Articles | Volume 12, issue 7
Geosci. Model Dev., 12, 2941–2960, 2019
https://doi.org/10.5194/gmd-12-2941-2019
Geosci. Model Dev., 12, 2941–2960, 2019
https://doi.org/10.5194/gmd-12-2941-2019
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.

Related authors

Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic 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
Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications
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
Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models
Hugo K. H. Olierook, Richard Scalzo, David Kohn, Rohitash Chandra, Ehsan Farahbakhsh, Gregory Houseman, Chris Clark, Steven M. Reddy, and R. Dietmar Müller
Solid Earth Discuss., https://doi.org/10.5194/se-2019-4,https://doi.org/10.5194/se-2019-4, 2019
Revised manuscript not accepted

Related subject area

Numerical methods
Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0)
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022,https://doi.org/10.5194/gmd-15-9015-2022, 2022
Short summary
Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022,https://doi.org/10.5194/gmd-15-8765-2022, 2022
Short summary
ISMIP-HOM benchmark experiments using Underworld
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Geosci. Model Dev., 15, 8749–8764, https://doi.org/10.5194/gmd-15-8749-2022,https://doi.org/10.5194/gmd-15-8749-2022, 2022
Short summary
spyro: a Firedrake-based wave propagation and full-waveform-inversion finite-element solver
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Geosci. Model Dev., 15, 8639–8667, https://doi.org/10.5194/gmd-15-8639-2022,https://doi.org/10.5194/gmd-15-8639-2022, 2022
Short summary
Spatial filtering in a 6D hybrid-Vlasov scheme to alleviate adaptive mesh refinement artifacts: a case study with Vlasiator (versions 5.0, 5.1, and 5.2.1)
Konstantinos Papadakis, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Markku Alho, Maxime Grandin, Maxime Dubart, Lucile Turc, Hongyang Zhou, Konstantinos Horaites, Ivan Zaitsev, Giulia Cozzani, Maarja Bussov, Evgeny Gordeev, Fasil Tesema, Harriet George, Jonas Suni, Vertti Tarvus, and Minna Palmroth
Geosci. Model Dev., 15, 7903–7912, https://doi.org/10.5194/gmd-15-7903-2022,https://doi.org/10.5194/gmd-15-7903-2022, 2022
Short summary

Cited articles

Agostinetti, N. P. and Malinverno, A.: Receiver function inversion by trans-dimensional Monte Carlo sampling, Geophys. J. Int., 181, 858–872, https://doi.org/10.1111/j.1365-246X.2010.04530.x, 2010. a, b, c
Anand, R. R. and Butt, C. R. M.: A guide for mineral exploration through the regolith in the Yilgarn Craton, Western Australia, Aust. J. Earth Sci., 57, 1015–1114, 2010. a
Beardsmore, G.: Data fusion and machine learning for geothermal target exploration and characterisation, Tech. rep., NICTA Final Report, available at: https://arena.gov.au/projects/data-fusion-and-machine-learning-for-geothermal/ (last access: 10 July 2019), 2014. a, b, c, d
Beardsmore, G., Durrant-Whyte, H., McCalman, L., O’Callaghan, S., and Reid, A.: A Bayesian inference tool for geophysical joint inversions, ASEG Extended Abstracts, 2016, 1–10, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Bodin, T., Sambridge, M., Tkalcic, H., Arroucau, P., Gallagher, K., and Rawlinson, N.: Transdimensional inversion of receiver functions and surface wave dispersion, Solid Earth, 117, B02301, https://doi.org/10.1029/2011JB008560, 2012. a, b, c
Download
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.