Revealing and interpreting patterns from continuous seismic data with unsupervised learning

Exploring large datasets of continuous seismic data is a challenging task. When targeting signals of interest with a good a priori knowledge on the signal's properties, it is possible to design a dedicated processing pipeline (earthquake detection, noise reduction, etc.). Many other sources can sign up in the data, with characteristics that differ from the targetted ones (changes in noise frequency, modulating signals, etc.). In this case, it is difficult to design a processing pipeline that will be robust to all the possible sources. In this work, we propose to use unsupervised learning to explore the data and reveal patterns in an interpretable way. We extract relevant features of continuous seismic data with a deep scattering network —a deep convolutional neural network with interpretable feature maps— and experiment various classical machine learning tools (clustering, dimensionality reduction, etc.) to reveal and interpret patterns in the data. We apply this method to various cases including to a decade of continuous data in the region of Guerrero, Mexico, and interpret the results in terms of seismicity and external datasets.