Articles | Volume 12, issue 7
Geosci. Model Dev., 12, 2941–2960, 2019
Geosci. Model Dev., 12, 2941–2960, 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.

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Revised manuscript not accepted

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Cited articles

Agostinetti, N. P. and Malinverno, A.: Receiver function inversion by trans-dimensional Monte Carlo sampling, Geophys. J. Int., 181, 858–872,, 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: (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,, 2012. a, b, c
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.