Applying regularized logistic regression (RLR) for the discrimination of sediment facies in reservoirs based on composite fingerprints
Applying regularized logistic regression (RLR) for the discrimination of sediment facies in reservoirs based on composite fingerprints
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基于复合指纹的正则化逻辑回归（RLR）判别储层沉积相
DOI：
10.1007/s11368-016-1627-7
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发表时间：
2017
影响因子：
3.6
通讯作者：
J Baade
中科院分区：
文献类型：
--
作者：
Reinwarth;J K Miller;C Glotzbach;K M Rowntree ;J Baade
目的土壤和沉积物可以根据“复合指纹”，即一组适合区分的物理和化学性质来区分。目前，统计逐步变量选择方法经常被应用于复合指纹识别，尽管它们受到了严厉的批评。在这里，我们测试了正则化Logistic回归(RLR)作为一种替代方法在水库淤积研究的背景下，其中大坝后相将与大坝前相区分。材料和方法对位于克鲁格国家公园的四个水库的坝前相和坝后相进行了粒度组成、颜色和乳酸钙可溶磷(PCAL)含量的研究。将RLR应用于训练数据，识别出复合指纹。拟合出的回归模型用于对训练数据集中未涉及的样本进行分类。为了进行比较，变量选择采用逐步判别函数分析(DFA)，样本分类采用线性判别分析(LDA)。通过对比现场解释和分类结果，验证了这两种方法的有效性。分析基于蒙特卡罗模拟和合成数据集，以量化不确定度并增强方法的可比性。结果和讨论RLR和逐步DFA识别粒度参数和PCAL含量对于相识别特别有用。忽略和考虑潜在的抽样偏差，这两种方法分别导致≤3%和5%的错误分类。在蒙特卡罗模拟中，RLR的性能优于逐步DFA/LDA，尽管错误率没有显著差异(p=100.84)。RLR使用的指纹属性平均减少了12%。此外，与从LDA计算的概率相比，RLR群成员的派生概率代表了更可靠的分类结论度量，这在错误分类样本的显著较低(p<；约0.001)的概率残差中显而易见。当每组数据满足多元正态分布且组内协方差矩阵相等时，逐步DFA/LDA比RLR具有更低的误分率。结论RLR是一种新的储层沉积相判别工具，更广泛地适用于需要区分土壤和沉积物的研究。虽然分步过程在实践中常常表现得同样好，但我们不鼓励使用它们来识别复合指纹，因为涉及具有虚假区分能力的变量的次优变量选择的风险。
PurposeSoils and sediments can be distinguished based on “composite fingerprints”, i.e., sets of physical and chemical properties that are suitable for discrimination. At present, statistical stepwise variable selection methods are frequently applied to identify composite fingerprints, although they have been seriously criticized. Here, we test regularized logistic regression (RLR) as an alternative approach in the context of a reservoir siltation study where the post-dam facies is to be distinguished from the pre-dam facies.Materials and methodsThe pre- and post-dam facies of four reservoirs located in the Kruger National Park were examined with respect to grain size composition, color, and content of calcium-lactate leachable phosphorus (PCAL). A composite fingerprint was identified applying RLR to training data. The fitted regression model was used for the classification of samples not involved in the training dataset. For comparison, variable selection was performed with stepwise discriminant function analysis (DFA) and samples were classified by applying linear discriminant analysis (LDA). Both approaches were validated by comparing field interpretation and classification results. The analysis was extended based on Monte Carlo simulations and synthetic datasets to quantify uncertainties and to enhance the method comparison.Results and discussionRLR and stepwise DFA identify grain size parameters andPCALcontent to be particularly useful for the facies discrimination. Neglecting and taking into account a potential sampling bias, both approaches lead to ≤3 and 5% misclassifications, respectively. RLR outperforms stepwise DFA/LDA in Monte Carlo simulations, although misclassification rates do not significantly differ (p= 0.84). RLR uses on average 12% less fingerprint properties. Moreover, RLR-derived probabilities of group membership represent a more reliable measure for classification conclusiveness than probabilities calculated from LDA, which is evident in significantly lower (p< 0.001) probability residuals for misclassified samples. Stepwise DFA/LDA reveals lower misclassification rates than RLR when data fulfill multivariate normality in each group and equal within-group covariance matrices.ConclusionsRLR is an innovative tool for the discrimination of sediment facies in reservoirs and, more generally, for studies requiring the discrimination of soils and sediments. Although stepwise procedures will in practice often perform similarly well, we discourage their use for the identification of composite fingerprints due to the risk of suboptimal variable selection involving variables with spurious discriminatory power.
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