Articles | Volume 11, issue 2
https://doi.org/10.5194/se-11-419-2020
© Author(s) 2020. 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-11-419-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2020
Research article |
| 31 Mar 2020
Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging
Centre for Exploration Targeting (School of Earth Sciences),
University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia
Mark Lindsay
Centre for Exploration Targeting (School of Earth Sciences),
University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia
Mark Jessell
Centre for Exploration Targeting (School of Earth Sciences),
University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia
Vitaliy Ogarko
The International Centre for Radio Astronomy Research, University
of Western Australia, 7 Fairway, 6009 Crawley, Australia
ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)
Related authors
Jérémie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessell
Solid Earth, 14, 43–68, https://doi.org/10.5194/se-14-43-2023, https://doi.org/10.5194/se-14-43-2023, 2023
Short summary
Short summary
We propose and apply a workflow to combine the modelling and interpretation of magnetic anomalies and resistivity anomalies to better image the basement. We test the method on a synthetic case study and apply it to real world data from the Cloncurry area (Queensland, Australia), which is prospective for economic minerals. Results suggest a new interpretation of the composition and structure towards to east of the profile that we modelled.
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
Short summary
This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
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
Short summary
To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know
the answer. We present a suite of synthetic 3D geological models with their gravity and magnetic responses that allow researchers to test their methods on a whole range of geologically plausible models, thus overcoming one of the fundamental limitations of automation studies.
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
Short summary
We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features and provide theoretical details of the open-source algorithm we use.
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
Short summary
One motivation for this study is to develop a workflow that enables the integration of geophysical datasets with different coverages that are quite common in exploration geophysics. We have utilized a level set approach to achieve this goal. The utilized technique parameterizes the subsurface in the same fashion as geological models. Our results indicate that the approach is capable of integrating information from seismic data in 2D to guide the 3D inversion results of the gravity data.
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
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.
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.
Jérémie Giraud, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, and Paul Cupillard
EGUsphere, https://doi.org/10.5194/egusphere-2023-129, https://doi.org/10.5194/egusphere-2023-129, 2023
Short summary
Short summary
We present and test an algorithm that integrates geological modelling into geophysical modelling. This is motivated by the need to model the earth using all available data and to reconcile the different types of measurements. We introduce the methodology in detail and test our algorithm using two idealised scenarios. Results suggest that the method we propose is effectively capable of improving the models recovered by geophysical inversion and may be applied in real world scenarios.
Jérémie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessell
Solid Earth, 14, 43–68, https://doi.org/10.5194/se-14-43-2023, https://doi.org/10.5194/se-14-43-2023, 2023
Short summary
Short summary
We propose and apply a workflow to combine the modelling and interpretation of magnetic anomalies and resistivity anomalies to better image the basement. We test the method on a synthetic case study and apply it to real world data from the Cloncurry area (Queensland, Australia), which is prospective for economic minerals. Results suggest a new interpretation of the composition and structure towards to east of the profile that we modelled.
Guillaume Pirot, Ranee Joshi, Jérémie Giraud, Mark Douglas Lindsay, and Mark Walter Jessell
Geosci. Model Dev., 15, 4689–4708, https://doi.org/10.5194/gmd-15-4689-2022, https://doi.org/10.5194/gmd-15-4689-2022, 2022
Short summary
Short summary
Results of a survey launched among practitioners in the mineral industry show that despite recognising the importance of uncertainty quantification it is not very well performed due to lack of data, time requirements, poor tracking of interpretations and relative complexity of uncertainty quantification. To alleviate the latter, we provide an open-source set of local and global indicators to measure geological uncertainty among an ensemble of 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
Short summary
This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
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
Short summary
To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know
the answer. We present a suite of synthetic 3D geological models with their gravity and magnetic responses that allow researchers to test their methods on a whole range of geologically plausible models, thus overcoming one of the fundamental limitations of automation studies.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
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
Short summary
We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features and provide theoretical details of the open-source algorithm we use.
