Earthquake detection - the identification of weak earthquake signals in continuous waveform data recorded by sensors in a seismic network - is a fundamental task in seismology. In this talk, I will describe the data science challenges associated with earthquake detection in massive seismic data sets. I will discuss how new algorithmic advances in ML and data mining are helping to advance the state-of-the-art in seismic signal analysis for earthquake monitoring.

One example is Fingerprint and Similarity Thresholding (FAST), a novel method for large-scale earthquake detection inspired by audio recognition technology (Yoon et al, 2015). FAST uses locality-sensitive hashing, a technique for efficiently identifying similar items in large data sets, to detect similar waveforms (candidate earthquakes) in continuous seismic data. Another example are deep neural networks, which have emerged as particularly powerful tools for classifying waveforms and identifying seismic phases. I will present an interpretable one-dimensional convolutional neural network (CNN) model for earthquake phase detection. This architecture modifies the GPD model of Ross et al. (2018), with parallel branches for prediction and signal reconstruction. The new prediction model achieves similar prediction accuracy, but with the added benefit of greater interpretability.

Finally, I will highlight how research in the emerging discipline of scientific machine learning will play a critical role in driving discovery in the Earth and physical sciences.