Articles | Volume 14, issue 1
https://doi.org/10.5194/essd-14-381-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-381-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
01 Feb 2022
Data description paper |
| 01 Feb 2022
| 01 Feb 2022
Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Jiateng Guo
College of Resources and Civil Engineering, Northeastern University,
Shenyang, China
Yunqiang Li
College of Resources and Civil Engineering, Northeastern University,
Shenyang, China
Mark Lindsay
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Mineral Resources, Commonwealth Scientific and Industrial Research Organisation, Australian Resources Research Centre, Kensington, Australia
Richard Scalzo
School of Mathematics and Statistics, University of Sydney, Sydney,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Jérémie Giraud
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
GeoRessources, Université de Lorraine, CNRS, 54000 Nancy, France
Guillaume Pirot
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Ed Cripps
Department of Mathematics and Statistics, The University of Western
Australia, Perth, Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Vitaliy Ogarko
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We developed a new method to reconstruct underground rock layers from drillhole data, using an advanced algorithm to ensure geologically realistic results. By combining data from multiple drillholes, our approach reduces uncertainty and improves accuracy. Tested on South Australian data, it successfully predicted stratigraphy and highlighted ways to enhance data quality. This innovation makes geological analysis more reliable, aiding exploration and resource management.
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Revised manuscript accepted for GMD
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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.
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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
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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.
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Solid Earth, 14, 43–68, https://doi.org/10.5194/se-14-43-2023, https://doi.org/10.5194/se-14-43-2023, 2023
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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.
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.
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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.
To robustly train and test automated methods in the geosciences, we need to have access to large...
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