Articles | Volume 10, issue 1
https://doi.org/10.5194/se-10-193-2019
© 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-10-193-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2019
Research article |
| 25 Jan 2019
| 25 Jan 2019
Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization
Centre for Exploration Targeting, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia
Mark Lindsay
Centre for Exploration Targeting, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia
Vitaliy Ogarko
The International Centre for Radio Astronomy Research, 7 Fairway, Ken and Julie Michael Building, The University of Western, Australia, WA Crawley 6009, Australia
Mark Jessell
Centre for Exploration Targeting, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia
Roland Martin
Géoscience Environnement Toulouse, UMR CNRS 5563, Université Paul Sabatier, Observatoire Midi-Pyrénées, 14 Avenue Edouard Belin, 31400 Toulouse, France
Evren Pakyuz-Charrier
Centre for Exploration Targeting, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia
Related authors
Lawrence A. Bird, Vitaliy Ogarko, Laurent Ailleres, Lachlan Grose, Jeremie Giraud, Felicity S. McCormack, David E. Gwyther, Jason L. Roberts, Richard S. Jones, and Andrew N. Mackintosh
EGUsphere, https://doi.org/10.5194/egusphere-2025-211, https://doi.org/10.5194/egusphere-2025-211, 2025
Short summary
Short summary
The terrain of the seafloor has important controls on the access of warm water to below floating ice shelves around Antarctica. Here, we present an open-source method to infer what the seafloor looks like around the Antarctic continent, and within these ice shelf cavities, using measurements of the Earth’s gravitational field. We present an improved seafloor map for the Vincennes Bay region in East Antarctica and assess its impact on ice melt rates.
Mark Douglas Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mosayeb Khademi
EGUsphere, https://doi.org/10.5194/egusphere-2024-3754, https://doi.org/10.5194/egusphere-2024-3754, 2025
This preprint is open for discussion and under review for Solid Earth (SE).
Short summary
Short summary
Geophysical data is used to understand the Earth's structure. Geophysical inversion is an optimisation technique that constructs a model of the Earth using geophysical data. Inversion is complex and requires constraints to produce a plausible model. Overfitting is a disadvantageous effect that can affect techniques that rely on optimisation. A new feature in an inversion platform reduces overfitting by informing inversion of spatial uncertainty contained within the geophysical data.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jérémie Giraud, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, and Paul Cupillard
Solid Earth, 15, 63–89, https://doi.org/10.5194/se-15-63-2024, https://doi.org/10.5194/se-15-63-2024, 2024
Short summary
Short summary
We present and test an algorithm that integrates geological modelling into deterministic geophysical inversion. 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 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.
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.
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.
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.
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.
Lawrence A. Bird, Vitaliy Ogarko, Laurent Ailleres, Lachlan Grose, Jeremie Giraud, Felicity S. McCormack, David E. Gwyther, Jason L. Roberts, Richard S. Jones, and Andrew N. Mackintosh
EGUsphere, https://doi.org/10.5194/egusphere-2025-211, https://doi.org/10.5194/egusphere-2025-211, 2025
Short summary
Short summary
The terrain of the seafloor has important controls on the access of warm water to below floating ice shelves around Antarctica. Here, we present an open-source method to infer what the seafloor looks like around the Antarctic continent, and within these ice shelf cavities, using measurements of the Earth’s gravitational field. We present an improved seafloor map for the Vincennes Bay region in East Antarctica and assess its impact on ice melt rates.
Mark Douglas Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mosayeb Khademi
EGUsphere, https://doi.org/10.5194/egusphere-2024-3754, https://doi.org/10.5194/egusphere-2024-3754, 2025
This preprint is open for discussion and under review for Solid Earth (SE).
Short summary
Short summary
Geophysical data is used to understand the Earth's structure. Geophysical inversion is an optimisation technique that constructs a model of the Earth using geophysical data. Inversion is complex and requires constraints to produce a plausible model. Overfitting is a disadvantageous effect that can affect techniques that rely on optimisation. A new feature in an inversion platform reduces overfitting by informing inversion of spatial uncertainty contained within the geophysical data.
