TY - GEN
T1 - Joint inversion of multi-configuration electromagnetic induction data to characterize subsurface electrical conductivity
AU - Jadoon, Khan
AU - Moghadas, Davood
AU - Jadoon, Anwar
AU - Missimer, Thomas M.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2014/10/21
Y1 - 2014/10/21
N2 - Electromagnetic induction (EMI) devices are capable of measuring the cumulative electrical conductivity over a certain depth range. In this study, a numerical experiment has been performed to test a novel join inversion approach for the Geonics EM34 instrument, by considering different coil offsets (10, 20 and 40 m), different coil orientations (vertical and horizontal), and different frequencies (6.4, 1.6 and 0.4 kHz). The subsurface is considered as four-layer model having different conductivities. The global multilevel coordinate search optimization algorithm is sequentially combination with the local optimization algorithm to minimize the misfit between the measured and modeled data. The layer conductivities are well predicted by the join inversion of electromagnetic data. The response surface of the objective function was investigated to assess the sensitivity of the subsurface layer conductivities. The sensitivity of the conductivity for the top two layers is less as compared to the deeper layers. The proposed approach is promising for the fast mapping of true conductivity distributions over large areas.
AB - Electromagnetic induction (EMI) devices are capable of measuring the cumulative electrical conductivity over a certain depth range. In this study, a numerical experiment has been performed to test a novel join inversion approach for the Geonics EM34 instrument, by considering different coil offsets (10, 20 and 40 m), different coil orientations (vertical and horizontal), and different frequencies (6.4, 1.6 and 0.4 kHz). The subsurface is considered as four-layer model having different conductivities. The global multilevel coordinate search optimization algorithm is sequentially combination with the local optimization algorithm to minimize the misfit between the measured and modeled data. The layer conductivities are well predicted by the join inversion of electromagnetic data. The response surface of the objective function was investigated to assess the sensitivity of the subsurface layer conductivities. The sensitivity of the conductivity for the top two layers is less as compared to the deeper layers. The proposed approach is promising for the fast mapping of true conductivity distributions over large areas.
UR - http://hdl.handle.net/10754/564509
UR - http://www.earthdoc.org/publication/publicationdetails/?publication=61828
U2 - 10.3997/2214-4609.20143496
DO - 10.3997/2214-4609.20143496
M3 - Conference contribution
SN - 9789073834347
BT - Near Surface Geoscience 2012 – 18th European Meeting of Environmental and Engineering Geophysics
PB - EAGE Publications
ER -