Register      Login
Exploration Geophysics Exploration Geophysics Society
Journal of the Australian Society of Exploration Geophysicists
RESEARCH ARTICLE

A comparison of binary and multiclass support vector machine models for volcanic lithology estimation using geophysical log data from Liaohe Basin, China

Dan Mou 1 2 Zhu-Wen Wang 1
+ Author Affiliations
- Author Affiliations

1 College of Geoexploration, Science and Technology, Jilin University, Changchun 130021, China.

2 Corresponding author. Email: mudan-main@163.com

Exploration Geophysics 47(2) 145-149 https://doi.org/10.1071/EG14114
Submitted: 3 November 2014  Accepted: 23 April 2015   Published: 15 May 2015

Abstract

Lithology estimation of rocks, especially volcanic lithology, is one of the major goals of geophysical exploration. In this paper, we propose the use of binary and multiclass support vector machine models with geophysical log data to estimate the volcanic lithology of the Liaohe Basin, China. Using neutron (CNL), density (DEN), acoustic (AC), deep lateral resistivity (RLLD), and gamma-ray (GR) log data from 40 wells (a total of 1200 log data points) in the Liaohe Basin, China, we first construct the binary support vector machine model to classify volcanic rock and non-volcanic rock. Then, we expand the binary model to a multiclass model using the approach of directed acyclic graphs, and construct multiclass models to classify six types of volcanic rocks: basalt, non-compacted basalt, trachyte, non-compacted trachyte, gabbro and diabase. To assess the accuracy of these two models, we compare their predictions with core data from four wells (at 800 different depth points in total). Results indicate that the accuracy of the binary and multiclass models are 98.4% and 87%, respectively, demonstrating that binary and multiclass support vector machine models are effective methods for classifying volcanic lithology.

Key words: directed acyclic graph, geophysical log data, parameter optimisation, support vector machine, volcanic lithology.


References

Chen, R., Gao, C.-Q., and Jin, Y.-Z., 2009, Application of crossplot technique to identify lithology of igneous rocks in Santanghu Basin: Lithologic Reservoirs, 21, 94–97
| 1:CAS:528:DC%2BD1MXmsFSksLk%3D&md5=297cac8ec5ce20cb57be6aae775e7a97CAS |

Cooper, G. R. J., and Cowan, D. R., 2009, Blocking geophysical borehole log data using the continuous wavelet transform: Exploration Geophysics, 40, 233–236
Blocking geophysical borehole log data using the continuous wavelet transform:Crossref | GoogleScholarGoogle Scholar |

Cristianini, N., and Scholkopf, B., 2002, Support vector machines and kernel methods – the new generation of learning machines: Artificial Intelligence Magazine, 23, 31–41

Dickson, B., and Beckitt, G., 2013, The application of Monte Carlo modelling to downhole total-count logging of uranium: part 1 - low grade mineralisation: Exploration Geophysics, 44, 56–62
The application of Monte Carlo modelling to downhole total-count logging of uranium: part 1 - low grade mineralisation:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXjsVymu7g%3D&md5=62ebd6c40a144903e43f3530d633d0e0CAS |

Hsu, C.-W., and Lin, C.-J., 2002, A comparison of methods for multiclass support vector machines: IEEE Transactions on Neural Networks, 13, 415–425
A comparison of methods for multiclass support vector machines:Crossref | GoogleScholarGoogle Scholar | 18244442PubMed |

Kavzoglu, T., and Colkesen, I., 2009, A kernel functions analysis for support vector machines for land cover classification: International Journal of Applied Earth Observation and Geoinformation, 11, 352–359
A kernel functions analysis for support vector machines for land cover classification:Crossref | GoogleScholarGoogle Scholar |

Li, K., Huang, H., and Tian, S., 2003, A novel multi-class SVM classifier based on DDAG: Pattern Recognition and Artificial Intelligence, 16, 44–47

Mabrouk, W. M., and Kamel, M. H., 2011, Shale volume determination using sonic, density and neutron data: Exploration Geophysics, 42, 155–158
Shale volume determination using sonic, density and neutron data:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXotVelsb4%3D&md5=81302332b338ec45ca41936d6751a8f5CAS |

Paasche, H., and Eberle, D., 2011, Automated compilation of pseudo-lithology maps from geophysical data sets: a comparison of Gustafson-Kessel and fuzzy c-means cluster algorithms: Exploration Geophysics, 42, 275–285
Automated compilation of pseudo-lithology maps from geophysical data sets: a comparison of Gustafson-Kessel and fuzzy c-means cluster algorithms:Crossref | GoogleScholarGoogle Scholar |

Roy, K. K., and Dutta, D. J., 1996, Normal and lateral log response for a 2-D borehole model: Exploration Geophysics, 27, 223–227
Normal and lateral log response for a 2-D borehole model:Crossref | GoogleScholarGoogle Scholar |

Vapnik, V. N., 1995, The nature of statistical learning theory: Springer-Verlag.

Vapnik, V. N., 1999, An overview of statistical learning theory: IEEE Transactions on Neural Networks, 10, 988–999
An overview of statistical learning theory:Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD1c%2FpsFSqtA%3D%3D&md5=1692852b69ca6f77db2849c481133d77CAS | 18252602PubMed |

Wang, G., Carr, T. R., Ju, Y., and Li, C, 2014, Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin: Computers & Geosciences, 64, 52–60
Identifying organic-rich Marcellus Shale lithofacies by support vector machine classifier in the Appalachian basin:Crossref | GoogleScholarGoogle Scholar |

Zuo, R., and Emmanuel, J. M., 2011, Support vector machine: a tool for mapping mineral prospectivity: Computers & Geosciences, 37, 1967–1975
Support vector machine: a tool for mapping mineral prospectivity:Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFaitb7N&md5=9bff02ac00e269b95ede778374ae41fdCAS |