Léonard Moracchini, Guillaume Pirot, Kerry Bardot, Mark W. Jessell, and James L. McCallum
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-154, https://doi.org/10.5194/gmd-2024-154, 2024
Revised manuscript under review for GMD
Short summary
Short summary
To facilitate the exploration of alternative hydrogeological scenarios, we propose to approximate costly physical simulations of contaminant transport by more affordable shortest distances computations. It enables to accept or reject scenarios within a predefined confidence interval. In particular, it can allow to estimate the probability of a fault acting as a preferential path or a barrier.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
Short summary
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Jérémie Giraud, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, and Paul Cupillard
Solid Earth, 15, 63–89, https://doi.org/10.5194/se-15-63-2024, https://doi.org/10.5194/se-15-63-2024, 2024
Short summary
Short summary
We present and test an algorithm that integrates geological modelling into deterministic geophysical inversion. 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 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.
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.
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.
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
Topological analysis in Monte Carlo simulation for uncertainty propagation
Joint analysis of the magnetic field and total gradient intensity in central Europe
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.
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.
Cited articles
Abtahi, S. M., Pedersen, L. B., Kamm, J., and Kalscheuer, T.: Case History
Extracting geoelectrical maps from vintage very-low-frequency airborne data,
tipper inversion, and interpretation: A case study from northern Sweden,
Geophysics, 81, B135–B147, https://doi.org/10.1190/geo2015-0296.1, 2016.
Abubakar, A., Gao, G., Habashy, T. M., and Liu, J.: Joint inversion approaches
for geophysical electromagnetic and elastic full-waveform data, Inverse Probl.,
28, 055016, https://doi.org/10.1088/0266-5611/28/5/055016, 2012.
Allmendinger, R. W., Siron, C. R., and Scott, C. P.: Structural data collection
with mobile devices: Accuracy, redundancy, and best practices, J. Struct. Geol.,
102, 98–112, https://doi.org/10.1016/j.jsg.2017.07.011, 2017.
Brown, V., Key, K., and Singh, S.: Seismically regularized controlled-source
electromagnetic inversion, Geophysics, 77, E57–E65, https://doi.org/10.1190/geo2011-0081.1, 2012.
Calcagno, P., Chilès, J. P., Courrioux, G., and Guillen, A.: Geological
modelling from field data and geological knowledge. Part I. Modelling method
coupling 3D potential-field interpolation and geological rules, Phys. Earth
Planet. Inter., 171, 147–157, https://doi.org/10.1016/j.pepi.2008.06.013, 2008.
Cawood, A. J., Bond, C. E., Howell, J. A., Butler, R. W. H., and Totake, Y.:
LiDAR, UAV or compass-clinometer? Accuracy, coverage and the effects on
structural models, J. Struct. Geol., 98, 67–82, https://doi.org/10.1016/j.jsg.2017.04.004, 2017.
de la Varga, M. and Wellmann, J. F.: Structural geologic modeling as an inference
problem: A Bayesian perspective, Interpretation, 4, SM1–SM16, https://doi.org/10.1190/INT-2015-0188.1, 2016.
de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic
geological modeling and inversion, Geosci. Model Dev. Discuss.,
https://doi.org/10.5194/gmd-2018-61, in review, 2018.
Demirel, C. and Candansayar, M. E.: Two-dimensional joint inversions of
cross-hole resistivity data and resolution analysis of combined arrays, Geophys.
Prospect., 65, 876–890, https://doi.org/10.1111/1365-2478.12432, 2017.
Dentith, M. and Mudge, S. T.: Geophysics for the mineral exploration geologist,
Cambridge University Press, Cambridge, UK, 2014.
Gallardo, L. A. and Meju, M. A.: Characterization of heterogeneous near-surface
materials by joint 2D inversion of dc resistivity and seismic data, Geophys.
Res. Lett., 30, 1658, https://doi.org/10.1029/2003GL017370, 2003.
Gallardo, L. A. and Meju, M. A.: Joint two-dimensional DC resistivity and
seismic travel time inversion with cross-gradients constraints, J. Geophys.
Res.-Solid, 109, B03311, https://doi.org/10.1029/2003JB002716, 2004.
