CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection

Each year, more than 50 terabytes of seismic data are archived at the Incorporated Research Institutions for Seismology (IRIS) alone. The massive amount of data highlights the need for more efficient and powerful tools for data processing and analyses. The main challenge is the efficient extraction of as much useful information as possible from these large datasets. This is where rapidly evolving machine learning (ML) approaches have the potential to play a key role (Zhu and Beroza 2018; Li et al, 2018; Ross et al, 2018b; Chen 2018). 1 One of the first stages that observational seismologists need to meet this challenge is in the processing of continuous data to detect earthquake signals. Among a large variety of detection methods developed in past few decades, STA/LTA (Allen, 1978) and template matching (Gibbons and Ringdal 2006; Shelly et al. 2007; Ross et al, 2017; Li et al, 2018) are the most commonly used algorithms. While STA/LTA is generalized and efficient, its sensitivity to timevarying background noise and lack of sensitivity to small events, false positives, and events recorded shortly after each other make it less than optimal for robust and sensitive detection. Although the high sensitivity of cross-correlation improves the detection threshold of template matching, the requirement of prior knowledge of templates and multiple cross-correlation procedures make it less general and inefficient for real-time processing of large seismic data volumes. Although more advanced algorithms such as Fingerprint And Similarity Thresholding (FAST) (Yoon et al, 2015) can improve the efficiency of the similarity search, the outputs are in that case limited to repeated events. Shallow Neural Networks (NN) are among the first ML methods used for the earthquake signal detection (e.g.