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

Supervised classifiers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifier, is widely used. However, the lack of labeled data usually limits applications of the supervised classifiers. To get more accurate seis...

Full description

Saved in:
Bibliographic Details
Published in:Journal of geophysics and engineering Vol. 16; no. 1; pp. 52 - 61
Main Authors: Xu, Pengcheng, Lu, Wenkai, Wang, Benfeng
Format: Journal Article
Language:English
Published: London Oxford University Press 01.02.2019
Subjects:
Accuracy
Algorithms
Classification
Engineering
Geophysics
Lithology
Neural networks
Support vector machines
transductive support vector machine
gas chimney detection
multi-attribute classification
semi-supervised learning
sparse autoencoder
ISSN:1742-2132, 1742-2140
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Supervised classifiers play important roles in seismic interpretation. Especially in gas chimney detection, the multilayer perceptron (MLP), a supervised classifier, is widely used. However, the lack of labeled data usually limits applications of the supervised classifiers. To get more accurate seismic interpretation results, we take advantage of the information of unlabeled data by unsupervised feature extraction and semi-supervised classification 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 classifier 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 classification 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 field data. The proposed framework has higher testing accuracy than the traditional MLP method, especially when the labeled training set is small. In field data experiments, the proposed framework also gets good prediction results for gas chimney locations.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-2132
1742-2140
DOI:10.1093/jge/gxy004