Bayesian inversion with Markov chains—II. The one‐dimensional DC multilayer case
Bayesian inversion with Markov chains—II. The one‐dimensional DC multilayer case
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DOI：
10.1046/j.1365-246x.1999.00905.x
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发表时间：
1999-09
影响因子：
2.8
通讯作者：
J. Schott;M. Roussignol;M. Menvielle;Flavien R. Nomenjahanary
中科院分区：
文献类型：
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作者：
J. Schott;M. Roussignol;M. Menvielle;Flavien R. Nomenjahanary
在本文中，我们将报道贝叶斯推理在一维多层模型直流电阻率反演中的应用。后验概率分布是通过基于吉布斯采样器的马尔可夫过程来探索的。该过程将在没有附加先验信息的情况下导致不切实际的估计，该估计采取第二马尔可夫链的形式，其中转移核对应于光滑性约束。结果是每个参数的后验边缘概率，以及如果需要，参数对的联合概率。我们将结合一个理论例子讨论该方法的主要性质，并用来自不同背景的一些现场例子来说明它的能力。
SUMMARY In this paper, we will report on the application of Bayesian inference to DC resistivity inversion for 1-D multilayer models. The posterior probability distribution is explored through a Markov process based upon a Gibbs’s sampler. The process would lead to unrealistic estimates without additional prior information, which takes the form of a second Markov chain where the transition kernel corresponds to a smoothness constraint. The outcomes are posterior marginal probabilites for each parameter, as well as, if required, joint probabilities for pairs of parameters. We will discuss the main properties of the method in the light of a theoretical example and illustrate its capabilities with some field examples taken from various contexts.