Towards geologically reasonable lithological classification from integrated geophysical inverse modelling: methodology and application case

Abstract

Abstract. We propose a methodology for the recovery of lithologies from geological and geophysical modelling results and apply it field data. Our technique relies on classification using self-organizing maps (SOM) paired with geoscientific consistency checks and uncertainty analysis. In the procedure we develop, the SOM is trained using prior geological information in the form of geological uncertainty, the expected spatial distribution of petrophysical properties, and constrained geophysical inversion results. We ensure local geological plausibility in the lithological model recovered from classification by enforcing basic topological rules through a process called post-regularisation. This prevents the three-dimensional lithological model from violating elementary geological principles while maintaining geophysical consistency. Interpretation of the resulting lithologies is complemented by the estimation of the uncertainty associated to the different nodes of the trained SOM. The application case we investigate uses data and models from the Yerrida Basin (Western Australia). Our results generally corroborate previous models of the region but they also suggest that the structural setting in some areas need to be updated. In particular, our results suggest the thinning of one of the greenstone belts in the area may be related to a deep structure not sampled by surface geological measurements and which was absent in previous geological models.

Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code

“…Tomofast-x is an extended implementation proposed and modified by Martin et al, 2018, Giraud et al (2019d, 2019c, Martin et al (2020), Ogarko et al (2020). Tomofast-x follows the object-oriented Fortran 2008 standard 130 and utilizes classes designed to account for the mathematics of the problem.…”

Multi‐scenario Interpretations From Sparse Fault Evidence Using Graph Theory and Geological Rules

“…Then, the geological formations affected by the fault network should be modeled. Generating such geometries would also be useful to assess the impact of fault network uncertainty on resource assessment (Richards et al, 2015), to incorporate this source of uncertainty in geophysical inverse problems (Giraud et al, 2019;Ragon et al, 2018), or to consider the geological likelihood in a Bayesian inference problem (Caumon, 2010;de la Varga & Wellmann, 2016). • Second, the graph formalism at this stage only considers pairwise associations but does not use the likelihood of associating several pieces of evidence at once.…”