HR: 10:30h
AN: NS12A-01 INVITED     [Abstracts]
TI: Challenges to Near-Real-Time Classification of Buried Conductive Targets Based on the Spatiotemporal EMI Response
AU: * Everett, M E
EM: everett@geo.tamu.edu
AF: Dept of Geology and Geophysics, Texas A&M University, College Station, TX 77845 United States
AU: Benavides, A
EM: abenavides@geo.tamu.edu
AF: Dept of Geology and Geophysics, Texas A&M University, College Station, TX 77845 United States
AU: Nguyen, C V
EM: cam@ee.tamu.edu
AF: Dept of Electrical Engineering, Texas A&M University, College Station, TX 77845 United States
AB: Classification of conductive targets using controlled-source electromagnetic induction (EMI) is an important task in near-surface applied geophysics. Target classification is sometimes required in near real-time, requiring efficient and fast data processing algorithms. We select empirical physics-based target parameters that are invariant under transmitter-receiver geometry. We used a modifed EM63 metal detector to construct a response database. The target collection consists of hollow and solid spheres, cylinders, plates and inert ordnance. The inversion method is based on non-linear least-squares combined with numerical continuation. The spatiotemporal forward response is that of a body whose induced magnetization tensor undergoes a stretched-exponential transient decay. The early-time background response and late-time system noise produce a rough objective function. In such cases, gradient descent methods can stop at sub-optimal solutions. Nevertheless, we observe model-space clustering of recovered target parameters that can be used a basis for classification, which is achieved here simply by association of an unknown target response with the category assigned to the nearest cluster. We discuss how noise and non-uniqueness of the inverse problem sometimes result in misclassification.
DE: 0925 Magnetic and electrical methods
SC: Near-Surface Geophysics [NS]
MN: 2005 Joint Assembly