CREIME—A Convolutional Recurrent Model for Earthquake Identification and Magnitude Estimation

Abstract

The detection and rapid characterization of earthquake parameters such as magnitude are important in real‐time seismological applications such as Earthquake Monitoring and Earthquake Early Warning (EEW). Traditional methods, aside from requiring extensive human involvement can be sensitive to signal‐to‐noise ratio leading to false/missed alarms depending on the threshold. We here propose a multitasking deep learning model—the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (a) detects the earthquake signal from background seismic noise, (b) determines the first P wave arrival time, and (c) estimates the magnitude using the raw three‐component waveforms from a single station as model input. Considering, that speed is essential in EEW, we use up to 2 s of P wave information which, to the best of our knowledge, is a significantly smaller data window compared to the previous studies. To examine the robustness of CREIME, we test it on two independent data sets and find that it achieves an average accuracy of 98% for event versus noise discrimination and can estimate first P‐arrival time and local magnitude with average root mean squared errors of 0.13 s and 0.65 units, respectively. We compare CREIME with traditional methods such as short‐term‐average/long‐term‐average (STA/LTA) and show that CREIME has superior performance, for example, the accuracy for signal and noise discrimination is higher by 4.5% and 11.5%, respectively, for the two data sets. We also compare the architecture of CREIME with the architectures of other baseline models, trained on the same data, and show that CREIME outperforms the baseline models.

Recent advances in earthquake seismology using machine learning

“…2 Typical pipeline for earthquake catalog development earthquakes and noises) or more categories. This issue has been addressed by various ML algorithms, including support vector machine, decision tree, logistic regression, and neural network, including DL (Reynen and Audet 2017;Meier et al 2019;Tang et al 2020;Albert and Linville 2020;Kim et al 2021b;Chakraborty et al 2022;Murti et al 2022). The target of such classifiers can be expanded to various phenomena by preparing appropriate training data.…”