Modeling and testing the LASI electromagnetic subsurface imaging systems
AuthorThomas, Scott James, 1961-
AdvisorSternberg, Ben K.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThree frequency-domain electromagnetic subsurface profiling systems have been developed which use frequencies from 30Hz to 30kHz, 1kHz to 1MHz, and from 30kHz to 30MHz respectively. The systems operate in the near-field and measure the ellipticity of the magnetic field. A grounded wire or a vertical magnetic dipole is used as the transmitter antenna. The receiving antennas consist of three mutually orthogonal antennas which are placed on the ground in an arbitrary orientation. Instead of performing rotations in three-dimensional complex space, a simple two-dimensional rotation operating in the complex plane is used to find ellipticity and relative tilt angle in three dimensions. Cross-talk between the receiver coils and corrections for coil misalignment are corrected using fixed coefficients. By employing cross-talk and coil misalignment corrections, coil-orientation invariance is achieved. Algorithms using one-dimensional computer modeling are developed to determine the expected minimum and maximum depths of penetration as a function of system noise and anomaly amplitude. Optimum target depth is computed from three-layer one-dimensional computer modeling and compares well with the magneto-telluric depth in the far-field. A large 100,000 gallon concrete-lined basin has been designed and constructed to perform full-scale physical modeling of the system response to various objects. The basin has been filled with water to simulate a conductive medium and a variety of targets have been submerged in the basin to simulate targets. Initial results indicate data can be collected from surveys over the basin to train neural networks. Trained neural networks can then perform real-time modeling during routine surveys.
Degree ProgramGraduate College
Mining and Geological Engineering