Stochastic Inversion of geophysical data by sequential Bayesian updating under a non-stationary Gaussian process prior

Jef Caers, Peng Li, Jonas Kloeckner, Juan Pablo Daza, Zhen Yin, Celine Scheidt

The acquisition of geophysical data is becoming increasingly important in the context of critical mineral exploration. Geophysical data and inversion product are essential to map many components of the critical mineral system by detecting geophysical anomalies that can be interpreted by expert geologists. However, the inversion of airborne geophysical data acquired along flightlines into subsurface petrophysical properties remains an outstanding challenge. Many inversion techniques rely either on 1D deterministic inversion or on stochastic inversion on a local scale. The outcome of our work is the stochastic inversion along flightlines of 2D panels (flightline direction vs depth), while at the same time producing plausible spatial variation of the petrophysical properties. Our method relies on a sequential application of Bayesian inversion, where we invert a sequence of 2D panels such that the variation of petrophysical properties avoid generation of artifacts across the panel boundaries. We show that our method can be used in a practical setting in the context of mineral exploration in the Cape Smith Belt of Canada.