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

arXiv: https://arxiv.org/abs/1810.01965 · HTML: https://mlsanity.com/paper/1810.01965/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction](https://mlsanity.com/paper/2309.02320/) (2023)
- [SeisLM: a Foundation Model for Seismic Waveforms](https://mlsanity.com/paper/2410.15765/) (2024)
- [Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability](https://mlsanity.com/paper/2201.08671/) (2022)
- [SeisT: A foundational deep learning model for earthquake monitoring tasks](https://mlsanity.com/paper/2310.01037/) (2023)
- [Transfer learning for self-supervised, blind-spot seismic denoising](https://mlsanity.com/paper/2209.12210/) (2022)
- [SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model](https://mlsanity.com/paper/2502.19960/) (2025)
- [Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)](https://mlsanity.com/paper/2306.13918/) (2023)
- [VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting](https://mlsanity.com/paper/2405.04252/) (2024)
- [FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector Machines](https://mlsanity.com/paper/2204.05112/) (2022)
- [PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation](https://mlsanity.com/paper/2411.12636/) (2024)
- [HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity](https://mlsanity.com/paper/2507.10850/) (2025)
- [Seismic event classification with a lightweight Fourier Neural Operator model](https://mlsanity.com/paper/2512.07425/) (2025)
- [GreenPhase: A Green Learning Approach for Earthquake Phase Picking](https://mlsanity.com/paper/2603.03344/) (2026)

## Abstract (arXiv)

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. The network 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 a single station. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California and tested it with an F-score of 99.95. 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. The model is applied 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 700 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region. The performance of the model is compared with STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise in lowering the detection threshold while minimizing false positive detection rates.