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Abstract

Summary

Geologic interpretation of large seismic stacked or migrated seismic images can be a time-consuming task for seismic interpreters. Neural network based semantic segmentation provides fast and automatic interpretations, provided a sufficient number of example interpretations are available. Networks that map from image-to-image emerged recently as powerful tools for automatic segmentation, but standard implementations require fully interpreted examples. Generating training labels for large images manually is time consuming. We introduce a partial loss-function and labeling strategies such that networks can learn from partially interpreted seismic images. This strategy requires only a small number of annotated pixels per seismic image. Tests on seismic images and interpretation information from the Sea of Ireland show that we obtain high-quality predicted interpretations from a small number of large seismic images. The combination of a partial-loss function, a multi-resolution network that explicitly takes small and large-scale geological features into account, and new labeling strategies make neural networks a more practical tool for automatic seismic interpretation.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201901513
2019-06-03
2024-04-25

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901513
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Automatic Classification of Geologic Units in Seismic Images Using Partially Interpreted Examples
Conference Proceedings 2019, 1 (2019); https://doi.org/10.3997/2214-4609.201901513
/content/papers/10.3997/2214-4609.201901513
/content/papers/10.3997/2214-4609.201901513
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