An automated method for topology consistent processing of parallel geological cross-sections based on topology reasoning

Abstract

The 3D modeling method based on parallel geological cross-sections is suitable for modeling large-scale bedrock geological bodies. However, there are usually more branching, pinch-out strata, deformation structures (such as faults and folds), and the correspondence problem of geological boundaries between adjacent cross-sections. These problems have become the most crucial obstacle in the current application of 3D geological modeling based on parallel geological cross-sections. To this end, this paper proposes a topological reasoning automatic processing method for the consistency of parallel geological cross-sections to obtain topologically consistent adjacent cross-sections efficiently. The method involves (1) Stratigraphic inference based on the distance and topological relationships, transforming strata with m:n relationships into three relatively simple cases of 1:1, 1:n, and 1:0; (2) Splitting branching strata based on the Voronoi diagram, transforming strata with 1:n relationships into 1:1 relationships; (3) Dealing pinch-out strata based on the method of filling virtual strata, transforming strata with 1:0 relationships into 1:1 relationships (4) Topologically consistent processing of stratigraphic boundaries based on automatic recognition of no-corresponding arcs and addition of virtual stratum. Based on experiments in the Longtoushan Hill area in Jiangsu Province, China, and the southeast side of Chandler Mountain, Alabama, USA, this method can effectively replace the many manual processes such as determining the location of pinch-out adding auxiliary cross-sections and performing branching strata correspondence in the current application of 3D geological modeling based on arc segments. The geological cross-sections processed based on this method have an exemplary data structure and consistent topological relationships. The 3D geological modeling algorithm based on parallel cross-sections has the advantages of low algorithm complexity, high automation, and stable modeling quality.