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
Short summary
One motivation for this study is to develop a workflow that enables the integration of geophysical datasets with different coverages that are quite common in exploration geophysics. We have utilized a level set approach to achieve this goal. The utilized technique parameterizes the subsurface in the same fashion as geological models. Our results indicate that the approach is capable of integrating information from seismic data in 2D to guide the 3D inversion results of the gravity data.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, Guillaume Caumon, Mark Jessell, and Robin Armit
Geosci. Model Dev., 14, 6197–6213, https://doi.org/10.5194/gmd-14-6197-2021, https://doi.org/10.5194/gmd-14-6197-2021, 2021
Short summary
Short summary
Fault discontinuities in rock packages represent the plane where two blocks of rock have moved. They are challenging to incorporate into geological models because the geometry of the faulted rock units are defined by not only the location of the discontinuity but also the kinematics of the fault. In this paper, we outline a structural geology framework for incorporating faults into geological models by directly incorporating kinematics into the mathematical framework of the model.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
Short summary
Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, and Mark Jessell
Geosci. Model Dev., 14, 3915–3937, https://doi.org/10.5194/gmd-14-3915-2021, https://doi.org/10.5194/gmd-14-3915-2021, 2021
Short summary
Short summary
LoopStructural is an open-source 3D geological modelling library with a model design allowing for multiple different algorithms to be used for comparison for the same geology. Geological structures are modelled using structural geology concepts and techniques, allowing for complex structures such as overprinted folds and faults to be modelled. In the paper, we demonstrate automatically generating a 3-D model from map2loop-processed geological survey data of the Flinders Ranges, South Australia.
Mark D. Lindsay, Sandra Occhipinti, Crystal Laflamme, Alan Aitken, and Lara Ramos
Solid Earth, 11, 1053–1077, https://doi.org/10.5194/se-11-1053-2020, https://doi.org/10.5194/se-11-1053-2020, 2020
Short summary
Short summary
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.
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
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.
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
Topological analysis in Monte Carlo simulation for uncertainty propagation
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.
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
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.
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.
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.
Burns, K.: Lithologic topology and structural vector fields applied to
subsurface prediction in geology, in: Proceedings of GIS/LIS 88,
ACSM-ASPRS, San Antonio, 25–34, 1988.
Carneiro, C. D. C., Fraser, S. J., Crósta, A. P., Silva, A. M., and
de M. Barros, C. E.: Semiautomated geologic mapping using self-organizing
maps and airborne geophysics in the Brazilian Amazon, Geophysics, 77,
K17–K24, https://doi.org/10.1190/geo2011-0302.1, 2012.
Carter-McAuslan, A., Lelièvre, P. G., and Farquharson, C. G.: A study of
fuzzy c -means coupling for joint inversion, using seismic tomography and
gravity data test scenarios, Geophysics, 80, W1–W15,
https://doi.org/10.1190/geo2014-0056.1, 2015.
Chang, H.-C., Kopaska-Merkel, D. C., and Chen, H.-C.: Identification of
lithofacies using Kohonen self-organizing maps, Comput. Geosci., 28,
223–229, https://doi.org/10.1016/S0098-3004(01)00067-X, 2002.
Chen, J., Hoversten, G. M., Vasco, D., Rubin, Y., and Hou, Z.: A Bayesian
model for gas saturation estimation using marine seismic AVA and CSEM data,
Geophysics, 72, WA85–WA95, https://doi.org/10.1190/1.2435082, 2007.
Chopra, S. and Marfurt, K. J.: Seismic facies classification using some
unsupervised machine-learning methods, in SEG Technical Program Expanded
Abstracts 2018, Society of Exploration Geophysicists, 2056–2060, 2018.
Cracknell, M. J. and Reading, A. M.: Spatial-Contextual Supervised
Classifiers Explored: A Challenging Example of Lithostratigraphy
Classification, IEEE J. Sel. Top. Appl. Earth Obs. Remote, 8, 1–14,
https://doi.org/10.1109/JSTARS.2014.2382760, 2015.
Deal, M. M. and Nolet, G.: Nullspace shuttles, Geophys. J. Int., 124,
372–380, https://doi.org/10.1111/j.1365-246X.1996.tb07027.x, 1996.
de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019.
Du, H., Cao, J., Xue, Y., and Wang, X.: Seismic facies analysis based on
self-organizing map and empirical mode decomposition, J. Appl. Geophys.,
112, 52–61, https://doi.org/10.1016/j.jappgeo.2014.11.007, 2015.
Eckhardt, E. A.: Partnership between geology and geophysics in prospecting
for oil, Geophysics, 5, 209–214, https://doi.org/10.1190/1.1441804, 1940.
Egenhofer, M. and Herring, J.: Categorizing binary topological relations
between regions, lines, and points in geographic databases, The, 1–28,
available at:
https://pdfs.semanticscholar.org/b303/39af3f0be6074f7e6ac0263e9ab34eb84271.pdf (last access: 18 March 2020), 1990.
