Information Theory Considerations In Patch-Based Training Of Deep Neural Networks On Seismic Time-Series

Recent advances in machine learning relies on convolutional deep neural networks. These are often trained on cropped image patches. Pertaining to non-stationary seismic signals this may introduce low frequency noise and non-generalizability.