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Automatic detection of geological folds using attributed relational graphs and formal grammar
- Li, An-Bo, Chen, Ying, Lü, Guo-Nian, Zhu, A-Xing
- Computers & geosciences 2019 v.127 pp. 75-84
- algorithms, automatic detection, cartography, case studies, computers, disaster preparedness, graphs, models, tectonics
- The accurate detection and characterization of geological structures are of primary importance for tectonic trend analysis, mineral and water resource exploration, major engineering projects, and geological disaster prevention. However, interpretation and mapping of geological structures by means of manual interpretation are inefficient and high-cost, and the performance varies greatly with the experts’ ability and experience. Folds are complex geological structures, and one of the most common and impressive geological phenomena. The objective of this study is to develop a novel method for the automatic detection of folds from geological maps using attributed relational graphs (ARG) and formal grammar. The method mainly involves (1) partitioning a study area into detecting units by the potential core stratum of fold (CSF) using Voronoi generator algorithm, (2) generating a strip for each detected unit used as modeling domain, (3) using ARG modeling to describe the spatial relations of strata for each strip, (4) defining the patterns of repeated strata corresponding to folds using formal grammar, (5) generating strings from the ARG model, and recognizing whether a symmetrically repeated substring can be extracted from the string using grammar inference to detect the presence of folds, (6) identifying fold types according to the temporal relationships of strata, and (7) mapping folds. Case studies demonstrate the effectiveness of this method for detecting folds in the Mount Lu and Muyunling areas. The proposed approach allows to speed up the interpretation of synclines/anticlines in providing a first rough detection solution, and can provide an appropriate solution to facilitate the detection of three-dimensional geological features from two-dimensional data.