Predicting hydrofacies and hydraulic conductivity from direct‐push data using a data‐driven relevance vector machine approach: Motivations, algorithms, and application
Predicting hydrofacies and hydraulic conductivity from direct‐push data using a data‐driven relevance vector machine approach: Motivations, algorithms, and application
复制标题
使用数据驱动的相关向量机方法根据直推数据预测水相和水力传导率：动机、算法和应用
DOI：
10.1002/2014wr015452
复制
发表时间：
2015
影响因子：
5.4
通讯作者：
A. Rivera
中科院分区：
文献类型：
--
作者：
D. Paradis;R. Lefebvre;E. Gloaguen;A. Rivera
渗透系数K的空间非均匀性对地下水流和溶质运移具有重要的控制作用。只要存在将地球物理数据与K联系起来的可靠关系，就可以使用间接地球物理数据来成像K的非均匀空间分布。本文提出了一种非参数学习机的方法来预测含水层K从锥渗透率测试（CPT）耦合土壤水分和电阻率探头（SMR）使用相关向量机（RVMs）。学习机的方法被证明与应用程序的异质松散的沿岸的含水层在12平方公里的子流域，K和多参数CPT/SMR探测之间的关系显得复杂。我们的方法涉及模糊聚类定义水文相（HF）的基础上CPT/SMR和K数据之前的训练的RVM的高频识别和K预测的基础上的CPT/SMR数据。该学习机是由代表研究区域的共定位训练数据集构建的，该数据集包括来自段塞试验的K数据和以15 cm的常见垂直分辨率放大的CPT/SMR数据。在训练之后，通过交叉验证来评估学习机的预测能力，交叉验证使用从训练数据集中保留的数据以及来自在训练过程期间未使用的流量计测试的K数据。结果表明，从学习机的HF和K的预测是一致的液压试验。CPT/SMR数据和基于RVM的学习机的组合使用被证明是强大而有效的，用于描述松散含水层的高分辨率K非均质性。
The spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up‐scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM‐based learning machine proved to be powerful and efficient for the characterization of high‐resolution K heterogeneity for unconsolidated aquifers.
影响因子：
--
作者：
Hsu, CW;Lin, CJ
通讯作者：
Lin, CJ