IMAGE Consortium

Inversion and Modelling of Applied Geophysical Electromagnetic data

General description and sponsors

The 3-year IMAGE project begins Sept. 1, 1999. The research is sponsored by NSERC through its Collaborative Research and Development (CRD) program, and the following companies:

Rio Tinto
Placer Dome
Anglo American
Muskox Minerals

This newly founded consortium involves a major new research collaboration between the Geophysical Inversion Facility (UBC-GIF) and the Scientific Computation and Visualization Group (SCV) at the University of British Columbia.

The primary focus is on forward modelling and inversion of 3D (three-dimensional) geophysical electromagnetic data.

There are a host of computational and geophysical challenges associated with solving the resulting mathematical models. These include numerical methods for inverse problems, fast simulation methods for the 3D electromagnetic equations, handling sources and boundary conditions on infinite domains, and parallel computing.



The forward problem

The inverse problem

Financial support

We can provide financial support and are looking for students and postdoctoral fellows
for this project. Contact or if interested.

Goals for a 3-year research program

Research Priorities

Research priorities are determined partly by considering what are important problems, and partly by what data sets are available to the research group. At the consortium meeting on May 9, 1999, we identified the following:

IMAGE Research Proposal Summary

We propose to develop methodologies and practical computer codes to invert geophysical electromagnetic observations to recover a three-dimensional (3D) distribution of electrical conductivity.

A new electromagnetic forward-modelling algorithm will solve for the low-frequency electric and magnetic fields over an arbitrary 3D conductivity structure. Vector and scalar potentials will be used rather than solving for the fields directly. Preliminary work shows this to have many advantages over existing approaches both in computational efficiency and robustness. The inverse problem will be solved by combining traditional non-linear optimization techniques with new techniques based on the differential equations themselves rather than the usual integral equations. By coupling the inverse and forward modelling directly, we plan to develop fast, flexible and robust algorithms.

Although our inverse procedures will be applicable to all low-frequency electromagnetic data, we will be concentrating upon three data types that are both generic and frequently used: frequency-domain magnetotelluric (MT) data, controlled-source audio-frequency magnetotelluric (CSAMT) data, and both surface and drill-hole data from a loop source. We will also develop software for forward modelling time-domain electromagnetic (TEM) data. The sponsors will supply the data on which the inversion codes will be tested, and the codes will be made available to the sponsors at the conclusion of the three-year period.