页岩气储层预测的多标签主动学习算法

针对页岩气储层数据获取困难、标签稀缺、标注成本高昂的问题,提出一种多标准主动查询的多标签学习(MAML)算法.首先,考虑样本的信息性和代表性来对样本进行初步处理;其次,加入包括属性差异性和标签丰富性的样本丰富性约束,在此基础上选择有价值的样本进行标签查询;最后,利用多标签学习算法来预测剩余样本的标签.通过11个Yaho...

Multi-label active learning algorithm for shale gas reservoir prediction

Concerning the problems of the difficulties in obtaining， the limitation of labels， and the high cost of labeling of shale gas reservoir data， a Multi-standard Active query Multi-label Learning （MAML） algorithm was proposed. First of all， with the consideration of the informativeness and representativeness of the samples， the preliminary processing was performed on the samples. Secondly， the sample richness constraints including attribute differences and label richness were added， on this basis， the valuable samples were selected and the labels of these samples were queried. Finally， a multi-label learning algorithm was used to predict the labels of the remaining samples. Through experiments on eleven Yahoo datasets， the MAML algorithm was compared with popular multi-label learning algorithms and active learning algorithms， and the superiority of the MAML algorithm was proved. Then， the experiments were extended to four real shale gas well logging datasets. In these experiments， compared with the multi-label learning algorithms： Multi-Label Multi-Label K-Nearest Neighbor （ML-KNN）， BackPropagation for Multi-Label Learning （BP-MLL）， multi-label learning with GLObal and loCAL label correlation （GLOCAL） and active learning by QUerying Informative and Representative Examples （QUIRE）， the MAML algorithm improved the average prediction accuracy of comprehensive quality of shale gas reservoirs by 45 percentage points， 68 percentage points， 68 percentage points， and 51 percentage points， respectively. The practicability and superiority of the MAML algorithm in the prediction of shale gas reservoir sweet spots are fully proved by these experimental results.