KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Earth Science and Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/653013
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AbstractSemblance 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.
CitationChen Y (2018) Automatic Semblance Picking by a Bottom-up Clustering Method. SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018. Available: http://dx.doi.org/10.1190/aiml2018-12.1.
SponsorsWe thank the sponsors of the CSIM consortium, the KAUST Supercomputing Laboratory and IT Research Computing Group.
PublisherSociety of Exploration Geophysicists
JournalSEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018