Figure 4 - uploaded by Tasman Gillfeather-Clark
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-Q-error map showing areas that vary notably from the BMU they were mapped as. The area outlined in red is a single geological unit (Silver King Frm.)
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... the process that gave rise to a given BMU. For example it's possible to identify drainage systems clearly. The SOM process also produces an error map referred to as Quantization error (Q-error), which measures how well the unit fits into the BMU it has been matched with, higher values corresponding to greater variance. The grayscale image in Fig. 4 shows the quantization error with brighter spots having higher Q-error ...
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Citations
... Previous work on the automatic detection of geological structures on maps is limited. A greater body of work exists on the related problem of automatic classification of lithology from remote sensing and airborne geophysical data, as in de Carvalho Carneiro et al. (2012), Reading (2013, 2014), Kuhn et al. (2018), Gillfeather-Clark and Smith (2018), and Bressan et al. (2020), and some work on fault and lineament detection from such datasets (e.g., Vasuki et al., 2014;Middleton et al., 2015;Aghaee et al., 2021). Another problem that has received greater attention is automatic interpretation of seismic reflection data, including the identification of faults (e.g., Wu et al., 2019Wu et al., , 2020Cunha et al., 2020;An et al., 2021An et al., , 2023Gao et al., 2022;Wang et al., 2023) and salt structures (e.g., Shi et al., 2019;Muller et al., 2022). ...
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.
The increasing availability of large geological datasets together with 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, the number of which is not 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.