Freeman, B., Boult, P. J., Yielding, G., and Menpes, S.: Using empirical
geological rules to reduce structural uncertainty in seismic interpretation
of faults, J. Struct. Geol., 32, 1668–1676, https://doi.org/10.1016/j.jsg.2009.11.001,
2010.
Giraud, J.: Synthetic geophysical survey using geological modelling from the
Yerrida Basin (Western Australia), https://doi.org/10.5281/zenodo.3522841, 2019.
Giraud, J., Pakyuz-Charrier, E., Jessell, M., Lindsay, M., Martin, R., and
Ogarko, V.: Uncertainty reduction through geologically conditioned
petrophysical constraints in joint inversion, Geophysics, 82, ID19–ID34,
https://doi.org/10.1190/geo2016-0615.1, 2017.
Giraud, J., Pakyuz-Charrier, E., Ogarko, V., Jessell, M., Lindsay, M., and
Martin, R.: Impact of uncertain geology in constrained geophysical
inversion, ASEG Ext. Abstr., 2018, 1–6,
https://doi.org/10.1071/ASEG2018abM1_2F, 2018a.
Giraud, J., Lindsay, M., and Ogarko, V.: Yerrida Basin Geophysical Modeling –
Input data and inverted models, https://doi.org/10.5281/zenodo.1238216, 2018b.
Giraud, J., Lindsay, M., Ogarko, V., Jessell, M., Martin, R., and
Pakyuz-Charrier, E.: Integration of geoscientific uncertainty into
geophysical inversion by means of local gradient regularization, Solid
Earth, 10, 193–210, https://doi.org/10.5194/se-10-193-2019, 2019a.
Giraud, J. J., Ogarko, V., Lindsay, M., Pakyuz-Charrier, E., Jessell, M., and
Martin, R.: Sensitivity of constrained joint inversions to geological and
petrophysical input data uncertainties with posterior geological analysis,
Geophys. J. Int., 218, 666–688, https://doi.org/10.1093/gji/ggz152, 2019b.
Godefroy, G., Caumon, G., Laurent, G., and Bonneau, F.: Structural
Interpretation of Sparse Fault Data Using Graph Theory and Geological Rules,
Math. Geosci., 51, 1091–1107, https://doi.org/10.1007/s11004-019-09800-0, 2019.
Godsil, C. and Royle, G.: Algebraic Graph Theory, 1–18, available at:
http://link.springer.com/10.1007/978-1-4613-0163-9_1 (last access: 18 March 2020), 2001.
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press,
Cambridge, MA, UWA, available at:
http://www.deeplearningbook.org (last access: 18 March 2020), 2016.
Green, C. H.: Integration in exploration, Geophysics, 13, 365–370,
https://doi.org/10.1190/1.1437404, 1948.
Hansen, P. C. and Johnston, P. R.: The L-Curve and its Use in the Numerical
Treatment of Inverse Problems, in Computational Inverse Problems in
Electrocardiography, 119–142, available at:
https://www.sintef.no/globalassets/project/evitameeting/2005/lcurve.pdf (last access: 18 March 2020), 2001.
Hansen, P. C. and O'Leary, D. P.: The Use of the L-Curve in the
Regularization of Discrete Ill-Posed Problems, SIAM J. Sci. Comput., 14,
1487–1503, https://doi.org/10.1137/0914086, 1993.
Jessell, M., Pakyuz-charrier, E., Lindsay, M., Giraud, J., and de Kemp, E.:
Assessing and Mitigating Uncertainty in Three-Dimensional Geologic Models in
Contrasting Geologic Scenarios, chap. 4, in: Metals, Minerals, and Society, 63–74, https://doi.org/10.5382/SP.21.04, 2018.
Jupp, D. L. B. and Vozoff, K.: Joint inversion of geophysical data, Geophys.
J. R. Astron. Soc., 42, 977–991, 1975.
Kalteh, A. M., Hjorth, P., and Berndtsson, R.: Review of the self-organizing
map (SOM) approach in water resources: Analysis, modelling and application,
Environ. Model. Softw., 23, 835–845, https://doi.org/10.1016/j.envsoft.2007.10.001,
2008.
Klose, C. D.: Self-organizing maps for geoscientific data analysis:
geological interpretation of multidimensional geophysical data, Comput.
Geosci., 10, 265–277, https://doi.org/10.1007/s10596-006-9022-x, 2006.
