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Chapter 16: Detection of Buried Steel Drums from Magnetic Anomaly Data Using an Artificial Intelligence Technique

By
Ahmed Salem
Ahmed Salem
Airborne Geophysics Department, Nuclear Materials Authority of Egypt, Kattamiya Road, Maadi, P.O. Box 530 Maadi, Cairo, Egypt
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Dhananjay Ravat
Dhananjay Ravat
Department of Geology MS 4234, Southern Illinois University at Carbondale, Carbondale, Il 62901, USA
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T. Jeffrey Gamey
T. Jeffrey Gamey
Oak Ridge National Laboratory, Oak Ridge, TN 37831-6038, USA
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Keisuke Ushijima
Keisuke Ushijima
Earth Resources Department, Faculty of Engineering, Kyushu University 6-10-1, Higashi-ku, Fukuoka 812-8581, Japan
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Published:
January 01, 2005

Introduction

Hazardous materials are often buried in ferrometallic containers. Detection and precise location of these objects and estimation of the type and quantity of the objects are becoming increasingly important in environmental investigations worldwide. The best geophysical technique for locating and mapping the distribution of ferrometallic materials is a magnetic survey, especially when the signal-to-noise ratio of the magnetic anomalies is high. However, geophysical measurements need to be interpreted and this can be time consuming. Therefore, there is a need for a real time technique that combines the efficiency of a human interpreter with the precision and speed of a computer. One such technique may be found in the emerging field of artificial neural networks.

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Contents

Investigations in Geophysics

Near-Surface Geophysics

Dwain K. Butler
Dwain K. Butler
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Society of Exploration Geophysicists
Volume
13
ISBN electronic:
9781560801719
Publication date:
January 01, 2005

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