A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data
A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data
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DOI：
10.1139/cgj-2017-0709
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发表时间：
2019-08
影响因子：
3.6
通讯作者：
Hui Wang;Xiangrong Wang;J. Wellmann;R. Liang
中科院分区：
文献类型：
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作者：
Hui Wang;Xiangrong Wang;J. Wellmann;R. Liang
本文提出了一种新的视角来理解锥入度数据集的空间和统计模式并使用这些模式识别土壤分层。同时考虑数据点之间物理空间（即沿深度）的局部一致性和特征空间（即 logQt-logFrspace，其中 Q 是归一化尖端阻力，Fris 是归一化摩擦比，或罗伯逊图）的统计相似性。所提出的方法本质上由两部分组成：（i）使用贝叶斯推理框架的模式检测方法和（ii）使用罗伯逊图的模式解释协议。第一部分是该方法的数学核心，它从输入数据集中推断物理空间中的空间模式和特征空间中的统计模式；第二部分将抽象模式转换为具有不同土壤行为类型的多个土层的直观空间配置。该方法的优点包括以自动和完全无监督的方式进行概率土壤分类和土壤分层识别。所提出的方法已在 MATLAB R2015b 和 Python 3.6 中实现，并使用各种数据集进行了测试，包括合成和真实的锥体贯入测试测深。结果表明，该方法能够准确、自动地检测土层，具有量化的不确定性和合理的计算成本。
This paper presents a novel perspective to understanding the spatial and statistical patterns of a cone penetration dataset and identifying soil stratification using these patterns. Both local consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQt–logFrspace, where Qtis the normalized tip resistance and Fris the normalized friction ratio, or the Robertson chart) between data points are considered simultaneously. The proposed approach, in essence, consists of two parts: (i) a pattern detection approach using the Bayesian inferential framework and (ii) a pattern interpretation protocol using the Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of the proposed approach include probabilistic soil classification and identification of soil stratification in an automatic and fully unsupervised manner. The proposed approach has been implemented in MATLAB R2015b and Python 3.6, and tested using various datasets including both synthetic and real-world cone penetration test soundings. The results show that the proposed approach can accurately and automatically detect soil layers with quantified uncertainty and reasonable computational cost.