Köhler, A., Ohrnberger, M., and Scherbaum, F.: Unsupervised pattern
recognition in continuous seismic wavefield records using Self-Organizing
Maps, Geophys. J. Int., 182, 1619–1630,
https://doi.org/10.1111/j.1365-246X.2010.04709.x, 2010.
Kohonen, T.: Analysis of a simple self-organizing process, Biol. Cybern.,
44, 135–140, https://doi.org/10.1007/BF00317973, 1982a.
Kohonen, T.: Self-organized formation of topologically correct feature maps,
Biol. Cybern., 43, 59–69, https://doi.org/10.1007/BF00337288, 1982b.
Kohonen, T.: The self-organizing map, Neurocomputing, 21, 1–6,
https://doi.org/10.1016/S0925-2312(98)00030-7, 1998.
Kohonen, T.: Essentials of the self-organizing map, Neural Networks, 37,
52–65, https://doi.org/10.1016/j.neunet.2012.09.018, 2013.
Lelièvre, P. G. and Farquharson, C. G.: Integrated Imaging for Mineral
Exploration, in: Integrated Imaging of the Earth: Theory and Applications, American Geophysical union,
137–166, 2016.
Li, Y. and Oldenburg, D. W.: Incorporating geological dip information into
geophysical inversions, Geophysics, 65, 148–157, https://doi.org/10.1190/1.1444705,
2000.
Li, Y., Melo, A., Martinez, C., and Sun, J.: Geology differentiation: A new
frontier in quantitative geophysical interpretation in mineral exploration,
Lead. Edge, 38, 60–66, https://doi.org/10.1190/tle38010060.1, 2019.
Lindsay, M., Occhipinti, S., Ramos, L., Aitken, A., and Hilliard, P.: An
integrated view of the Yerrida Basin with implications for its architecture
and mineral prospectivity, Crawley, 2018.
Lindsay, M., Occhipinti, S., Laflamme, C., Aitken, A., and Ramos, L.: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin, Solid Earth Discuss., https://doi.org/10.5194/se-2019-192, in review, 2020.
Lines, L. R., Schultz, A. K., and Treitel, S.: Cooperative inversion of
geophysical data, Geophysics, 53, 8–20, https://doi.org/10.1190/1.1442403, 1988.
Maag, E. and Li, Y.: Discrete-valued gravity inversion using the guided
fuzzy c – means clustering technique, Geophysics, 83, G59–G77,
https://doi.org/10.1190/geo2017-0594.1, 2018.
Martin, R. and Obermayer, K.: Self-Organizing Maps, in Encyclopedia of
Neuroscience, Elsevier, 551–560, 2009.
Martin, R., Ogarko, V., Komatitsch, D., and Jessell, M.: Parallel
three-dimensional electrical capacitance data imaging using a nonlinear
inversion algorithm and Lp norm-based model, Measurement, 128, 428–445,
https://doi.org/10.1016/j.measurement.2018.05.099, 2018.
Meju, M. A. and Gallardo, L. A.: Structural Coupling Approaches in
Integrated Geophysical Imaging, American Geophysical union, 49–67, 2016.
Melo, A. T., Sun, J., and Li, Y.: Geophysical inversions applied to 3D
geology characterization of an iron oxide copper-gold deposit in Brazil,
Geophysics, 82, K1–K13, https://doi.org/10.1190/geo2016-0490.1, 2017.
Miljkovic, D.: Brief review of self-organizing maps, in 2017 40th
International Convention on Information and Communication Technology,
Electronics and Microelectronics (MIPRO), IEEE, Opatija, Croatia, 1061–1066, 2017.
Moorkamp, M., Heincke, B., Jegen, M., Hobbs, R. W., and Roberts, A. W.: Joint
Inversion in Hydrocarbon Exploration, in Integrated Imaging of the Earth:
Theory and Applications, American Geophysical union, 167–189, 2016.
Muñoz, G. and Rath, V.: Beyond smooth inversion: the use of nullspace
projection for the exploration of non-uniqueness in MT, Geophys. J. Int.,
164, 301–311, https://doi.org/10.1111/j.1365-246X.2005.02825.x, 2006.
Nettleton, L. L.: Geophysics, geology and oil finding, Geophysics, 14,
273–289, https://doi.org/10.1190/1.1437535, 1949.
