Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-2941-2019
© 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/gmd-12-2941-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
15 Jul 2019
Development and technical paper |
| 15 Jul 2019
| 15 Jul 2019
Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
Richard Scalzo
CORRESPONDING AUTHOR
Centre for Translational Data Science, University of Sydney,
Darlington NSW 2008, Australia
David Kohn
Sydney Informatics Hub, University of Sydney,
Darlington NSW 2008, Australia
Hugo Olierook
School of Earth and Planetary Sciences, Curtin University,
Bentley WA 6102, Australia
Gregory Houseman
School of Earth and Environment, University of Leeds,
Leeds, LS2 9JT, UK
Rohitash Chandra
Centre for Translational Data Science, University of Sydney,
Darlington NSW 2008, Australia
School of Geosciences, University of Sydney,
Darlington NSW 2008, Australia
Mark Girolami
The Alan Turing Institute for Data Science,
British Library, 96 Euston Road, London, NW1 2DB, UK
Department of Mathematics,
Imperial College London, London, SW7 2AZ, UK
Sally Cripps
Centre for Translational Data Science, University of Sydney,
Darlington NSW 2008, Australia
School of Mathematics and Statistics,
University of Sydney, Darlington NSW 2008, Australia
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31 citations as recorded by crossref.
- Three-Dimensional Prospective Modeling and Deep Metallogenic Prediction of the Lintan Gold Deposit in Guizhou Province, China S. Cheng et al. https://doi.org/10.3390/min14121295
- Uncertainty: Nothing is more certain S. Cripps & H. Durrant‐Whyte https://doi.org/10.1002/env.2745
- Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution R. Chandra et al. https://doi.org/10.1029/2019GC008465
- Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography A. Vinciguerra et al. https://doi.org/10.1111/1365-2478.13148
- Assessing geometrical uncertainties in geological interface models using Markov chain Monte Carlo sampling via abstract graph J. Huang et al. https://doi.org/10.1016/j.tecto.2023.230032
- Transdimensional and Hamiltonian Monte Carlo inversions of Rayleigh‐wave dispersion curves: a comparison on synthetic datasets M. Aleardi et al. https://doi.org/10.1002/nsg.12100
- Combining discrete cosine transform and convolutional neural networks to speed up the Hamiltonian Monte Carlo inversion of pre‐stack seismic data M. Aleardi https://doi.org/10.1111/1365-2478.13025
- GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences S. Haan https://doi.org/10.21105/joss.02690
- Surrogate-assisted parallel tempering for Bayesian neural learning R. Chandra et al. https://doi.org/10.1016/j.engappai.2020.103700
- Three-dimensional weights of evidence modelling of a deep-seated porphyry Cu deposit E. Farahbakhsh et al. https://doi.org/10.1144/geochem2020-038
- A geostatistical Markov chain Monte Carlo inversion algorithm for electrical resistivity tomography M. Aleardi et al. https://doi.org/10.1002/nsg.12133
- Uncertainty quantification of geologic model parameters in 3D gravity inversion by Hessian-informed Markov chain Monte Carlo Z. Liang et al. https://doi.org/10.1190/geo2021-0728.1
- 3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China X. Mao et al. https://doi.org/10.3390/min10030233
- Bayesian fusion of MT and AEM probabilistic models with geological data: examples from the eastern Gawler Craton, South Australia H. Seillé et al. https://doi.org/10.1080/08123985.2023.2222766
- Bayesian inversion of magnetotelluric data considering dimensionality discrepancies H. Seillé & G. Visser https://doi.org/10.1093/gji/ggaa391
- Efficient regional scale 3D potential field geophysical modelling to redefine the geometry of granite bodies beneath prospective, geologically complex, northwest Tasmania E. Eshaghi et al. https://doi.org/10.1016/j.oregeorev.2020.103799
- Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application J. Giraud et al. https://doi.org/10.5194/se-14-43-2023
- Discrete cosine transform for parameter space reduction in linear and non-linear AVA inversions M. Aleardi https://doi.org/10.1016/j.jappgeo.2020.104106
- Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models R. Scalzo et al. https://doi.org/10.5194/gmd-15-3641-2022
- Bayesian neural multi-source transfer learning R. Chandra & A. Kapoor https://doi.org/10.1016/j.neucom.2019.10.042
- Surrogate-assisted Bayesian inversion for landscape and basin evolution models R. Chandra et al. https://doi.org/10.5194/gmd-13-2959-2020
- Uncertainty assessment for 3D geologic modeling of fault zones based on geologic inputs and prior knowledge A. Krajnovich et al. https://doi.org/10.5194/se-11-1457-2020
- 3DWofE: An open-source software package for three-dimensional weights of evidence modeling E. Farahbakhsh et al. https://doi.org/10.1016/j.simpa.2020.100039
- Transdimensional joint inversion of flow and well log data using a cascaded Metropolis sampler on a layer-cake model J. Herrero et al. https://doi.org/10.1016/j.geoen.2024.213605
- Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning R. Chandra et al. https://doi.org/10.1016/j.envsoft.2021.105002
- Simulation-based mineral prospectivity modeling and Gray Wolf optimization algorithm for delimiting exploration targets K. Mostafaei et al. https://doi.org/10.1016/j.oregeorev.2025.106458
- Bayesian Neural Networks via MCMC: A Python-Based Tutorial R. Chandra & J. Simmons https://doi.org/10.1109/ACCESS.2024.3401234
- Uncertainty analysis of 3D potential-field deterministic inversion using mixed Lp norms X. Wei & J. Sun https://doi.org/10.1190/geo2020-0672.1
- Geophysical inversion for 3D contact surface geometry C. Galley et al. https://doi.org/10.1190/geo2019-0614.1
- Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models H. Olierook et al. https://doi.org/10.1016/j.gsf.2020.04.015
- Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics J. Pall et al. https://doi.org/10.1016/j.envsoft.2019.104610
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
Producing 3-D models of structures under the Earth's surface based on sensor data is a key problem in geophysics (for example, in mining exploration). There may be multiple models that explain the data well. We use the open-source Obsidian software to look at the efficiency of different methods for exploring the model space and attaching probabilities to models, leading to less biased results and a better idea of how sensor data interact with geological assumptions.
Producing 3-D models of structures under the Earth's surface based on sensor data is a key...
