A knowledge-driven modeling formalism for automatic structural interpretation

Building structural models of geological entities is generally addressed as an interpolation problem that requires human experts to interpret input data and use knowledge. Although experts can effectively interpret, their interpretations can be subjective and occasionally prone to error. This is largely due to under-sampling of data, requiring experts to make choices in the selection and preparation of these data and knowledge, and selection and configuration of modeling algorithms. Modeling algorithms also do not reflect the complex expert interpretation process, as they incorporate only a portion of the knowledge typically held by experts and have limited ability to directly interact with experts during the interpretation process itself. This makes it challenging to build geologically complex models and systematically identify and address inconsistencies in a model. Part of the solution to these issues is the formalization of the interpretation process, which incorporates more knowledge and better reflects expert decision-making. In this paper we develop and prototype such a formalization. A prototype algorithm and tool are presented and applied to simple folding structures, and the results are favorably compared to existing approaches. This comparison highlights the potential of the proposed approach to reduce the need for expert involvement and increase the range of knowledge utilized.