An automatic geological 3D cross-section generator: Geopropy, an open-source library
Geological modelling is an essential aspect of underground investigations, with cross-sections being one of the key aspects. This modelling can be done by experienced geologists or using mathematical methods. We present Geopropy, an open-source decision-making algorithm implemented in Python, that g...
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| Published in | Environmental modelling & software : with environment data news Vol. 149; p. 105309 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ltd
01.03.2022
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1364-8152 1873-6726 1873-6726 |
| DOI | 10.1016/j.envsoft.2022.105309 |
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| Summary: | Geological modelling is an essential aspect of underground investigations, with cross-sections being one of the key aspects. This modelling can be done by experienced geologists or using mathematical methods. We present Geopropy, an open-source decision-making algorithm implemented in Python, that generates 3D cross-sections (the boreholes do not have to be aligned). It performs as an intelligent agent that simulates the steps taken by the geologist in the process of creating the cross-section, coupled with data-driven decisions. The algorithm detects zones with more than one possible outcome and, based on the level of complexity (or user preference), proceeds to automatic, semiautomatic or manual stages. Geopropy could be the basis of a new, simpler, more comprehensible way of looking at geological models in industry and academia while at the same time creating the potential for using novel machine learning algorithms in geological modelling.
•Geopropy is an open source Python library to create 3D geological cross section.•Geopropy is an intelligent agent based on a knowledge – data driven approach.•The algorithm performs by emulating geologist decision making steps.•Geopropy would generate results explicitly. It would not replace implicit methods. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 1364-8152 1873-6726 1873-6726 |
| DOI: | 10.1016/j.envsoft.2022.105309 |