Occhipinti, S., Hocking, R., Lindsay, M., Aitken, A., Copp, I., Jones, J.,
Sheppard, S., Pirajno, F., and Metelka, V.: Paleoproterozoic basin
development on the northern Yilgarn Craton, Western Australia, Precambrian
Res., 300, 121–140, https://doi.org/10.1016/j.precamres.2017.08.003, 2017.
Paasche, H. and Tronicke, J.: Cooperative inversion of 2D geophysical data
sets: A zonal approach based on fuzzy c – means cluster analysis, Geophysics,
72, A35–A39, https://doi.org/10.1190/1.2670341, 2007.
Paasche, H., Tronicke, J., and Dietrich, P.: Automated integration of
partially colocated models: Subsurface zonation using a modified fuzzy c
-means cluster analysis algorithm, Geophysics, 75, P11–P22,
https://doi.org/10.1190/1.3374411, 2010.
Pakyuz-Charrier, E., Giraud, J., Lindsay, M., and Jessell, M.: Common
Uncertainty Research Explorer Uncertainty Estimation in Geological 3D
Modelling, ASEG Ext. Abstr., 2018, 1,
https://doi.org/10.1071/ASEG2018abW10_2D, 2018a.
Pakyuz-Charrier, E., Giraud, J., Ogarko, V., Lindsay, M., and Jessell, M.:
Drillhole uncertainty propagation for three-dimensional geological modeling
using Monte Carlo, Tectonophysics, 16–39, https://doi.org/10.1016/j.tecto.2018.09.005, 2018b.
Pakyuz-Charrier, E., Lindsay, M., Ogarko, V., Giraud, J., and Jessell, M.:
Monte Carlo simulation for uncertainty estimation on structural data in
implicit 3-D geological modeling, a guide for disturbance distribution
selection and parameterization, Solid Earth, 9, 385–402,
https://doi.org/10.5194/se-9-385-2018, 2018c.
Pakyuz-Charrier, E., Jessell, M., Giraud, J., Lindsay, M., and Ogarko, V.:
Topological analysis in Monte Carlo simulation for uncertainty propagation,
Solid Earth, 10, 1663–1684, https://doi.org/10.5194/se-10-1663-2019, 2019.
Pellerin, J., Botella, A., Bonneau, F., Mazuyer, A., Chauvin, B., Lévy,
B., and Caumon, G.: RINGMesh: A programming library for developing mesh-based
geomodeling applications, Comput. Geosci., 104, 93–100,
https://doi.org/10.1016/j.cageo.2017.03.005, 2017.
Pirajno, F. and Adamides, N. G.: Geology and Mineralization of the
Palaeoproterozoic Yerrida Basin, Western Australia, Perth,
available at: https://catalogue.nla.gov.au/Record/524116 (last access: 18 March 2020), 2000.
Roden, R., Smith, T., and Sacrey, D.: Geologic pattern recognition from
seismic attributes: Principal component analysis and self-organizing maps,
Interpretation, 3, SAE59–SAE83, https://doi.org/10.1190/INT-2015-0037.1, 2015.
Santos, E. T. F. and Bassrei, A.: L- and Θ-curve approaches for the
selection of regularization parameter in geophysical diffraction tomography,
Comput. Geosci., 33, 618–629, https://doi.org/10.1016/j.cageo.2006.08.013, 2007.
Shalaginov, A. and Franke, K.: A new method for an optimal SOM size
determination in neuro-fuzzy for the digital forensics applications, in:
Lecture Notes in Computer Science (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, Palma de Mallorca, 549–563, https://doi.org/10.1007/978-3-319-19222-2_46, 2015.
Shannon, C. E. E.: A Mathematical Theory of Communication, Bell Syst. Tech.
J., 27, 379–423, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x, 1948.
Singh, A. and Sharma, S. P.: Identification of different geologic units
using fuzzy constrained resistivity tomography, J. Appl. Geophys., 148,
127–138, https://doi.org/10.1016/j.jappgeo.2017.11.014, 2018.
Stavrakoudis, D., Dragozi, E., Gitas, I., and Karydas, C.: Decision Fusion
Based on Hyperspectral and Multispectral Satellite Imagery for Accurate
Forest Species Mapping, Remote Sens., 6, 6897–6928, https://doi.org/10.3390/rs6086897,
2014.
Sun, J. and Li, Y.: Multidomain petrophysically constrained inversion and
geology differentiation using guided fuzzy c-means clustering, Geophysics,
80, ID1–ID18, https://doi.org/10.1190/geo2014-0049.1, 2015.
