Automatic Semblance Picking by a Bottom-up Clustering Method

Yuqing Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish (US)
Title of host publicationSEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018
PublisherSociety of Exploration Geophysicists
StatePublished - Dec 14 2018


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