic optimizations follow a gradient update and have relatively fast searching speed compared with random move. One attractive realization is to combine two schemes, where people intr

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Interpret Model Complexity: Trans-Dimensional MCMC Method,eters given the observed azimuthal resistivity measurements. The statistical inversion resolves the local minimum problem in the deterministic methods and tells the uncertainty of model parameters via the statistical distribution. However, the effect of using traditional MCMC methods is challenged w

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Accelerated Bayesian Inversion Using Parallel Tempering,er, the observation tells us an inadequate performance when sampling a complex model. The decreased sampling efficiency is due to the dimensional changes. Hence, one possible solution comes to make MCMC methods more scalable and to be deployed on a high-performance computing system. The idea brings

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