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Examples of multi-attribute, neural network-based seismic object detection

By
P. De Groot
P. De Groot
dGB Earth Sciences BV, 7511 AE Enschede, The Netherlands (e-mail: paul.degroot@dgb-group.com)
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H. Ligtenberg
H. Ligtenberg
dGB Earth Sciences BV, 7511 AE Enschede, The Netherlands (e-mail: paul.degroot@dgb-group.com)
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T. Oldenziel
T. Oldenziel
dGB Rotterdam BV, Taborstraat 12, The Netherlands
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D. Connolly
D. Connolly
dGB-USA LLC, One Sugar Creek Center Boulevard, Suite 935, Sugar Land, TX 77478, USA
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P. Meldahl
P. Meldahl
Statoil AS, N-4001 Stavanger, Norway
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Published:
January 01, 2004

Abstract

Certain seismic objects, like faults and gas chimneys, are often difficult to delineate using conventional attribute analysis. Many attributes contain useful information about the target object but each new attribute provides a new and different view of the data. The challenge is to find the optimal attribute for a specific interpretation. In this paper the optimal attribute is found with a pattern recognition approach based on multi-dimensional/multi-attributes and neural network modelling. Multi-dimensional attributes, as opposed to point attributes, can provide the spatial information on the seismic objects. The role of the neural network is to classify the input attributes into two or more output classes. Neural networks are trained on seismic attributes extracted at representative example locations that are manually picked by a seismic interpreter. This approach is a form of supervised learning in which the network learns to recognize certain seismic responses associated with the identified target objects. Application of the trained network yields an ‘object probability’ cube for the target object. Essentially, the neural network can target any seismic or geological feature requiring detailed analysis. In this paper the method is described and examples are shown of gas chimneys, faults, salt domes and 4D anomalies. Some interpretation aspects are discussed.

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Contents

Geological Society, London, Memoirs

3D Seismic Technology: Application to the Exploration of Sedimentary Basins

Richard J. Davies
Richard J. Davies
Cardiff University, UK
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Joseph A. Cartwright
Joseph A. Cartwright
Cardiff University, UK
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Simon A. Stewart
Simon A. Stewart
BP, Azerbaijan
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Mark Lappin
Mark Lappin
ExxonMobil Exploration Company, USA
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John R. Underhill
John R. Underhill
The University of Edinburgh, UK
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Geological Society of London
Volume
29
ISBN electronic:
9781862394049
Publication date:
January 01, 2004

GeoRef

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