Gallardo, L. A. and Meju, M. A.: Joint two-dimensional cross-gradient imaging
of magnetotelluric and seismic traveltime data for structural and lithological
classification, Geophys. J. Int., 169, 1261–1272, https://doi.org/10.1111/j.1365-246X.2007.03366.x, 2007.
Gallardo, L. A. and Meju, M. A.: Structure-coupled multiphysics imaging in
geophysical sciences, Rev. Geophys., 49, RG1003, https://doi.org/10.1029/2010RG000330, 2011.
Gallardo, L. A., Fontes, S. L., Meju, M. A., Buonora, M. P., and de Lugao, P.
P.: Robust geophysical integration through structure-coupled joint inversion
and multispectral fusion of seismic reflection, magnetotelluric, magnetic,
and gravity images: Example from Santos Basin, offshore Brazil, Geophysics,
77, B237–B251, https://doi.org/10.1190/geo2011-0394.1, 2012.
Gao, G., Abubakar, A., and Habashy, T. M.: Joint petrophysical inversion of
electromagnetic and full-waveform seismic data, Geophysics, 77, WA3, https://doi.org/10.1190/geo2011-0157.1, 2012.
Giraud, J., Jessell, M., Lindsay, M., Parkyuz-Charrier, E., and Martin, R.:
Integrated geophysical joint inversion using petrophysical constraints and
geological modelling, in: SEG Technical Program Expanded Abstracts 2016,
Society of Exploration Geophysicists, Houston, TX, 1597–1601, 2016.
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., Lindsay, M., and Ogarko, V.: Yerrida Basin Geophysical Modeling – Input
data and inverted models, Version version 1.0, Data set, Zenodo, https://doi.org/10.5281/zenodo.1238216, 2018a.
Giraud, J., Ogarko, V., and Pakyuz-Charrier, V.: Synthetic dataset for the
testing of local conditioning of regularization function using geological
uncertainty, Version version 1.0, Data set, Zenodo, https://doi.org/10.5281/zenodo.1238529, 2018b.
Guillen, A., Calcagno, P., Courrioux, G., Joly, A., and Ledru, P.: Geological
modelling from field data and geological knowledge. Part II. Modelling
validation using gravity and magnetic data inversion, Phys. Earth Planet. Inter.,
171, 158–169, https://doi.org/10.1016/j.pepi.2008.06.014, 2008.
Guo, Z., Dong, H., and Kristensen, Å.: Image-guided regularization of marine
electromagnetic inversion, Geophysics, 82, E221–E232, https://doi.org/10.1190/geo2016-0130.1, 2017.
Haber, E. and Oldenburg, D.: Joint inversion: a structural approach, Inverse
Probl., 13, 63–77, https://doi.org/10.1088/0266-5611/13/1/006, 1997.
Heincke, B., Jegen, M., Moorkamp, M., Hobbs, R. W., and Chen, J.: An adaptive
coupling strategy for joint inversions that use petrophysical information as
constraints, J. Appl. Geophys., 136, 279–297, https://doi.org/10.1016/j.jappgeo.2016.10.028, 2017.
Hoerl, A. E. and Kennard, R. W.: Ridge Regression: Application to nonorthogonal
problems, Technometrics, 12, 69–82, https://doi.org/10.1080/00401706.1970.10488634, 1970.
Jardani, A., Revil, A., and Dupont, J. P.: Stochastic joint inversion of
hydrogeophysical data for salt tracer test monitoring and hydraulic conductivity
imaging, Adv. Water Resour., 52, 62–77, https://doi.org/10.1016/j.advwatres.2012.08.005, 2013.
Jessell, M. W., Ailleres, L., and de Kemp, E. A.: Towards an integrated
inversion of geoscientific data: What price of geology?, Tectonophysics, 490,
294–306, https://doi.org/10.1016/j.tecto.2010.05.020, 2010.
Jessell, M. W., Aillères, L., De Kemp, E., Lindsay, M., Wellmann, F.,
Hillier, M., Laurent, G., Carmichael, T., and Martin, R.: Next Generation
Three-Dimensional Geologic Modeling and Inversion, SEG Spec. Publ. 18,
chap. 13, SEG, Keystone, CO, USA, 261–272, 2014.
