Semblance picking is an important but tedious labor-intensive processing procedure in the petroleum industry. For a large 3D dataset, this task becomes extremely time-consuming. In this paper, we present an automatic semblance picking technique based on the K-means clustering algorithm. K-means clustering method can automatically partition different clusters of energy in the semblance spectrum into different groups. The centroid of each group is the automatically picked semblance point. A synthetic and field data example is shown in this paper to illustrate the effectiveness of this method.
|Title of host publication
|SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018
|Society of Exploration Geophysicists
|Published - Dec 14 2018
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank the sponsors of the CSIM consortium, the KAUST Supercomputing Laboratory and IT Research Computing Group.