Articles | Volume 12, issue 7
Geosci. Model Dev., 12, 2941–2960, 2019
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.
Geosci. Model Dev., 12, 2941–2960, 2019
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
Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
Richard Scalzo et al.
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Cited
22 citations as recorded by crossref.
- Bayesian neural multi-source transfer learning R. Chandra & A. Kapoor 10.1016/j.neucom.2019.10.042
- Surrogate-assisted Bayesian inversion for landscape and basin evolution models R. Chandra et al. 10.5194/gmd-13-2959-2020
- Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution R. Chandra et al. 10.1029/2019GC008465
- Uncertainty assessment for 3D geologic modeling of fault zones based on geologic inputs and prior knowledge A. Krajnovich et al. 10.5194/se-11-1457-2020
- 3DWofE: An open-source software package for three-dimensional weights of evidence modeling E. Farahbakhsh et al. 10.1016/j.simpa.2020.100039
- Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography A. Vinciguerra et al. 10.1111/1365-2478.13148
- Transdimensional and Hamiltonian Monte Carlo inversions of Rayleigh‐wave dispersion curves: a comparison on synthetic datasets M. Aleardi et al. 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 10.1111/1365-2478.13025
- GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences S. Haan 10.21105/joss.02690
- Surrogate-assisted parallel tempering for Bayesian neural learning R. Chandra et al. 10.1016/j.engappai.2020.103700
- Three-dimensional weights of evidence modelling of a deep-seated porphyry Cu deposit E. Farahbakhsh et al. 10.1144/geochem2020-038
- Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning R. Chandra et al. 10.1016/j.envsoft.2021.105002
- A geostatistical Markov chain Monte Carlo inversion algorithm for electrical resistivity tomography M. Aleardi et al. 10.1002/nsg.12133
- Uncertainty analysis of 3D potential-field deterministic inversion using mixed Lp norms X. Wei & J. Sun 10.1190/geo2020-0672.1
- 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. 10.3390/min10030233
- Bayesian inversion of magnetotelluric data considering dimensionality discrepancies H. Seillé & G. Visser 10.1093/gji/ggaa391
- Geophysical inversion for 3D contact surface geometry C. Galley et al. 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. 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. 10.1016/j.envsoft.2019.104610
- 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. 10.1016/j.oregeorev.2020.103799
- Application of Knowledge-Driven Methods for Mineral Prospectivity Mapping of Polymetallic Sulfide Deposits in the Southwest Indian Ridge between 46° and 52°E Y. Ma et al. 10.3390/min10110970
- Discrete cosine transform for parameter space reduction in linear and non-linear AVA inversions M. Aleardi 10.1016/j.jappgeo.2020.104106
22 citations as recorded by crossref.
- Bayesian neural multi-source transfer learning R. Chandra & A. Kapoor 10.1016/j.neucom.2019.10.042
- Surrogate-assisted Bayesian inversion for landscape and basin evolution models R. Chandra et al. 10.5194/gmd-13-2959-2020
- Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution R. Chandra et al. 10.1029/2019GC008465
- Uncertainty assessment for 3D geologic modeling of fault zones based on geologic inputs and prior knowledge A. Krajnovich et al. 10.5194/se-11-1457-2020
- 3DWofE: An open-source software package for three-dimensional weights of evidence modeling E. Farahbakhsh et al. 10.1016/j.simpa.2020.100039
- Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography A. Vinciguerra et al. 10.1111/1365-2478.13148
- Transdimensional and Hamiltonian Monte Carlo inversions of Rayleigh‐wave dispersion curves: a comparison on synthetic datasets M. Aleardi et al. 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 10.1111/1365-2478.13025
- GeoBO: Python package for Multi-Objective Bayesian Optimisation and Joint Inversion in Geosciences S. Haan 10.21105/joss.02690
- Surrogate-assisted parallel tempering for Bayesian neural learning R. Chandra et al. 10.1016/j.engappai.2020.103700
- Three-dimensional weights of evidence modelling of a deep-seated porphyry Cu deposit E. Farahbakhsh et al. 10.1144/geochem2020-038
- Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning R. Chandra et al. 10.1016/j.envsoft.2021.105002
- A geostatistical Markov chain Monte Carlo inversion algorithm for electrical resistivity tomography M. Aleardi et al. 10.1002/nsg.12133
- Uncertainty analysis of 3D potential-field deterministic inversion using mixed Lp norms X. Wei & J. Sun 10.1190/geo2020-0672.1
- 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. 10.3390/min10030233
- Bayesian inversion of magnetotelluric data considering dimensionality discrepancies H. Seillé & G. Visser 10.1093/gji/ggaa391
- Geophysical inversion for 3D contact surface geometry C. Galley et al. 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. 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. 10.1016/j.envsoft.2019.104610
- 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. 10.1016/j.oregeorev.2020.103799
- Application of Knowledge-Driven Methods for Mineral Prospectivity Mapping of Polymetallic Sulfide Deposits in the Southwest Indian Ridge between 46° and 52°E Y. Ma et al. 10.3390/min10110970
- Discrete cosine transform for parameter space reduction in linear and non-linear AVA inversions M. Aleardi 10.1016/j.jappgeo.2020.104106
Discussed (preprint)
Latest update: 31 Jan 2023
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...