Jessell, M. W., Pakyuz-charrier, E., Lindsay, M., Giraud, J., and de Kemp, E.:
Assessing and Mitigating Uncertainty in Three-Dimensional Geologic Models in
Contrasting Geologic Scenarios, SEG Special Publications no. 21, SEG, Keystone,
CO, USA, 63–74, https://doi.org/10.5382/SP.21.04, 2018.
Johnson, S. P., Thorne, A. M., Tyler, I. M., Korsch, R. J., Kennett, B. L. N.,
Cutten, H. N., Goodwin, J., Blay, O., Blewett, R. S., Joly, A., Dentith, M. C.,
Aitken, A. R. A., Holzschuh, J., Salmon, M., Reading, A., Heinson, G., Boren,
G., Ross, J., Costelloe, R. D., and Fomin, T.: Crustal architecture of the
Capricorn Orogen, Western Australia and associated metallogeny, Aust. J. Earth
Sci., 60, 681–705, https://doi.org/10.1080/08120099.2013.826735, 2013.
Juhojuntti, N. and Kamm, J.: Joint inversion of seismic refraction and
resistivity data using layered models – Applications to groundwater
investigation, Geophysics, 80, EN43–EN55, https://doi.org/10.1190/geo2013-0476.1, 2015.
Kalscheuer, T., Blake, S., Podgorski, J. E., Wagner, F., Green, A. G., Maurer,
H., Jones, A. G., Muller, M., Ntibinyane, O., and Tshoso, G.: Joint inversions
of three types of electromagnetic data explicitly constrained by seismic
observations: results from the central Okavango Delta, Botswana, Geophys. J.
Int., 202, 1429–1452, https://doi.org/10.1093/gji/ggv184, 2015.
Lelièvre, P. G. and Farquharson, C. G.: Integrated Imaging for Mineral
Exploration, in: Integrated Imaging of the Earth: Theory and Applications,
John Wiley & Sons, Inc., New York, 137–166, 2016.
Lelièvre, P. G., Farquharson, C., and Hurich, C.: Joint inversion of seismic
traveltimes and gravity data on unstructured grids with application to mineral
exploration, Geophysics, 77, K1–K15, https://doi.org/10.1190/geo2011-0154.1, 2012.
Ley-Cooper, A.-Y., Munday, T., and Ibrahimi, T.: Inversion of the Capricorn
Orogeny regional Airborne Electromagnetic (AEM) survey, Western Australia,
CSIRO Technical Report EP166290, CSIRO, Perth, WA, 2017.
Li, Y. and Oldenburg, D. W.: 3-D inversion of magnetic data, Geophysics, 61,
394–408, https://doi.org/10.1190/1.1443968, 1996.
Lindsay, M. D., Aillères, L., Jessell, M. W., de Kemp, E. A., and Betts, P.
G.: Locating and quantifying geological uncertainty in three-dimensional models:
Analysis of the Gippsland Basin, southeastern Australia, Tectonophysics,
546–547, 10–27, https://doi.org/10.1016/j.tecto.2012.04.007, 2012.
Lindsay, M. D., Perrouty, S., Jessell, M. W., and Aillères, L.: Making the
link between geological and geophysical uncertainty: geodiversity in the Ashanti
Greenstone Belt, Geophys. J. Int., 195, 903–922, https://doi.org/10.1093/gji/ggt311, 2013a.
Lindsay, M. D., Jessell, M. W., Ailleres, L., Perrouty, S., de Kemp, E., and
Betts, P. G.: Geodiversity: Exploration of 3D geological model space,
Tectonophysics, 594, 27–37, https://doi.org/10.1016/j.tecto.2013.03.013, 2013b.
Lindsay, M. D., Perrouty, S., Jessell, M., and Ailleres, L.: Inversion and
Geodiversity: Searching Model Space for the Answers, Math. Geosci., 46,
971–1010, https://doi.org/10.1007/s11004-014-9538-x, 2014.
