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 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.
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Elsevier
2022-03-01
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| Subjects: | 3D geological modelling, Cross-sections, Decision making algorithm, Open-source, Python, |
| Online Access: | http://hdl.handle.net/10261/259489 http://dx.doi.org/10.13039/501100004837 https://api.elsevier.com/content/abstract/scopus_id/85123320863 |
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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. |
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