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- W3113078119 abstract "The interpretation of seismic data is vital for characterizing sediments' shape in areas of geological study. In seismic interpretation, deep learning becomes useful for reducing the dependence on handcrafted facies segmentation geometry and the time required to study geological areas. This work presents a Deep Neural Network for Facies Segmentation (DNFS) to obtain state-of-the-art results for seismic facies segmentation. DNFS is trained using a combination of cross-entropy and Jaccard loss functions. Our results show that DNFS obtains highly detailed predictions for seismic facies segmentation using fewer parameters than StNet and U-Net." @default.
- W3113078119 created "2020-12-21" @default.
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- W3113078119 date "2020-11-15" @default.
- W3113078119 modified "2023-09-26" @default.
- W3113078119 title "Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks." @default.
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