  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 original CNN. More
importantly, it replaces the pixel nature of conventional CNN filters with a
constrained function form that depends on 5 parameters that are more in line
with seismic signatures. Further, we constrain the angle and wavelength of the
Gabor kernels to certain ranges in the training process based on what we expect
in the seismic images. The experiments on the Netherland F3 dataset show the
effectiveness of the proposed method in a seismic facies classification task,
especially when applied to testing data with lower signal-to-noise ratios.
Besides, we also test this modified CNN using different kernels on
salt$\&$pepper and speckle noise. The results show that we obtain the best
generalization and robustness of the CNN to noise when Gabor kernels are used
in the first layer.
