HR: 15:10h
AN: NS43A-07    [Abstracts]
TI: Joint and Cooperative Inversion Strategies for Mineral Exploration
AU: * Lelièvre, P G
EM: plelievre@eos.ubc.ca
AF: UBC-Geophysical Inversion Facility (UBC-GIF), University of British Columbia, 6339 Stores Road, Vancouver, BC V6T 1Z4, Canada
AU: Phillips, N D
EM: nigelp@mirageoscience.com
AF: Mira Geoscience, 512B - 409 Granville St, Vancouver, BC V6C 1T2, Canada
AU: Oldenburg, D W
EM: doug@eos.ubc.ca
AF: UBC-Geophysical Inversion Facility (UBC-GIF), University of British Columbia, 6339 Stores Road, Vancouver, BC V6T 1Z4, Canada
AB: Geophysical inversion for mineral exploration typically involves a single type of data sensitive to a single physical property. Combining several complimentary types of geophysical data collected over the same Earth region reduces ambiguity and can enhance inversion results. This is important whenever the Earth can not be adequately resolved by any one type of data. Combining different types of data into an inversion is important when a structural or stochastic relationship is thought to exist between the different physical property distributions. By inverting each data set individually, the recovered physical property models may be inconsistent with the geologic knowledge regarding structure or property relationships. Cooperative and joint inversion strategies can be employed to ensure consistency between the different models. A conventional cooperative inversion strategy is to invert one set of data independently and use that result to constrain a subsequent independent inversion of the second set. However, the models obtained through this simple procedure are commonly biased towards the result of the first inversion and/or towards the survey with greater sensitivity. We have developed more appropriate cooperative inversion strategies that exploit the smoothness weighting functionality of the UBC-GIF inversion codes. Another approach is to fit the data sets simultaneously in what is termed a joint inversion. Many authors have performed simultaneous inversions of data from different surveys sensitive to the same physical property. Others have jointly inverted data sets responsive to different physical properties between which there is an established analytic relationship. However, little work has focused on joint inversion of disparate data sets (from surveys responsive to different physical properties) when there is no analytic relationship available between the properties. To do so, we enforce the structural or compositional similarity between the property models. We use model-gradient dot- and cross-products to measure structural similarity (e.g. coincident boundaries of geologic bodies). We use statistical cross-correlation to measure compositional similarity, which assumes some linear relationship between the physical properties. Our joint and cooperative strategies allow inversion of disparate geophysical data sets. They ensure consistency between the recovered physical property models when stipulated by the available geologic information. These property models can then be better interpreted in concert with the geologic structural and petrophysical information.
DE: 3260 Inverse theory
SC: Near-Surface Geophysics [NS]
MN: 2007 Fall Meeting