TY - GEN
T1 - Automatic Semblance Picking by a Bottom-up Clustering Method
AU - Chen, Yuqing
N1 - KAUST 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.
PY - 2018/12/14
Y1 - 2018/12/14
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/653013
UR - https://library.seg.org/doi/10.1190/AIML2018-12.1
U2 - 10.1190/aiml2018-12.1
DO - 10.1190/aiml2018-12.1
M3 - Conference contribution
BT - SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018
PB - Society of Exploration Geophysicists
ER -