Mauricio Araya-Polo

Mauricio Araya-Polo, Stuart Farris and Manuel Florez Standford University and Shell International Exploration & Production Inc.

Combining Unsupervised and Supervised Deep Learning approaches for Seismic Tomography

Signals from inner earth, seismic waveforms, are heavily manipulated before human interpreters have a chance of figuring the subsurface structures. That manipulation adds modeling biases and it is limited by methodological shortcomings. Alternatively, using waveforms directly is becoming possible thanks to current Deep Learning (DL) advances such as (Araya-Polo et al., 2017 and 2018; Lin et al., 2017). Further extending that work, we present a DL approach that takes realistic raw seismic waveforms as inputs and produces subsurface velocity models as output. When insufficient data is used for training, DL algorithms tend to either over-fit or fail completely. Gathering large amounts of labeled and standardized seismic data sets is not straight forward. We address this shortage of quality data by building a Generative Adversarial Network (GAN) to augment our original training data set, which then is used by the DL seismic tomography as input.