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首页> 外文期刊>Pure and Applied Geophysics >A Hybrid Monte Carlo Method Based Artificial Neural Networks Approach for Rock Boundaries Identification: A Case Study from the KTB Bore Hole
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A Hybrid Monte Carlo Method Based Artificial Neural Networks Approach for Rock Boundaries Identification: A Case Study from the KTB Bore Hole

机译：基于混合蒙特卡罗方法的人工神经网络方法进行岩石边界识别：以KTB钻孔为例

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摘要

Identification of rock boundaries and structural features from well log response is a fundamental problem in geological field studies. However, in a complex geologic situation, such as in the presence of crystalline rocks where metamorphisms lead to facies changes, it is not easy to discern accurate information from well log data using conventional artificial neural network (ANN) methods. Moreover inferences drawn by such methods are also found to be ambiguous because of the strong overlapping of well log signals, which are generally tainted with deceptive noise. Here, we have developed an alternative ANN approach based on Bayesian statistics using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) inversion scheme for modeling the German Continental Deep Drilling Program (KTB) well log data. MCMC algorithm draws an independent and identically distributed (i.i.d) sample by Markov Chain simulation technique from posterior probability distribution using the principle of statistical mechanics in Hamiltonian dynamics. In this algorithm, each trajectory is updated by approximating the Hamiltonian differential equations through a leapfrog discrimination scheme. We examined the stability and efficiency of the HMC-based approach on "noisy" data assorted with different levels of colored noise. We also perform uncertainty analysis by estimating standard deviation (STD) error map of a posteriori covariance matrix at the network output of three types of lithofacies over the entire length of the litho section of KTB. Our analyses demonstrate that the HMC-based approach renders robust means for classification of complex lithofacies successions from the KTB borehole noisy signals, and hence may provide a useful guide for understanding the crustal inhomogeneity and structural discontinuity in many other tectonically critical and complex regions.
机译：通过测井响应识别岩石边界和结构特征是地质野外研究的一个基本问题。但是，在复杂的地质情况下，例如存在变质作用导致相变的结晶岩，使用常规的人工神经网络（ANN）方法很难从测井数据中识别出准确的信息。此外，由于测井信号的强烈重叠，通常也被欺骗性噪声污染，因此用这种方法得出的推论也被模棱两可。在这里，我们已经基于贝叶斯统计数据，使用混合蒙特卡洛（HMC）/马尔可夫链蒙特卡洛（MCMC）反演方案的概念，开发了一种替代性的ANN方法，用于对德国大陆深层钻探计划（KTB）测井数据进行建模。 MCMC算法是利用哈密顿动力学中的统计力学原理通过后验概率分布通过马尔可夫链模拟技术从后验概率分布中提取独立且均匀分布的（i.i.d）样本的。在该算法中，通过跳跃鉴别方案通过近似汉密尔顿微分方程来更新每个轨迹。我们检查了基于HMC的方法在各种色彩噪声水平不同的“嘈杂”数据上的稳定性和效率。我们还通过估算KTB岩石段整个长度上三种类型的岩相的网络输出处的后验协方差矩阵的标准差（STD）误差图来执行不确定性分析。我们的分析表明，基于HMC的方法为从KTB钻孔噪声信号分类复杂岩相演替提供了强有力的手段，因此可能为理解许多其他构造关键和复杂区域的地壳非均质性和结构不连续性提供有用的指导。

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