Tan Nguyen

Tremor Generative Adversarial Networks: A Deep Generative Model Approach for Geophysical Signal Generation

Inspired by the recent success of the Generative Adversarial Networks (GANs) for images, we propose to employ GANs to generate realistic geophysical signals from labeled data. Signals, here, include seismicity, sedimentary sequences, geological models etc. We present a preliminary application of a GAN to generate tremors: Synthetic tremors generated by one of our GANs, trained with data collected in Mexico. Studying the trained GANs facilitates our understanding of the data generating process. These GANs can also be inverted into inference algorithms that capture intrinsic properties of the generating process. GAN-generated tremors can be used as templates to help detect additional tremors and potentially result in better generalization to new sensor signals.