Abstract
Access to information about the occurrence of past geological events and their chronology is crucial to cognize the evolution of subsurface structures. We refer to such tasks as Event Sequence Interpretation (ESI). The sequence of events describes the process of structural evolution and is the basis for structural interpretation and structural geological modeling. ESI has not been highly automated due to the need of a large amount of expert knowledge. However, manual ESI can introduce cognitive biases and is also difficult in structurally complex regions, thus affecting the credibility of structural interpretations. Therefore, we propose a knowledge-based ESI approach for structural geological models in this paper. A hierarchical cognition model lays the foundation for the ESI apptoach. A knowledge representation meta-model is used to formally represent the knowledge of geological events. Each instance of the meta-model is called an Event Pattern, which describes the associations between the occurrence of geological events and the geometric configuration of structural elements (geological surfaces and geological bodies). The chronology of geological events comes from the spatial relations of the structural elements. Our method can quickly infer the spatial relations between structural elements from the structural interpretation data and derive the possible temporal relationships between events from these spatial relationships. By demonstrating event sequence-guided structural modeling, we show the positive impact of event sequences on structural geological modeling.
Similar content being viewed by others
References
Abel M, Silva AL, De Ros LF, Mastella LS, Campbell JA, Novello T (2004) Petrographer: managing petrographic data and knowledge using an intelligent database application. Expert Syst Appl 26(1):9–18. https://doi.org/10.1016/S0957-4174(03)00104-0
Allen JF (1990) Maintaining knowledge about temporal intervals. Commun. ACM 26(11):361–372. https://doi.org/10.1145/182.358434
Babaie HA, Oldow JS, Babaei A, Lallemant H GA, Watkinson AJ, Sinha AK (2006) Designing a modular architecture for the structural geology ontology. SPECIAL PAPERS-GEOLOGICAL SOCIETY OF AMERICA 397:269. https://doi.org/10.1130/2006.2397(21)
Bharadwaj SS, Kumar RC, Sumukha BN, George K (2017) Pattern classification with meta-cognition and online sequential learning algorithm. In: 2017 International joint conference on neural networks (IJCNN), pp 1932–1939. IEEE
Biasotti S, Marini S, Mortara M, Patane G, Spagnuolo M, Falcidieno B (2003) 3d shape matching through topological structures. In: International conference on discrete geometry for computer imagery, pp 194–203. Springer
Bond CE (2015) Uncertainty in structural interpretation: Lessons to be learnt. J Struct Geol 74 (may):185–200. https://doi.org/10.1016/j.jsg.2015.03.003
Boote DavidRD (2018) The geological history of the istria ‘depression’, romanian black sea shelf: tectonic controls on second-/third-order sequence architecture. Geological Society, London, Special Publications 464 (1):169–209
Borgo S, Masolo C (2009) Foundational choices in dolce. In: Handbook on ontologies, pp 361–381. Springer
Burns KL (1975) Analysis of geological events. J Int Assoc Math Geol 7(4):295–321. https://doi.org/10.1007/BF02081703
Carbonera JL, Abel M, Scherer ClaitonMS (2015) Visual interpretation of events in petroleum exploration: An approach supported by well-founded ontologies. Expert Systems with applications 42(5):2749–2763. https://doi.org/10.1016/j.eswa.2014.11.021
Cen H, Koedinger K, Junker B (2006) Learning factors analysis–a general method for cognitive model evaluation and improvement. In: International conference on intelligent tutoring systems, pp 164–175. Springer
Chan CW (2002) Cognitive informatics: A knowledge engineering perspective. In: Proceedings first IEEE international conference on cognitive informatics, pp 49–56. IEEE
Chen D, Tucker ME, Jiang M, Zhu J (2001) Long-distance correlation between tectonic-controlled, isolated carbonate platforms by cyclostratigraphy and sequence stratigraphy in the devonian of south china. Sedimentology 48(1):57–78. https://doi.org/10.1016/j.quaint.2008.06.009
Egenhofer M (1990) A mathematical framework for the definition of topological relations. In: Proc. the fourth international symposium on spatial data handing, pp 803–813
Ford GP, Zhang J (1992) Structural graph-matching approach to image understanding. In: Intelligent robots and computer vision X: Algorithms and techniques, vol 1607, pp 559–569. International society for optics and photonics
Gero JS (1990) Design prototypes: a knowledge representation schema for design. AI magazine 11(4):26–26
