Using the scattering transform to predict stratigraphic units from well logs

Much of geophysical interpretation relies on trained pattern recognition of signals and images, a workflow that can be modeled by supervised machine learning. A challenge of supervised learning is determining a physically meaningful feature set that can successfully classify the data. Defined by a network of cascading wavelets, the scattering transform provides a non-linear multiscale analysis that has deep connections to the fractal statistics of the signal. Interestingly, the scattering transform takes the form of a pre-trained convolutional neural network. This paper uses the scattering transform to extract features from well logs in order to train a classifier that can predict stratigraphic units. The methodology is tested on interpreted well logs from Trenton-Black River project and initial results are presented.