Using the scattering transform to predict stratigraphic units from well logs

TitleUsing the scattering transform to predict stratigraphic units from well logs
Publication TypeJournal Article
Year of Publication2016
AuthorsBen B. Bougher, Felix J. Herrmann
JournalCSEG Recorder
Volume41
Number1
Page22-25
Month01
Keywordsmachine learning, scattering transform, well logs
Abstract

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.

Notes

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Citation Keybougher2015CSEGust