Open AccessThree‐dimensional inversion of ZTEM data
Author(s) -
Holtham Elliot,
Oldenburg Douglas W.
Publication year - 2010
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1111/j.1365-246x.2010.04634.x
Subject(s) - inversion (geology) , magnetotellurics , geology , synthetic data , data set , remote sensing , transfer function , algorithm , ground truth , geodesy , geophysics , computer science , mathematics , seismology , physics , electrical resistivity and conductivity , statistics , engineering , quantum mechanics , machine learning , electrical engineering , tectonics
SUMMARY Z ‐Axis Tipper Electromagnetic Technique (ZTEM) data are airborne electromagnetic data which record the vertical magnetic field that results from natural sources. The data are transfer functions that relate the local vertical field to orthogonal horizontal fields measured at a reference station on the ground. The transfer functions depend on frequency and provide information about the 3‐D conductivity structure of the Earth. The practical frequency range is 30–720 Hz and hence it is possible to see structures at depths of a kilometre or more if the earth is of moderate conductivity. This depth of penetration is significantly greater than that obtained with controlled source EM techniques and, when coupled with rapid spatial acquisition with an airborne system, means that ZTEM data can be used to map large‐scale structures that are difficult to survey with ground based surveys. We present some fundamentals about understanding the signatures obtained with ZTEM transfer functions and then develop a Gauss–Newton algorithm to invert ZTEM data. The algorithm is applied to synthetic examples and to a field data set from the Bingham Canyon region in Utah. The field data set requires a workflow procedure to estimate appropriate noise levels in individual frequency components. These noise levels can then be used to invert multiple frequencies simultaneously. ZTEM data are insensitive to a 1‐D conductivity structures and hence the background can be difficult to estimate. We provide two methods to determine appropriate background models. Interestingly, topography, which is usually a hinderance in field data interpretation, provides a first‐order signal in the ZTEM data and helps with this calibration.