Mathieu Chambefort

Signal and Noise Detection using Recurrent Autoencoders on Seismic Marine Data

Mathieu Chambefort, Nicolas Salaun, Emilie Chautru, Stephan Clémençon and Guillaume Poulain

MINES ParisTech - PSL University Centre de Géosciences, CGG, and Telecom ParisTech, LTCI, Université Paris Saclay

In the Big Data era, geophysics are faced with new industrial contrains like processing more and more seismic data (more than 106 shot points per marine seismic survey [Belz and Dolymnyj, 2018]) in a more timely, reliable and efficient manner (improving signal enhancement, [Martin et al., 2015]). To deal with these challenges, we develop a deep learning approach based on recurrent LSTM ([Wong and Luo, 2018]) to the processing of seismic time series; this separates the signal from the noise based on the encoded information. This contribution provides empirical evidence that the representation provided by the internal layers of the deployed autoencoder encodes the original information well. More precisely, focus is here on the linear noise that possibly blurs marine seismic data ([Elboth et al., 2009]). The data under study is composed of massive synthetic shot points. The goal pursued is to design an autoencoder capable of detecting the possible occurrence of linear noise in the data. Next, the encoded information is classified. The obtained results are compared with those of a traditional technique, which essentially consists in applying directly a K-NN algorithm on the envelope of the analytical signal, as if all the dataset came from the same area.