Geophysics is an essential tool for imaging the subsurface. It relies on physical properties contrasts between subsurface bodies, such as density, magnetic susceptibility or electrical conductivity. These contrasts produce a physical response that can be measured remotely by geophysical instruments. Data from geophysical surveys are processed to provide information about those physical properties. These can be used in helping solve a variety of problems in resource exploration, environmental or geotechnical areas.
However attempting to extract complex geological information from geophysical inversion can prove to be challenging, even with the required expert knowledge. Moreover, while there is room for practitioners in the choice of the prior parameters (reference model or the importance of smoothness), it can be difficult to relate the resulting models to some of the complex geological questions that are being asked (how many different units is there? Can we still explain the data by assuming this structural configuration?).
A large improvement in the recovery of the subsurface structures, and significant reduction in the uncertainty of the recovered features, can be achieved by incorporating petrophysical and geological information into the inversion of geophysical data. Those prior information allow to restrain the space of acceptable solutions to the inverse problem.
My research focuses on developing a new framework for incorporating petrophysical and geological information into voxel-based geophysical inversion. By developing the geophysical inverse problem from a probabilistic perspective, I redesign the objective function and the iteration steps as a suite of cyclic optimization problems in which three separate MAP optimization problems are solved using geophysical, petrophysical and geological data respectively. By quantitatively linking these data into a single framework, I recover a final inverted model that reproduces the observed, or desired, petrophysical and geological features while fitting the geophysical data.
Astic T., Oldenburg D.W., 2019. A framework for petrophysically and geologically guided geophysical inversion using a dynamic Gaussian mixture model prior, Geophysical Journal International, ggz389, https://doi.org/10.1093/gji/ggz389
Astic, T. & Chouteau, M., 2019. Geological interpretation of the northern flank of the Matagami camp, Québec, using gravity and magnetic inversions, Canadian Journal of Earth Sciences, 56(5), 471–482. https://doi.org/10.1139/cjes-2018-0049
Mir, R., Perrouty S., Astic T., Bérubé C.L., Smith R.S., 2019. Structural complexity inferred from anisotropic resistivity: Example from airborne EM and compilation of historical resistivity/induced polarization data from the gold-rich Canadian Malartic district, Québec, Canada, Geophysics 2019 84:2, B153-B167. https://doi.org/10.1190/geo2018-0444.1
Astic, T. & Oldenburg, D. W., 2018. Petrophysically guided geophysical inversion using a dynamic gaussian mixture model prior, in SEG Technical Program Expanded Abstracts 2018, pp. 2312–2316. https://doi.org/10.1190/segam2018-2995155.1