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

By Mostafa Mousavi, Weiqiang Zhu, Y. Sheng, Gregory C. Beroza. Scientific Reports, 2018

316 citations recorded by the source corpus.

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. We also apply the model to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 800 microearthquakes as small as −1.3 ML induced during hydraulic fracturing far away than the training region. We compare the performance of the model with the STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise for lowering the detection threshold while minimizing false positive detection rates.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/364f6add1d7b23eb8ca809c7663cac01e294d059

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Citations in the corpus

Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “To detect small and weak earthquake signals robust to strong noise and non-earthquake signals, a residual network with convolutional and recurrent units is developed (Mousavi, Zhu, Sheng, et al., 2019).”
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI (2019, methodology): “…(detrending, band-pass ﬁltering, and resampling), we performed post-processing consisting of de-signaling followed by double checking using the generalized earthquake detector, CRED [6] to ensure that the noise traces do not contain earthquake signal (even hidden within the background noise).”
Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking (2020, methodology): “Building upon our previous work 7 , we introduce a multi-task network of recurrent and convolutional layers that incorporates attention mechanism as well.”
Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018, methodology): “Deep learning has been shown to be a powerful tool for learning the characteristics of seismic data [47, 48, 49, 50, 51, 52, 53, 54].”