Martin, R., Monteiller, V., Komatitsch, D., Perrouty, S., Jessell, M., Bonvalot,
S., and Lindsay, M.: Gravity inversion using wavelet-based compression on
parallel hybrid CPU/GPU systems: application to southwest Ghana, Geophys. J.
Int., 195, 1594–1619, https://doi.org/10.1093/gji/ggt334, 2013.
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 regularization, Measurement, 128, 428–445,
https://doi.org/10.1016/j.measurement.2018.05.099, 2018.
Molodtsov, D. M., Troyan, V. N., Roslov, Y. V., and Zerilli, A.: Joint inversion
of seismic traveltimes and magnetotelluric data with a directed structural
constraint, Geophys. Prospect., 61, 1218–1228, https://doi.org/10.1111/1365-2478.12060, 2013.
Moorkamp, M., Roberts, A. W., Jegen, M., Heincke, B., and Hobbs, R. W.:
Verification of velocity-resistivity relationships derived from structural
joint inversion with borehole data, Geophys. Res. Lett., 40, 3596–3601,
https://doi.org/10.1002/grl.50696, 2013.
Novakova, L. and Pavlis, T. L.: Assessment of the precision of smart phones
and tablets for measurement of planar orientations: A case study, J. Struct.
Geol., 97, 93–103, https://doi.org/10.1016/j.jsg.2017.02.015, 2017.
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.
Olierook, H. K. H., Sheppard, S., Johnson, S. P., Occhipinti, S. A., Reddy, S.
M., Clark, C., Fletcher, I. R., Rasmussen, B., Zi, J. W., Pirajno, F., LaFlamme,
C., Do, T., Ware, B., Blandthorn, E., Lindsay, M., Lu, Y. J., Crossley, R. J.,
and Erickson, T. M.: Extensional episodes in the Paleoproterozoic Capricorn
Orogen, Western Australia, revealed by petrogenesis and geochronology of
mafic–ultramafic rocks, Precambrian Res., 306, 22–40, https://doi.org/10.1016/j.precamres.2017.12.015, 2018.
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.
Pakyuz-Charrier, E., Giraud, J., Ogarko, V., Lindsay, M., and Jessell, M.:
Drillhole uncertainty propagation for three-dimensional geological modeling
using Monte Carlo, Tectonophysics, 747–748, 16–39, https://doi.org/10.1016/j.tecto.2018.09.005, 2018a.
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, 2018b.
Pears, G., Reid, J., and Chalke, T.: Advances in Geologically Constrained
Modelling and Inversion Strategies to Drive Integrated Interpretation in
Mineral Exploration, in: Proceedings of Exploration 17: Sixth Decennial
International Conference on Mineral Exploration, edited by: Tschirhart, V. and
Thomas, V., Toronto. 221–238, available at: http://www.dmec.ca/getattachment/e27f3192-d38e-4d8a-a5f2-404e5d4ca632/Resources/Exploration-17/Iterative-Forward-Modelling-and-Inversion-of-Geoph.aspx
(last access: 17 January 2019), 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: 17 January 2019), 2000.
Pirajno, F. and Occhipinti, S. A.: Three Palaeoproterozoic basins – Yerrida,
Bryah and Padbury – Capricorn Orogen, Western Australia, Aust. J. Earth Sci.,
47, 675–688, https://doi.org/10.1046/j.1440-0952.2000.00800.x, 2000.
Pirajno, F., Occhipinti, S. A., and Swager, C. P.: Geology and tectonic
evolution of the Palaeoproterozoic Bryah, Padbury and Yerrida Basins (formerly
Glengarry Basin), Western Australia: implications for the history of the
south-central Capricorn Orogen, Precambrian Res., 90, 119–140, https://doi.org/10.1016/S0301-9268(98)00045-X, 1998.
Portniaguine, O. and Zhdanov, M. S.: 3-D magnetic inversion with data compression
and image focusing, Geoophysics, 67, 1532–1541, https://doi.org/10.1190/1.1512749, 2002.
Rittgers, J. B., Revil, A., Mooney, M. A., Karaoulis, M., Wodajo, L., and Hickey,
C. J.: Time-lapse joint inversion of geophysical data with automatic joint
constraints and dynamic attributes, Geophys. J. Int., 207, 1401–1419,
https://doi.org/10.1093/gji/ggw346, 2016.
