Aykroyd, RG and Hamed, FMO (2014) Horizon Detection in Seismic Data: An Application of Linked Feature Detection from Multiple Time Series. Advances in Statistics, 2014. 548070. ISSN 2356-6892
Abstract
Seismic studies are a key stage in the search for large scale underground features such as water reserves, gas pockets, or oil fields. Sound waves, generated on the earth’s surface, travel through the ground before being partially reflected at interfaces between regions with high contrast in acoustic properties such as between liquid and solid. After returning to the surface, the reflected signals are recorded by acoustic sensors. Importantly, reflections from different depths return at different times, and hence the data contain depth information as well as position. A strong reflecting interface, called a horizon, indicates a stratigraphic boundary between two different regions, and it is the location of these horizons which is of key importance. This paper proposes a simple approach for the automatic identification of horizons, which avoids computationally complex and time consuming 3D reconstruction. The new approach combines nonparametric smoothing and classification techniques which are applied directly to the seismic data, with novel graphical representations of the intermediate steps introduced. For each sensor position, potential horizon locations are identified along the corresponding time-series traces. These candidate locations are then examined across all traces and when consistent patterns occur the points are linked together to form coherent horizons.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2014 R. G. Aykroyd and F. M. O. Hamed. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Apr 2016 12:24 |
Last Modified: | 05 Apr 2016 12:24 |
Published Version: | http://dx.doi.org/10.1155/2014/548070 |
Status: | Published |
Publisher: | Hindawi Publishing Corporation |
Identification Number: | 10.1155/2014/548070 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:91307 |