Towards Geologically Realistic Inversion

During the past 10 years there has been a shift in geophysical interpretation away from idealised geometrical bodies floating in air and towards fully three-dimensional models. This transition has been driven by a number of factors, both technological and conceptual. The principal conceptual driver has been the growing recognition of the importance of integrated interpretation. Interpretation is a shared responsibility of geoscientists, the common goal being an Earth model consistent with all available information. Inversion is a numerical process whereby an initial model is adjusted in order to improve the degree of agreement, or fit, between the measured geophysical data and the corresponding calculated data. Petrophysical properties provide the link between geophysics and geology, but this link is indirect. Most 3D inversion programs operate on quantitative “property only” models, which define a distribution of one or more physical properties in the sub-surface. After inversion of pure property models, geology must be inferred from the rock properties. Geological models are categorical, insofar as the sub-surface is divided into rock type domains. In order to capture both geology and petrophysics, a model must be both categorical and quantitative. Such ‘geo-physical’ models are a force for integration in their own right, and also offer a number of practical advantages over pure property models. In particular, the topological significance of geological boundaries is maintained, permitting geometry inversion as well as property inversion. Analysis of geophysical data alone is not sufficient to fully prescribe the sub-surface distribution of rock properties. Geological and petrophysical information, primarily derived from drill holes, is required in order to reduce uncertainty. “Ground truth”, in the form of drill hole pierce points, is imposed explicitly during geometry inversion as fixed points on the geological surfaces. Likewise, petrophysical measurements, if any, locally constrain the rock properties. In addition to these “hard” constraints, petrophysical data can if available in sufficient quantity, constitute a basis for statistically characterising and constraining the property distribution. Otherwise, “soft” constraints can be imposed in the form of a priori weights, e.g. to condition the depth and shape of causative bodies. Geologically-constrained inversion of gravity and magnetic data is illustrated below using a 3D potential fields inversion program interfaced to a 3D geological modelling package. Three examples are described, covering greenfields depth-to-basement (geometry) inversion over a large area, combined geometry and property inversion over a lightly drilled exploration prospect, and property inversion for brownfields exploration over the Cannington mine. Petrophysical property modelling in the mining industry is still fairly rudimentary overall. This is more a reflection on the state of the data than on the state of the art: modelling software exists, but petrophysical data is often inadequate or completely absent. In the next ten years, greater volumes of petrophysical data will support an expanded role for geostatistics. The stronger emphasis on petrophysics will be driven not only by the need for more sophisticated rock property modelling, but also by the demands of managers to quantify the uncertainty in interpretation.