Automatic Semblance Picking by a Bottom-up Clustering Method

Yuqing Chen

doi:10.1190/aiml2018-12.1

Automatic Semblance Picking by a Bottom-up Clustering Method

Yuqing Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English (US)
Title of host publication	SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018
Publisher	Society of Exploration Geophysicists
DOIs	https://doi.org/10.1190/aiml2018-12.1
State	Published - Dec 14 2018

Bibliographical note

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.

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10.1190/aiml2018-12.1

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title = "Automatic Semblance Picking by a Bottom-up Clustering Method",

abstract = "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.",

author = "Yuqing Chen",

note = "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.",

year = "2018",

month = dec,

day = "14",

doi = "10.1190/aiml2018-12.1",

language = "English (US)",

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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 -

Automatic Semblance Picking by a Bottom-up Clustering Method

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