Learnable Gabor kernels in convolutional neural networks for seismic interpretation tasks

Understand

The use of convolutional neural networks (CNNs) in seismic interpretation tasks, like facies classification, has garnered a lot of attention for its high accuracy.

• However, its drawback is usually poor generalization when trained with limited training data pairs, especially for noisy data.
• Seismic images are dominated by diverse wavelet textures corresponding to seismic facies with various petrophysical parameters, which can be suitably represented by Gabor functions.
• Inspired by this fact, we propose using learnable Gabor convolutional kernels in the first layer of a CNN network to improve its generalization.