Articles | Volume 15, issue 9
Model description paper
09 May 2022
Model description paper |  | 09 May 2022

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

Related authors

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,,, 2022
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Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
Richard Scalzo, David Kohn, Hugo Olierook, Gregory Houseman, Rohitash Chandra, Mark Girolami, and Sally Cripps
Geosci. Model Dev., 12, 2941–2960,,, 2019
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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.,,, 2019
Revised manuscript not accepted

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Cited articles

Backus, G. and Gilbert, F.: The Resolving Power of Gross Earth Data, Geophys. J. Royal Astron. Soc., 16, 169–205,, 1968. a
Backus, G. and Gilbert, F.: Uniqueness in the inversion of inaccurate gross Earth data, Philos. T. Roy. Soc. Lond A, 266, 123–192,, 1970. a
Backus, G. E.: Long-wave elastic anisotropy produced by horizontal layering, J. Geophys. Res., 67, 4427–4440, 1962. a
Backus, G. E. and Gilbert, J. F.: Numerical Applications of a Formalism for Geophysical Inverse Problems, Geophys. J. Roy. Astron. Soc., 13, 247–276,, 1967. a
Beardsmore, G., Durrant-Whyte, H., and Callaghan, S. O.: A Bayesian inference tool for geophysical joint inversions, ASEG Extended Abstracts 2016.1 (2016), 1–10,, 2016. a, b, c
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.