Articles | Volume 11, issue 2
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, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko

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Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application
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Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models
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Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code
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Constraining 3D geometric gravity inversion with a 2D reflection seismic profile using a generalized level set approach: application to the eastern Yilgarn Craton
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Related subject area

Subject area: Crustal structure and composition | Editorial team: Geodesy, gravity, and geomagnetism | Discipline: Geodesy
Sequential inversion of GOCE satellite gravity gradient data and terrestrial gravity data for the lithospheric density structure in the North China Craton
Yu Tian and Yong Wang
Solid Earth, 11, 1121–1144,,, 2020
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Topological analysis in Monte Carlo simulation for uncertainty propagation
Evren Pakyuz-Charrier, Mark Jessell, Jérémie Giraud, Mark Lindsay, and Vitaliy Ogarko
Solid Earth, 10, 1663–1684,,, 2019
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Joint analysis of the magnetic field and total gradient intensity in central Europe
Maurizio Milano, Maurizio Fedi, and J. Derek Fairhead
Solid Earth, 10, 697–712,,, 2019
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Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization
Jeremie Giraud, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier
Solid Earth, 10, 193–210,,, 2019
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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.