A semi-supervised learning framework for gas chimney detection based on sparse autoencoder and TSVM

Abstract

Supervised classifers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifer, is widely used. However, the lack of labeled data usually limits applications of the supervised classifers. To get more accurate seismic interpretation results, we take advantage of the information of unlabeled data by unsupervised feature extraction and semi-supervised classifcation methods. The sparse autoencoder (SAE) is an unsupervised learning method that can extract the features of data without labels, and the transductive support vector machine (TSVM) is a semi-supervised method that trains a classifer according to both labeled and unlabeled data. In this paper, we propose a semi-supervised learning framework that combines SAE and TSVM to detect gas chimneys. In this framework, SAE is used to extract features from data and TSVM is used to classify the labeled and unlabeled features. Therefore, the unlabeled data is taken advantage of in both unsupervised feature extraction and semi-supervised classifcation to improve accuracy. In order to improve the precision of detection, the attributes of neighborhood regions are also utilized. Due to the information learned from plenty of unlabeled data, the proposed framework performs well. Numerical experiments are carried out on sample sets and feld data. The proposed framework has higher testing accuracy than the traditional MLP method, especially when the labeled training set is small. In feld data experiments, the proposed framework also gets good prediction results for gas chimney locations.