Articles | Volume 10, issue 5
https://doi.org/10.5194/se-10-1663-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-10-1663-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Method article
| 
10 Oct 2019
Method article |
| 10 Oct 2019
| 10 Oct 2019
Topological analysis in Monte Carlo simulation for uncertainty propagation
Evren Pakyuz-Charrier
CORRESPONDING AUTHOR
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Intrepid Geophysics, 3 Male Street, Brighton, VIC 3186,
Australia
Mark Jessell
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Jérémie Giraud
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Mark Lindsay
Centre for Exploration Targeting, The University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009,
Australia
Vitaliy Ogarko
International Centre for Radio Astronomy Research, The
University of Western Australia, Ken and Julie Michael
Building, 7 Fairway, Crawley, WA 6009, Australia
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Mark D. Lindsay, Sandra Occhipinti, Crystal Laflamme, Alan Aitken, and Lara Ramos
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Integrated interpretation of multiple datasets is a key skill required for better understanding the composition and configuration of the Earth's crust. Geophysical and 3D geological modelling are used here to aid the interpretation process in investigating anomalous and cryptic geophysical signatures which suggest a more complex structure and history of a Palaeoproterozoic basin in Western Australia.
Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 11, 419–436, https://doi.org/10.5194/se-11-419-2020, https://doi.org/10.5194/se-11-419-2020, 2020
Short summary
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.
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
Short summary
We propose the quantitative integration of geology and geophysics in an algorithm integrating the probability of observation of rocks with gravity data to improve subsurface imaging. This allows geophysical modelling to adjust models preferentially in the least certain areas while honouring geological information and geophysical data. We validate our algorithm using an idealized case and apply it to the Yerrida Basin (Australia), where we can recover the geometry of buried greenstone belts.
Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mark Jessell
Solid Earth, 9, 385–402, https://doi.org/10.5194/se-9-385-2018, https://doi.org/10.5194/se-9-385-2018, 2018
Short summary
Short summary
MCUE is a method that produces probabilistic 3-D geological models by sampling from distributions that represent the uncertainty of the initial input dataset. This process generates numerous plausible datasets used to produce a range of statistically plausible 3-D models which are combined into a single probabilistic model. In this paper, improvements to distribution selection and parameterization for input uncertainty are proposed.
Xiaojun Feng, Enyuan Wang, Jérôme Ganne, Roland Martin, and Mark W. Jessell
Solid Earth Discuss., https://doi.org/10.5194/se-2017-142, https://doi.org/10.5194/se-2017-142, 2018
Preprint withdrawn
J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, and Mark W. Jessell
Geosci. Model Dev., 9, 1019–1035, https://doi.org/10.5194/gmd-9-1019-2016, https://doi.org/10.5194/gmd-9-1019-2016, 2016
Short summary
Short summary
We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
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
Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging
Joint analysis of the magnetic field and total gradient intensity in central Europe
Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization
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
Short summary
Given the inconsistency of the plane height and also the effects of the initial density model on the inversion results, the sequential inversion of on-orbit GOCE satellite gravity gradient and terrestrial gravity are divided into two integrated processes. Some new findings are discovered through the reliable and effective inversion results in the North China Craton.
Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 11, 419–436, https://doi.org/10.5194/se-11-419-2020, https://doi.org/10.5194/se-11-419-2020, 2020
Short summary
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.
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
Short summary
In this work we aim to interpret the extended magnetic low visible at satellite altitudes above central Europe by performing a joint analysis of magnetic field and total gradient intensity maps at low and high altitudes. Here we demonstrate that such a magnetic anomaly is mainly a result of the contrast between two crustal platforms differing strongly in geological and magnetic properties. Synthetic model tests have been created to support our modeling.
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
Short summary
We propose the quantitative integration of geology and geophysics in an algorithm integrating the probability of observation of rocks with gravity data to improve subsurface imaging. This allows geophysical modelling to adjust models preferentially in the least certain areas while honouring geological information and geophysical data. We validate our algorithm using an idealized case and apply it to the Yerrida Basin (Australia), where we can recover the geometry of buried greenstone belts.
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Short summary
This paper improves the Monte Carlo simulation for uncertainty propagation (MCUP) method for 3-D geological modeling. Topological heterogeneity is observed in the model suite. The study demonstrates that such heterogeneity arises from piecewise nonlinearity inherent to 3-D geological models and contraindicates use of global uncertainty estimation methods. Topological-clustering-driven uncertainty estimation is proposed as a demonstrated alternative to address plausible model heterogeneity.
This paper improves the Monte Carlo simulation for uncertainty propagation (MCUP) method for 3-D...
