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

Abstract

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.

A Machine‐Learning Approach for Earthquake Magnitude Estimation

“…Hence, they are commonly used for modeling of sequential data similar to earthquake signals. A detailed description of LSTMs and their applications to earthquake data can be found in (Mousavi, Zhu, Sheng, & Beroza, ). The advantage of using LSTM units for magnitude estimation lies in their insensitivity to unnormalized inputs due to their gated mechanism consisting of Tanh and Sigmoid activation functions Mousavi, Zhu, Sheng, and Beroza ( ) .…”

“…Our goal in this study was to develop a method for a fast and reliable estimation of earthquake magnitude directly from raw seismograms recorded on a single station. This is part of a larger project aiming to develop a full deep‐learning pipeline (Zhu et al (); Mousavi et al (); Zhu and Beroza (); Mousavi et al, , Mousavi et al, ]) for earthquake signal processing and monitoring.…”

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

“…Deep learning [45,46] is a powerful machine learning technique that can learn extremely complex functions through neural networks. 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].…”

A Novel Method of Seismic Signal Detection Using Waveform Features

“…The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.Over the past decades, the traditional STA/LTA method [5][6][7] has been used to pick up seismic signals. It reflects the instantaneous change in signal energy by calculating the ratio of the short-term and long-term average of data.…”

“…It has been widely used in many real-time detection systems, such as Earthquake Early Warning (EEW), seismic data processing software Seiscomp3, CTBTO International Data Centre waveform data processing software IDCR3, Geotool, and so on. The limitation of STA/LTA is that it is sensitive to the time-varying background noise [7], which increases the probability of small-signal misdetection. So it is not suitable for lower SNR signals [8].…”