Rigorously quantifying observational uncertainty is essential for accelerating and automating geophysical inversions for subsurface mineral exploration

Le résumé fourni par la source

The green energy transition is driving unprecedented demand for critical minerals. To meet this demand, we not only need to discover more mineral deposits, but accelerate the rate of these new discoveries. It is unlikely that many new discoveries will be based on surface observations alone, so geophysics will be valuable in providing the subsurface information required to find new deposits. However, applying geophysics to explore for new mineral deposits is limited by two key factors: uncertainty in subsurface images caused by non-uniqueness and the time taken to get these results from the field to decision makers. Better observational uncertainty quantification can address both these challenges. Here, we first emphasise the theoretical trade-off between subjective inversion choices and observational uncertainty, before practically showing the sensitivity of subsurface models output from geophysical inversions to observational (measurement) uncertainties via real-world examples. We first use an induced polarisation inversion to demonstrate how quantifying observational uncertainties not only results in more plausible subsurface images but also results that are less sensitive to subjective regularisation choices (due to decreased non-uniqueness). We then show a similar result for a seismology example: ambient noise tomography. We also briefly introduce the benefits for performing joint inversions and increasing inversion computational efficiency, as well as recent instrumentation advances that could drive a step-change in observational uncertainty quantification. The theoretical basis of what we show is not novel and the effects of quantifying observational uncertainty on output models are obvious. However, what we wish to emphasise here is instead the impact of quantifying uncertainty and rigorously including it in inversion workflows on reducing subjectivity of geophysical inversions. Reducing subjectivity is essential in the endeavour to automate inversion workflows. The drive to automate workflows is motivated by speed gains and near real-time exploration. If one can speed up inversion workflows then one can unlock near-real-time mineral exploration, allowing the mining industry to explore regions far faster than otherwise possible and meet the increased demand posed by the green energy transition.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.