Semi-supervised support vector regression based on data similarity and its application to rock-mechanics parameters estimation

Abstract

Rock-mechanics parameters such as Young’s modulus and Poisson’s ratio are critical to geomechanical analysis and resource exploration. Because these parameters come from laboratory measurement, they present some characteristics such as insufficient samples and contamination of outliers. In this paper, a novel semi-supervised support vector machine soft sensor is devised considering the characteristics of the parameters. First, it takes into account data similarity and selects labeled data set that are most similar to the continuous unlabeled data set at each iteration to improve estimation performance. Meanwhile, an outlier deletion algorithm is developed for a better similarity comparison. After that, a semi-supervised approach is presented for the estimation of rock-mechanics parameters, it can leverage continuous unlabeled data to train the model dynamically. Finally, the verification of our method is carried out on data set from UCI (University of California, Irvine) and several drilling sites. The results demonstrate that our method outperforms eight well-known methods in estimation accuracy.