Learnable Gabor Kernels in Convolutional Neural Networks for Seismic Interpretation Tasks

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. The modified network combines the interpretability features of Gabor filters and the reliable learning ability of the original CNN. It replaces the pixel nature of conventional CNN filters with a constrained function form that depends on five parameters that are more in line with seismic signatures. This allows us, in training, to constrain the angle and wavelength of the Gabor kernels to specific ranges to help enhance the seismic features and reduce noise. We, also, test this modified CNN using various kernels on salt&pepper and speckle noise. The experiments on the Netherland F3 dataset show that we obtain the best generalization and robustness of the CNN to noise when Gabor kernels are used in the first layer.