Sun, J. and Li, Y.: Joint-clustering inversion of gravity and magnetic data
applied to the imaging of a gabbro intrusion, in SEG Technical Program
Expanded Abstracts 2016, Society of Exploration
Geophysicists, 2175–2179, 2016.
Sun, J. and Li, Y.: Magnetization clustering inversion Part II: Assessing
the uncertainty of recovered magnetization directions, Geophysics, 8, 1–86,
https://doi.org/10.1190/geo2018-0480.1, 2019.
Tarabalka, Y., Benediktsson, J. A., and Chanussot, J.: Spectral–Spatial
Classification of Hyperspectral Imagery Based on Partitional Clustering
Techniques, IEEE Trans. Geosci. Remote, 47, 2973–2987,
https://doi.org/10.1109/TGRS.2009.2016214, 2009.
Thiele, S. T., Jessell, M. W., Lindsay, M., Ogarko, V., Wellmann, J. F., and
Pakyuz-Charrier, E.: The topology of geology 1: Topological analysis, J.
Struct. Geol., 91, 27–38, https://doi.org/10.1016/j.jsg.2016.08.009, 2016.
Towles, H. C.: A study in integration of geology and geophysics, Geophysics,
17, 876–899, https://doi.org/10.1190/1.1437821, 1952.
Uriarte, E. A. and Martín, F. D.: Topology Preservation in SOM, Int. J.
Appl. Math. Comput. Sci., 1, 19–22, 2005.
van der Baan, M. and Jutten, C.: Neural networks in geophysical
applications, Geophysics, 65, 1032–1047, https://doi.org/10.1190/1.1444797, 2000.
Vatanen, T., Osmala, M., Raiko, T., Lagus, K., Sysi-Aho, M., Orešič,
M., Honkela, T., and Lähdesmäki, H.: Self-organization and missing
values in SOM and GTM, Neurocomputing, 147, 60–70,
https://doi.org/10.1016/j.neucom.2014.02.061, 2015.
Vesanto, J. and Alhoniemi, E.: Clustering of the self-organizing map, IEEE
Trans. Neural Networks, 11, 586–600, https://doi.org/10.1109/72.846731, 2000.
Ward, W. O. C., Wilkinson, P. B., Chambers, J. E., Oxby, L. S., and Bai, L.:
Distribution-based fuzzy clustering of electrical resistivity tomography
images for interface detection, Geophys. J. Int., 197, 310–321,
https://doi.org/10.1093/gji/ggu006, 2014.
Wegener, A.: Die Entstehung der Kontinente und Ozeane, Braunschweig, Braunschweig, 1920.
Wellmann, J. F. and Regenauer-Lieb, K.: Uncertainties have a meaning:
Information entropy as a quality measure for 3-D geological models,
Tectonophysics, 526–529, 207–216, https://doi.org/10.1016/j.tecto.2011.05.001, 2012.
Wellmann, J. F., Horowitz, F. G., Schill, E., and Regenauer-Lieb, K.: Towards
incorporating uncertainty of structural data in 3D geological inversion,
Tectonophysics, 490, 141–151, https://doi.org/10.1016/j.tecto.2010.04.022, 2010.
Wrona, T., Pan, I., Gawthorpe, R. L., and Fossen, H.: Seismic facies analysis
using machine learning, Geophysics, 83, O83–O95,
https://doi.org/10.1190/geo2017-0595.1, 2018.
Zhang, G., Wang, Z., and Chen, Y.: Deep learning for Seismic Lithology
Prediction, Geophys. J. Int., 215, 1368–1387, https://doi.org/10.1093/gji/ggy344, 2018.
Zhao, T., Jayaram, V., Roy, A., and Marfurt, K. J.: A comparison of
classification techniques for seismic facies recognition, Interpretation, 3,
SAE29–SAE58, https://doi.org/10.1190/INT-2015-0044.1, 2015.
Zhao, T., Li, F., and Marfurt, K. J.: Constraining self-organizing map facies
analysis with stratigraphy: An approach to increase the credibility in
automatic seismic facies classification, Interpretation, 5, T163–T171,
https://doi.org/10.1190/int-2016-0132.1, 2017.
Zlatanova, S.: On 3D topological relationships, in Proceedings 11th
International Workshop on Database and Expert Systems Applications, IEEE Comput. Soc., 2000, 913–919, 2000.
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.
We propose a methodology for the identification of rock types using geophysical and geological...