Schweizer, D., Blum, P., and Butscher, C.: Uncertainty assessment in 3-D
geological models of increasing complexity, Solid Earth, 8, 515–530,
https://doi.org/10.5194/se-8-515-2017, 2017.
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.
Sun, J. and Li, Y.: Joint inversion of multiple geophysical data using guided
fuzzy c-means clustering, Geophysics, 81, ID37–ID57, https://doi.org/10.1190/geo2015-0457.1, 2016.
Sun, J. and Li, Y.: Joint inversion of multiple geophysical and petrophysical
data using generalized fuzzy clustering algorithms, Geophys. J. Int., 208,
1201, https://doi.org/10.1093/gji/ggw442, 2017.
Thiele, S. T., Jessell, M. W., Lindsay, M., Wellmann, J. F., and Pakyuz-Charrier,
E.: The topology of geology 2: Topological uncertainty, J. Struct. Geol., 91,
74–87, https://doi.org/10.1016/j.jsg.2016.08.010, 2016.
Tikhonov, A. N. and Arsenin, V. Y.: Solutions of Ill-Posed Problems, John Wiley,
New York, 1977.
Vatankhah, S. and Renaut, R. A.: Comment on: `Improving compact gravity inversion
based on new weighting functions', by Mohammad Hossein Ghalehnoee, Abdolhamid
Ansari and Ahmad Ghorbani, Geophys. J. Int., 211, 346–348, https://doi.org/10.1093/gji/ggx058, 2017.
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.
Wellmann, J. F., Lindsay, M., Poh, J., and Jessell, M.: Validating 3-D Structural
Models with Geological Knowledge for Improved Uncertainty Evaluations, Energy
Procedia, 59, 374–381, https://doi.org/10.1016/j.egypro.2014.10.391, 2014.
Wellmann, J. F., de la Varga, M., Murdie, R. E., Gessner, K., and Jessell, M.:
Uncertainty estimation for a geological model of the Sandstone greenstone belt,
Western Australia – insights from integrated geological and geophysical
inversion in a Bayesian inference framework, Spec. Publ. SP453.12, Geol. Soc.,
London, https://doi.org/10.1144/SP453.12, 2017.
Wiik, T., Nordskag, J. I., Dischler, E. Ø., and Nguyen, A. K.: Inversion of
inline and broadside marine controlled-source electromagnetic data with
constraints derived from seismic data, Geophys. Prospect., 63, 1371–1382,
https://doi.org/10.1111/1365-2478.12294, 2015.
Williams, N. C.: Geologically-constrained UBC-GIF gravity and magnetic
inversions with examples from the Agnew-Wiluna greenstone belt, Western
Australia, University of British Columbia, available at:
https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0052390
(last access: 17 January 2019), 2008.
Williams, N. C.: Mass and magnetic properties for 3D geological and geophysical
modelling of the southern Agnew–Wiluna Greenstone Belt and Leinster nickel
deposits, Western Australia, Aust. J. Earth Sci., 56, 1111–1142,
https://doi.org/10.1080/08120090903246220, 2009.
Yamamoto, J. K., Koike, K., Kikuda, A. T., da Campanha, G. A. C., and Endlen, A.:
Post-processing for uncertainty reduction in computed 3D geological models,
Tectonophysics, 633, 232–245, https://doi.org/10.1016/j.tecto.2014.07.013, 2014.
Yan, P., Kalscheuer, T., Hedin, P., and Garcia Juanatey, M. A.: Two-dimensional
magnetotelluric inversion using reflection seismic data as constraints and
application in the COSC project, Geophys. Res. Lett., 44, 3554–3563,
https://doi.org/10.1002/2017GL072953, 2017.
Zhdanov, M. S., Gribenko, A., and Wilson, G.: Generalized joint inversion of
multimodal geophysical data using Gramian constraints, Geophys. Res. Lett.,
39, L09301, https://doi.org/10.1029/2012GL051233, 2012.
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.
We propose the quantitative integration of geology and geophysics in an algorithm integrating...
