Project: Inversion of Time Domain Electromagnetic Datasets to Produce Realistic 3D Conductivity Models
Time domain electromagnetic (TEM) data provides unique information about the ground conductivity which is directly associated with minerals and their geologic signatures. However,because of the complexity of numerical modelling and the cost of computation, three-dimensional TEM data inversion that reveals realistic distribution of subsurface conductivity has not been fully
developed. My PhD thesis focuses on the practical inversions of two types of TEM data, viz. airborne TEM and inductive source resistivity (ISR).
Airborne TEM is a very important type of data that gives unique insight of ground conductivity at the depth of most exploitable mineral deposit. The main difficulty of 3D airborne TEM inversion is that too many transmitters are involved, so the computation of multi-source problem may be extremely expensive. This results that mining exploration heavily depends on the data-based method and 1D inversion. However, my study has shown that 1D inversion may produce completely incorrect conductivity model due to the considerably large footprint of airborne TEM system and this problem can only be tackled by 3D inversion. As the first pass I have inverted a VTEM dataset from Mt. Milligan deposit in BC and produced a 3D conductivity model in
accordance with known geology. My next step will be presenting a practical workflow for the industy, by which geophysicists can invert large-scale airborne TEM data with smart selection of soundings for inversion to achieve desired resolution with reasonable computational cost.
The other type of dataset is inductive source resistivity (ISR), which employs large transmitter loop but measures transient electrical field (E-field). ISR has been proven to be sensitive to deep and poorly conductive structure and thus very promising to be an effective alternative to the conventional magnetic field TEM in resistive geology. However ISR data have not been 3D inverted because of the complexity of the survey. I am now working with synthetic model
inversions and a field dataset from Shea Creek uranium deposit. The next step is to do some researches in the aspects of survey design and a detailed workflow of ISR inversion. With the knowledge learned in this study, the prospectors can have more insight about the E-field TEM and are able to better use this technique in deep exploration.