Convolutional neural networks (CNNs) is a type of supervised learning technique that can be directly applied to amplitude data for seismic data classification. The high flexibility in CNN architecture enables researchers to design different models for specific problems. In this study, I introduce an encoder-decoder CNN model for seismic facies classification, which classifies all samples in a seismic line simultaneously and provides superior seismic facies quality comparing to the traditional patch-based CNN methods. I compare the encoder-decoder model with a traditional patch-based model to conclude the usability of both CNN architectures.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 204B (Anaheim Convention Center)
Presentation Type: Oral
