Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network

Zhu, Jingbao; Li, Shanyou; Song, Jindong; Wang, Yuan

doi:10.3389/feart.2021.653226

Cited by 31 publications

(14 citation statements)

References 38 publications

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“…Meanwhile, some researchers have used neural networks to establish magnitude estimation models [17][18][19]. [20] used a deep convolutional neural network (CNN) to establish a magnitude estimation model (DCNN-M model) based on the strong-motion data recorded by the Japan Kyoshin Network (K-NET) stations.…”

Section: Introductionmentioning

confidence: 99%

Rapid earthquake magnitude estimation combining a neural network and transfer learning in China: Application to the 2022 Lushan M6.1 earthquake

Zhu

et al. 2023

Front. Phys.

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Introduction: China is one of the most seismically active countries in the world. It is an important task for a Chinese earthquake early warning system to quickly obtain robust magnitude estimation. However, within the first few seconds after P-wave arrival, there is considerable scatter in magnitude estimation for traditional methods based on a single early warning parameter.Methods: To explore the feasibility of using a convolutional neural network for magnitude estimation in China, establish a magnitude estimation model suitable for China and provide more robust magnitude estimation based on strong-motion data from China, we propose a new approach combining a convolutional neural network and transfer learning (TL) to construct a magnitude estimation model (TLDCNN-M) in this study.Results and Discussion: Our results show that for the same test dataset, in terms of the mean absolute error and standard deviation of magnitude estimation errors, the TLDCNN-M model has better performance than traditional methods and convolutional neural network models without using TL. Meanwhile, we apply the method to the 2022 Lushan M6.1 earthquake occurred in Sichuan province, China. At 3 s after the earliest P phase, the magnitude estimation error is less than 0.5. With the increase in time after the earliest P phase, the magnitude estimation is close to the catalog magnitude; at 10 s after the earliest P phase, the magnitude estimation error is less than 0.2.

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Section: Introductionmentioning

confidence: 99%

Rapid earthquake magnitude estimation combining a neural network and transfer learning in China: Application to the 2022 Lushan M6.1 earthquake

Zhu

et al. 2023

Front. Phys.

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“…Unlike Zhu et al. (2021) where a set of 12 features extracted from 3 s of data is used to perform magnitude estimation and Ochoa et al. (2018) where features extracted from 5 s of P wave data are fed to a Support Vector Machine Regression algorithm to estimate magnitude, CREIME is end‐to‐end using a combination of Convolutional and Recurrent neural network to extract features directly from the raw waveform.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present a novel approach to achieve multi-tasking Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME), which can simultaneously perform earthquake identification, local magnitude estimation, and first P-wave arrival time regression solely based on 1-2 s P-wave recording. Unlike Zhu et al (2021) where a set of 12 features extracted from 3 s of data is used to perform magnitude estimation and Ochoa et al (2018) where features extracted from 5 s of P wave data are fed to a Support Vector Machine Regression algorithm to estimate magnitude, CREIME is end-to-end using a combination of Convolutional and Recurrent neural network to extract features directly from the raw waveform. Unlike traditional approaches such as Nakamura (1988), Allen and Kanamori (2003), and Wu and Zhao (2006) no a-priori information on hypocentral distance is required for magnitude estimation.…”

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confidence: 99%

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

Chakraborty

Fenner

et al. 2022

JGR Solid Earth

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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.

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“…In this paper we present a novel approach to achieve multi-tasking Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME), which can simultaneously perform earthquake identification, local magnitude estimation and first P-arrival time regression solely based on 1-2 seconds P-wave recording. Unlike [47] which uses a set of twelve features extracted from 3 seconds of data to perform magnitude estimation, CREIME is end-to-end using a combination of Convolutional and Recurrent neural network to extract features directly from the raw waveform. The motivation for using such a small duration of P-wave data lies in its potentially easier utility in applications such as rapid earthquake characterisation for EEW systems ( [16] [48] and references therein).…”

Section: Introductionmentioning

confidence: 99%

CREIME: A Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation

Chakraborty,

Fenner,

et al. 2022

Preprint

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The detection and rapid characterisation of earthquake parameters such as magnitude are of prime importance in seismology, particularly in applications such as Earthquake Early Warning (EEW). Traditionally, algorithms such as short-term average/long-term average (STA/LTA) are used for event detection, while frequency or amplitude domain parameters calculated from 1-3 seconds of first P-arrival data are sometimes used to provide a first estimate of (body-wave) magnitude. Owing to extensive involvement of human experts in parameter determination, these approaches are often found to be insufficient. Moreover, these methods are sensitive to the signal-to-noise ratio and may often lead to false or missed alarms depending on the choice of parameters. We, therefore, propose a multi-tasking deep learning model -the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (i) detects the first earthquake signal, from background seismic noise, (ii) determines first P-arrival time as well as (iii) estimates the magnitude using the raw 3-component waveform data from a single station as model input. Considering, that speed is of the essence in EEW, we use up to two seconds of P-wave information which, to the best of our knowledge, is a significantly smaller data window (5 second window with up to 2 seconds

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Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network

Cited by 31 publications

References 38 publications

Rapid earthquake magnitude estimation combining a neural network and transfer learning in China: Application to the 2022 Lushan M6.1 earthquake

Rapid earthquake magnitude estimation combining a neural network and transfer learning in China: Application to the 2022 Lushan M6.1 earthquake

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

CREIME: A Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation

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