Towards More Reliable Surface Geometry Inversion Methods for Mineral Exploration

Most large and easily accessible mineral deposits have been found and exploited. To continue to supply critical mineral resources central to global industries, mineral exploration must move to deposits that are deeper or smaller, and therefore are more challenging to identify and characterize using geophysical methods. To provide reliable imaging results for these challenging scenarios, new inversion techniques are required that can reduce the non-uniqueness of the inverse problem through tight integration of geophysical and geological data.

For this purpose, we are studying surface-geometry inversion (SGI) methods, which parameterize the Earth in terms of surfaces representing interfaces between different rock units. This parameterization is more consistent with geologists' understandings of the Earth, and has high potential to allow the tight integration of geophysical and geological information that we seek. Our SGI approach effectively takes some initial surface-based model, for example a geological model, and alters the position of the surfaces to improve the fit to the geophysical data. Using geophysical inversion to determine the geometry of subsurface targets has a long-established history, tracing back to the early days of geophysical interpretation. These methods continue to gain considerable attention because of the growing demand for more precise and interpretable visual representations of subsurface bodies.

Recently, SGI methods are becoming increasingly common and have been applied to many varied imaging scenarios. However, little work has thoroughly assessed the reliability of these methods. It is important to know whether the solutions obtained from SGI are unique and stable and, if they are not, how to add regularization or constraints to make them so. Without a well-posed problem, any interpretations of the subsurface based on those solutions, and any exploration decisions based on those interpretations, are unreliable. Assessing the numerical characteristics of SGI problems is challenging because they overwhelmingly use global heuristic optimization methods and stochastic sampling in their solution, they are severely nonlinear, and they lack explicit matrix operators and derivatives. A critical aspect is understanding when regularization/stabilization should be incorporated into the SGI optimization problem to create a well-posed problem. In this work, we make headway towards a better understanding of these important issues in the specific context of inverting potential field data for mineral exploration scenarios.