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首页> 外文会议>International Conference on Environmental and Engineering Geophysics >Electromagnetic Full Waveform Inversion based on Bayesian Markov-chain Monte-Carlo Method
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Electromagnetic Full Waveform Inversion based on Bayesian Markov-chain Monte-Carlo Method

机译：基于Bayesian Markov-Chain Monte-Carlo方法的电磁全波形反转

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Straight-ray-based inversion technique to estimate attenuation rate from electromagnetic tomography in coal mine has been available to geophysicists for over twenty years. This method gives a good computational efficiency but not a satisfy resolution. On account of the increasing computational power, accurate forward modeling can be included in advanced inversion approaches such that the full-waveform content can be exploited. Conventional full-waveform inversion methods are referred to as deterministic and are based on the minimization of an error term between the forward responses and the observed waveforms at each trace location. It is commonly used to give a 'best estimate' or 'most likely' case, regardless of the attendant uncertainties - which is the nature of most geophysical problems. Deterministic method is also easy to be trapped in a local minimum if there is not a good start model. To address this limitation, we present a probabilistic full-waveform inversion method in Bayesian formulation. In this formulation, solution to inverse problem is a probability density function refers to as posteriori distribution which describes all information available. Make use of Bayesian theorem combined with Markov-chain Monte-Carlo (MCMC) sampling, we can generate stochastic realizations from the posteriori distribution of model parameters. Bayesian-MCMC methods can incorporate any information that can be expressed in terms of probabilities and provide more precise model parameter even with an arbitrary initial model. In case study, we explore the performance of electromagnetic full-waveform inversion with MCMC through a simple synthetic tomographic example in coal mine, dielectric permittivity values of a moisture anomaly in coal seam can be obtained with a good resolution. Results demonstrate the feasibility of our statistical inversion method.
机译：基于直射的倒置技术来估算煤矿电磁断层摄影衰减率的衰减率已有二十年来的地球物理学家。该方法提供了良好的计算效率，但不能满足分辨率。由于计算能力的增加，准确的前瞻性建模可以包含在高级反转方法中，使得可以利用全波形内容。传统的全波形反转方法称为确定性，并且基于在每个跟踪位置处的前向响应和观察到的波形之间的误差项的最小化。它通常用于给予“最佳估计”或“最有可能”的情况，无论具有服务员的不确定性如何 - 这是大多数地球物理问题的性质。如果没有良好的启动模型，确定性方法也容易被捕获在局部最小值。为了解决这一限制，我们在贝叶斯配方中提出了一种概率的全波形反演方法。在该制剂中，逆问题的解决方案是概率密度函数是指描述所有可用信息的后验分布。利用贝叶斯定理与马尔可夫链条Monte-Carlo（MCMC）采样相结合，我们可以从模型参数的后验分布中产生随机实现。 Bayesian-MCMC方法可以包含任何可以在概率中表达的信息，即使具有任意初始模型，也可以提供更精确的模型参数。在研究中，我们通过煤矿中简单的合成断层法示例探讨了MCMC的电磁全波形反转的性能，煤层中水分异常的介电常数值可以通过良好的分辨率获得。结果证明了我们统计反演方法的可行性。

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