论文标题
使用弹性特性的贝叶斯优化属于岩相分类的贝叶斯优化的支持 - 矢量机
Support-vector-machine with Bayesian optimization for lithofacies classification using elastic properties
论文作者
论文摘要
我们研究了贝叶斯优化（BO）的适用性，以优化与支持 - 矢量机（SVM）相关的超参数，以便使用UKC的East Central Graben中的井数据得出的弹性属性对相分类。现场数据集的跨图产品似乎已成功地用非线性边界进行了分类。尽管在BO方案中需要预先确定一些因素，例如迭代编号可以处理预测准确性和计算成本之间的权衡，但这种方法有效地降低了与SVM架构有关的可能的人类主观性。根据地下客观技术评估，我们提出的工作流可能对资源探索和开发有益。
We investigate an applicability of Bayesian-optimization (BO) to optimize hyperparameters associated with support-vector-machine (SVM) in order to classify facies using elastic properties derived from well data in the East Central Graben, UKCS. The cross-plot products of the field dataset appear to be successfully classified with non-linear boundaries. Although there are a few factors to be predetermined in the BO scheme such as an iteration number to deal with a trade-off between the prediction accuracy and the computational cost, this approach effectively reduces possible human subjectivity connected to the architecture of the SVM. Our proposed workflow might be beneficial in resource-exploration and development in terms of subsurface objective technical evaluations.
