Table of Contents
Advances in Statistics
Volume 2014 (2014), Article ID 548070, 10 pages
http://dx.doi.org/10.1155/2014/548070
Research Article

Horizon Detection in Seismic Data: An Application of Linked Feature Detection from Multiple Time Series

1Department of Statistics, University of Leeds, Leeds LS2 9JT, UK
2Department of Statistics, University of Benghazi, P.O. Box 1308, Benghazi, Libya

Received 6 May 2014; Revised 30 July 2014; Accepted 12 August 2014; Published 9 September 2014

Academic Editor: Shuo-Jye Wu

Copyright © 2014 Robert G. Aykroyd and Fathi 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.

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.