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Abstract

Summary

We present an approach to detect and segment salt bodies in new unlabelled datasets based on 3D attributes and a classifier. The classifier is trained on one labelled dataset and used to classify salt bodies on new unlabelled datasets.

Through a forward attribute selection algorithm and manual inspection of attribute images and classified images, we have evaluated a wide set of attributes and classifiers with the aim to be able to detect salt in unlabelled datasets. The simple nearest mean classifier, together with a set of three attributes; gradient tensor coherency, grey level co-occurrence matrix energy and kurtosis variability, were selected.

Finally, the method is tested on two datasets containing salt bodies. We trained the classifier on slices from one dataset and classified the salt structure on the other dataset. The resulting classification gives a 3D model of the salt body and had an estimated error rate of 9.0 %.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201600880
2016-05-30
2024-04-25

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References

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3D Attributes and Classification of Salt Bodies on Unlabelled Datasets
Conference Proceedings 2016, 1 (2016); https://doi.org/10.3997/2214-4609.201600880
/content/papers/10.3997/2214-4609.201600880
/content/papers/10.3997/2214-4609.201600880
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