Abstract This paper proposes a method for estimating trends and detecting layer boundaries in depth-dependent soil data based on a least absolute shrinkage statistical operator (lasso). Although the lasso appears to be promising for subsurface modeling because no predetermined basis functions or stratification models are required, it does not provide information on the uncertainty of its estimated solution, i.e., a point estimate. In subsurface modeling, however, characterization of uncertainty is pivotal because soil data can be (spatially) sparse and noisy. A lasso-based method that can quantify its estimation accuracy while preserving its attractive sparsity feature is proposed. The performance of this sparse Bayesian lasso (SBLasso) is demonstrated through numerical tests and an actual case study of its accuracy of trend estimation and layer boundary detection. The degree of accuracy or inaccuracy of estimation provided by the SBLasso clearly corresponds to data quality, such as the number of available data points, noise level, and noise correlation. A method of soil stratification based on SBLasso was also proposed, and the stratification results by SBLasso were compared with those produced by existing methods for validation.

中文翻译：

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使用稀疏贝叶斯套索的深度相关土壤数据中的趋势估计和层边界检测

摘要 本文提出了一种基于最小绝对收缩统计算子（lasso）的深度相关土壤数据中的趋势估计和层边界检测方法。尽管 lasso 似乎有希望用于地下建模，因为不需要预定的基函数或分层模型，但它不提供有关其估计解的不确定性的信息，即点估计。然而，在地下建模中，不确定性的表征至关重要，因为土壤数据可能（空间上）稀疏且嘈杂。提出了一种基于套索的方法，该方法可以量化其估计精度，同时保留其有吸引力的稀疏特征。这种稀疏贝叶斯套索 (SBlasso) 的性能通过数值测试和实际案例研究来证明其趋势估计和层边界检测的准确性。SBLasso 提供的估计准确度或不准确度与数据质量明显对应，例如可用数据点的数量、噪声水平和噪声相关性。还提出了一种基于SBLasso的土壤分层方法，并将SBLasso的分层结果与现有方法产生的分层结果进行比较以进行验证。