Articles | Volume 11, issue 2
Solid Earth, 11, 419–436, 2020
Solid Earth, 11, 419–436, 2020
Research article
31 Mar 2020
Research article | 31 Mar 2020

Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging

Jérémie Giraud et al.

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Cited articles

Ackora-Prah, J., Ayekple, Y. E., Acquah, R. K., Andam, P. S., Sakyi, E. A., and Gyamfi, D.: Revised Mathematical Morphological Concepts, Adv. Pure Math., 5, 155–161,, 2015. 
Anquez, P., Pellerin, J., Irakarama, M., Cupillard, P., Lévy, B., and Caumon, G.: Automatic correction and simplification of geological maps and cross-sections for numerical simulations, C. R. Geosci., 351, 48–58,, 2019. 
Bauer, K., Schulze, A., Ryberg, T., Sobolev, S. V., and Weber, M. H.: Classification of lithology from seismic tomography: A case study from the Messum igneous complex, Namibia, J. Geophys. Res.-Sol. Ea., 108, 1–15,, 2003. 
Bauer, K., Muñoz, G., and Moeck, I.: Pattern recognition and lithological interpretation of collocated seismic and magnetotelluric models using self-organizing maps, Geophys. J. Int., 189, 984–998,, 2012. 
Benavent, X., Dura, E., Vegara, F., and Domingo, J.: Mathematical Morphology for Color Images: An Image-Dependent Approach, Math. Probl. Eng., 2012, 1–18,, 2012. 
Short summary
We propose a methodology for the identification of rock types using geophysical and geological information. It relies on an algorithm used in machine learning called self-organizing maps, to which we add plausibility filters to ensure that the results respect base geological rules and geophysical measurements. Application in the Yerrida Basin (Western Australia) reveals that the thinning of prospective greenstone belts at depth could be due to deep structures not seen from surface.