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Found 4 papers, 1 papers with code
Self-Supervised Annotation of Seismic Images using Latent Space Factorization
Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation.
Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection
Images from the selected clusters are used to train the encoder-decoder network.
Explaining Deep Models through Forgettable Learning Dynamics
Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model by increasing the variety of difficult regions.
Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation
In recent years, deep neural networks have significantly impacted the seismic interpretation process.
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