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A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

Benbernou, R. and Warwick, K. (2007) A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events. In: Icspc: 2007 IEEE International Conference on Signal Processing and Communications, Vols 1-3, Proceedings. IEEE, New York, pp. 1331-1334. ISBN 9781424412358

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Abstract/Summary

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Item Type:Book or Report Section
Divisions:Faculty of Science > School of Systems Engineering
ID Code:14355
Uncontrolled Keywords:Seismic horizon, auto-tracker, fuzzy inference, pattern recognition, neural networks
Additional Information:Proceedings Paper IEEE International Conference on Signal Processing and Communications NOV 24-27, 2007 Dubai, U ARAB EMIRATES
Publisher:IEEE

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