Deep learning algorithms are immensely data-hungry and rely on large amounts of labeled data to achieve good performance. However the earth is intrinsically unlabeled and we are often confronted to fuzzy boundaries, uncertain labels, and absence of ground truth. Moreover, deep learning models do not always generalize well to conditions that are different from the ones encountered during training. In this context, it can be difficult to leverage deep learning algorithms for seismic problems. Herein we introduce strategies for overcoming these limitations, using synthetic data generation and transfer learning to jump-start the training of neural networks. We present this methodology through two case studies: earthquake detection using the Northern California Seismic Network (NCSN); and targeted noise filtering for ambient seismic noise recorded by a fiber optic array underneath Stanford campus.

Presentation Date: Tuesday, October 16, 2018

Start Time: 9:20:00 AM

Location: Poster Station 3

Presentation Type: Poster

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