Channel Characterization Using Support Vector Machine

Abstract

Rapid growth in the size of seismic data and the number of attributes cause to increase the significance of pattern recognition techniques in interpreting the seismic data. Unsupervised methods include k-means, self-organizing maps (SOM) and generative topographic maps (GTM) let interpreters do a preliminary interpretation and conclude relatively suitable information with no much primary data from studied area. On the other hand, utilizing supervised learning such as neural networks (NN) and support vector machines (SVM) by interpreters require some primary information from studied area to seed the existent facies and use these seeded samples as the input to the algorithm. In this study, to detect channel facies of one of the southwest hydrocarbon fields of Iran, we used k-means and SVM to train the second algorithm by using the extracted primary information from the first algorithm. Results show that the existent channels in the studied area have two different facies that can be detected by applied algorithms.