GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds

The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Geoscientific Model Development Ročník 18; číslo 4; s. 939 - 960
Hlavní autoři: Oakley, David, Loiselet, Christelle, Coowar, Thierry, Labbe, Vincent, Callot, Jean-Paul
Médium: Journal Article
Jazyk:angličtina
Vydáno: Katlenburg-Lindau Copernicus GmbH 19.02.2025
European Geosciences Union
Copernicus Publications
Témata:
Algorithms
Analysis
Artificial neural networks
Automation
Clustering
Data analysis
Data science
Datasets
Earth Sciences
Fault lines
Folds
Geological data
Geological mapping
Geological maps
Geologists
Geology
Identification
Learning algorithms
Machine learning
Neural networks
Sciences of the Universe
Statistical analysis
Statistical methods
Structures
Supervised learning
Topography
Unsupervised learning
ISSN:1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X, 1991-959X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster and less subjective interpretations of geological data than are possible from a human interpreter, but translating the understanding of a trained geologist to an algorithm is not straightforward. In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. We then use the HDBSCAN clustering algorithm to cluster these possible fold identifications into a smaller number of distinct folds. This clustering algorithm is chosen because it does not require the number of clusters to be known a priori. For the supervised learning case, we use synthetic models of folds to train a convolutional neural network to identify folds using map and topographic data. We test both methods on synthetic and real datasets, where they both prove capable of identifying folds. We also find that distinguishing folds from similar map patterns produced by topography is a major issue that must be accounted for with both methods. The unsupervised method has advantages, including the explainability of its results, and provides clearly better results in one of the two real-world test datasets, while the supervised learning method is more fully automated and likely more easily extensible to other structures. Both methods demonstrate the ability of machine learning to interpret folds on geological maps and have potential for further development targeting a wider range of structures and datasets.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
1991-959X
DOI:10.5194/gmd-18-939-2025