Articles | Volume 11, issue 2
Solid Earth, 11, 419–436, 2020
https://doi.org/10.5194/se-11-419-2020
Solid Earth, 11, 419–436, 2020
https://doi.org/10.5194/se-11-419-2020
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.

Related authors

Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022,https://doi.org/10.5194/gmd-15-3641-2022, 2022
Short summary
Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications
Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko
Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022,https://doi.org/10.5194/essd-14-381-2022, 2022
Short summary
Utilisation of probabilistic MT inversions to constrain magnetic data inversion: proof-of-concept and field application
Jeremie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessell
Solid Earth Discuss., https://doi.org/10.5194/se-2021-124,https://doi.org/10.5194/se-2021-124, 2021
Preprint under review for SE
Short summary
Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code
Jérémie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021,https://doi.org/10.5194/gmd-14-6681-2021, 2021
Short summary
Constraining 3D geometric gravity inversion with a 2D reflection seismic profile using a generalized level set approach: application to the eastern Yilgarn Craton
Mahtab Rashidifard, Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 12, 2387–2406, https://doi.org/10.5194/se-12-2387-2021,https://doi.org/10.5194/se-12-2387-2021, 2021
Short summary

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, https://doi.org/10.5194/se-11-1121-2020,https://doi.org/10.5194/se-11-1121-2020, 2020
Short summary
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, https://doi.org/10.5194/se-10-1663-2019,https://doi.org/10.5194/se-10-1663-2019, 2019
Short summary
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, https://doi.org/10.5194/se-10-697-2019,https://doi.org/10.5194/se-10-697-2019, 2019
Short summary
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, https://doi.org/10.5194/se-10-193-2019,https://doi.org/10.5194/se-10-193-2019, 2019
Short summary

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, https://doi.org/10.4236/apm.2015.54019, 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, https://doi.org/10.1016/j.crte.2018.12.001, 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, https://doi.org/10.1029/2001JB001073, 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, https://doi.org/10.1111/j.1365-246X.2012.05402.x, 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, https://doi.org/10.1155/2012/678326, 2012. 
Download
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.