Chapter 16: Detection of Buried Steel Drums from Magnetic Anomaly Data Using an Artificial Intelligence Technique
Ahmed Salem, Dhananjay Ravat, T. Jeffrey Gamey, Keisuke Ushijima, 2005. "Detection of Buried Steel Drums from Magnetic Anomaly Data Using an Artificial Intelligence Technique", Near-Surface Geophysics, Dwain K. Butler
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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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Near-surface geophysics uses the investigational methods of geophysics to study the nature of the very outermost part of the earth’s crust. Man interacts with this part of the earth’s crust: he walks on it; he drills and excavates into it; he constructs structures on and in it; he utilizes its water and mineral resources; and his wastes are stored on and in it and seep into it. The very outermost part of the Earth’s crust is extremely dynamic-in both technical (physical properties) and nontechnical (political, social, legal) terms-which leads to both technical and nontechnical challenges that are much different than the challenges faced by “traditional” applications of geophysics for regional geologic mapping and for oil and gas exploration (see Chapter 2).