Proceedings of the 2016 International Conference on Education, Management and Computer Science

An Application of Spearman’s Rho in Blind Deconvolution of Seismic Data

Authors
Rongrong Wang, Fei Xu, Xiaobo Zhou
Corresponding Author
Rongrong Wang
Available Online May 2016.
DOI
10.2991/icemc-16.2016.182How to use a DOI?
Keywords
Spearman’s rho; Seismic wavelet; Blind deonvolution; Mutual information; Inverse filter
Abstract

The paper proposes a novel seismic blind deconvolution approach based on the Spearman’s rho in the case of band-limited seismic data with a low dominant frequency and short data records. The spearman’s rho is a measure of the dependence between two continuous random variables without the influence of the marginal distributions, by which a new criterion for blind deconvolution is constructed. The optimization program for new criterion of blind deconvolution is performed by applying Neidell’s wavelet model to the inverse filter. The simulation test and actual seismic data processing results show that this method can effectively expand the frequency band of seismic records, and enhance the resolution of seismic date.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Education, Management and Computer Science
Series
Advances in Intelligent Systems Research
Publication Date
May 2016
ISBN
978-94-6252-202-2
ISSN
1951-6851
DOI
10.2991/icemc-16.2016.182How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Rongrong Wang
AU  - Fei Xu
AU  - Xiaobo Zhou
PY  - 2016/05
DA  - 2016/05
TI  - An Application of Spearman’s Rho in Blind Deconvolution of Seismic Data
BT  - Proceedings of the 2016 International Conference on Education, Management and Computer Science
PB  - Atlantis Press
SP  - 920
EP  - 925
SN  - 1951-6851
UR  - https://doi.org/10.2991/icemc-16.2016.182
DO  - 10.2991/icemc-16.2016.182
ID  - Wang2016/05
ER  -