Gruber TR (1993) A translation approach to portable ontology specifications. Knowledge acquisition 5(2):199–220. https://doi.org/10.1006/knac.1993.1008
Guarino N (1994) The ontological level. Philosophy and the cognitive sciences
Guizzardi G (2011) Ontological foundations for conceptual part-whole relations: The case of collectives and their parts. Springer, Berlin
Haghighi PD, Burstein F, Zaslavsky A, Arbon P (2013) Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decis Support Syst 54(2):1192–1204
Haproff PJ (2018) Tectonic evolution of the easternmost himalayan collisional system. Ph.D. Thesis, University of California, Los Angeles
Hoyes J, Cheret T (2011) A review of ‘global’ interpretation methods for automated 3d horizon picking. Lead Edge 30(1):38–47
Johansson I (2000) Determinables as universals. Monist 83(1):101–121. https://doi.org/10.5840/monist20008312
Johnson WE (1921) Logic. Cambridge University Press, Cambridge
Latecki LJ, Lakämper R, Wolter D (2003) Shape similarity and visual parts. In: International conference on discrete geometry for computer imagery, pp 34–51. Springer
Li L, Sugumaran V (2019) A cognitive-based aes model towards learning written english. J AMB INTEL HUM COMP 10(5):1811–1820. https://doi.org/10.1007/s12652-018-0743-1
Lim SL, Kurashov A, Bentley P (2016) Higher order cognition using computers: Learning abstract concepts with recursive graph-based self organizing maps. In: Proceedings of the artificial life conference 2016 13, pp 398–405. MIT Press
Liu C, Zhu E, Zhang Q, Wei X (2018) Modeling of agent cognition in extensive games via artificial neural networks. IEEE transactions on neural networks and learning systems 29(10):4857–4868. https://doi.org/10.1109/TNNLS.2017.2782266
Lomask J, Clapp RG, Biondi B (2007) Application of image segmentation to tracking 3d salt boundaries. Geophysics 72(4):P47–P56. https://doi.org/10.1190/1.2732553
Lorenzatti A, Abel M, Fiorini SR, Bernardes AK, dos SantosScherer CM (2010) Ontological primitives for visual knowledge. In: Brazilian symposium on artificial intelligence, pp 1–10. Springer
Malik J, Binford TO (1983) Reasoning in time and space. In: IJCAI, vol 83, pp 343–345
Masolo C, Borgo S, Gangemi A, Guarino N, Oltramari A (2003) Wonderweb deliverable d18, ontology library (final). ICT project 33052:31
Mastella LS, Abel M, DeRos LF, Perrin M, Rainaud J-F (2007) Event ordering reasoning ontology applied to petrology and geological modelling. In: Theoretical advances and applications of fuzzy logic and soft computing, pp 465–475. Springer
Mastella LS, Abel M, Lamb LC, DeRos LF (2005) Cognitive modelling of event ordering reasoning in imagistic domains. . In: International joint conference on artificial intelligence, vol 19, pp 528. LAWRENCE ERLBAUM ASSOCIATES LTD
Perrin M, Rainaud J-F (2013) Shared earth modeling: Knowledge driven solutions for building and managing subsurface 3d geological models
Rey J, Somoza L, Martínez-Frías J (1995) Tectonic, volcanic, and hydrothermal event sequence on deception island (antarctica). Geo-Mar Lett 15(1):1–8. https://doi.org/10.1007/BF01204491
Smalley I, O’Hara-Dhand K, Wint J, Machalett B, Jary Z, Jefferson I (2009) Rivers and loess: the significance of long river transportation in the complex event-sequence approach to loess deposit formation. Quat Int 198(1-2):7–18. https://doi.org/10.1016/j.quaint.2008.06.009
Wellmann F, Caumon G (2018) 3-d structural geological models: Concepts, methods, and uncertainties. Adv Geophys 59:1–121. https://doi.org/10.1016/bs.agph.2018.09.001
Wu X (2017) Directional structure-tensor-based coherence to detect seismic faults and channels. Geophysics 82(2):A13–A17. https://doi.org/10.1190/geo2016-0473.1
Wu X, Liang L, Shi Y, Fomel S (2019) Faultseg3d: Using synthetic data sets to train an end-to-end convolutional neural network for 3d seismic fault segmentation. Geophysics 84(3):IM35–IM45. https://doi.org/10.1190/geo2018-0646.1
**ong W, Ji X, Ma Y, Wang Y, AlBinHassan NM, Ali MN, Luo Y (2018) Seismic fault detection with convolutional neural network. Geophysics 83(5):O97–O103. https://doi.org/10.1190/geo2017-0666.1
Zhang Z, Wang S, Yang X, Jiang F, Shen J, Li X (2004) Evidence of a geological event and environmental change in the catchment area of the yellow river at 0.15 ma. Quat Int 117(1):35–40
Zhong J, McGuinness DL, Antonellini M, Aydin A (2005) Ontology for structural geology. In: AGU fall meeting abstracts
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by: H. Babaie
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhan, X., Lu, C. & Hu, G. Event sequence interpretation of structural geological models: a knowledge-based approach. Earth Sci Inform 14, 99–118 (2021). https://doi.org/10.1007/s12145-020-00558-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-020-00558-2