Register      Login
Exploration Geophysics Exploration Geophysics Society
Journal of the Australian Society of Exploration Geophysicists
RESEARCH ARTICLE

Interactive geophysical inversion using qualitative geological constraints

Chris Wijns 1 2 5 Peter Kowalczyk 3 4
+ Author Affiliations
- Author Affiliations

1 CSIRO Exploration and Mining, PO Box 1130, Bentley, WA 6102, Australia.

2 Present address: Resolute Mining Ltd, 4th Floor, 28 The Esplanade, Perth, WA 6000, Australia.

3 Placer Dome Inc., 1500–1055 Dunsmuir Street, Vancouver, BC, V7X 1P1, Canada.

4 Present address: Mira Geoscience Advanced Geophysical Interpretation Centre, Suite 512B, 409 Granville Street, Vancouver, BC, V4A 5M6, Canada.

5 Corresponding author. Email: chrisw@resolute-ltd.com.au

Exploration Geophysics 38(3) 208-212 https://doi.org/10.1071/EG07021
Submitted: 6 February 2007  Accepted: 8 August 2007   Published: 19 September 2007

Abstract

Numerical inversion of geophysical data does not normally require user interaction apart from the selection of initial inversion parameters. However, such an inversion often returns a single solution based upon default parameters. While this solution will be geophysically correct, assuming convergence of the algorithm, it may not be the most geologically reasonable answer. It is necessary to incorporate human interaction in selecting inversion solutions, this being the most efficient method for adding qualitative geological constraints. An automatic system provides a user-directed search of the space of geophysical solutions. Rankings assigned to numerical inversion results guide a genetic algorithm in advancing towards a conceptual target. Our example uses resistivity and chargeability data from a pole-dipole induced polarisation survey collected during a mineral exploration program. We invert for specific geological features: a defined, conductive top layer, sharp geological boundaries in the resistivity, and greatest depth of resolution of the inversion algorithm. The interactive system is an organised way to investigate the solution space for valid inversion results that emphasise these geological possibilities.

Key words: inversion, interactive, geologically constrained.


Acknowledgments

Fabio Boschetti and Thomas Poulet, both at CSIRO, engaged the authors in many valuable discussions of interactive evolutionary computation.


References

Boschetti, F., Dentith, M., and List, R., 1996, Inversion of seismic refraction data using genetic algorithms: Geophysics 61, 1715–1727.
Crossref | GoogleScholarGoogle Scholar | Goldberg D. , 1989, Genetic Algorithms in Search, Optimization, and Machine Learning: Addison-Wesley, 412 pp.

Oldenburg, D. W., and Li, Y., 1999, Estimating depth of investigation in DC resistivity and IP surveys: Geophysics 64, 403–416.
Crossref | GoogleScholarGoogle Scholar |

Takagi, H., 2001, Interactive evolutionary computation: fusion of the capacities of EC optimization and human evaluation: Proceedings of the IEEE 89, 1275–1296.
Crossref | GoogleScholarGoogle Scholar |

Wijns, C., Boschetti, F., and Moresi, L., 2003, Inverse modelling in geology by interactive evolutionary computation: Journal of Structural Geology 25, 1615–1621.
Crossref | GoogleScholarGoogle Scholar |