Earthquake magnitude estimation using the τc and Pd method for earthquake early warning systems

Jin, Xing; Zhang, Hongcai; Li, Jun; Wei, Yongxiang; Ma, Qiang

doi:10.1007/s11589-013-0005-4

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Even for the same magnitude scale, values reported by different agencies may differ by up to 0.5 units (Mousavi & Beroza, 2020). Traditionally, frequency‐domain parameters such as predominant period

{\tau }_{p}^{\mathrm{max}\,}

(Allen & Kanamori, 2003; Nakamura, 1988), effective average period τ c (Jin et al., 2013; Kanamori, 2005; Kuyuk & Allen, 2013) and amplitude domain parameters such as peak displacement ( P d ) (Jin et al., 2013; Kuyuk & Allen, 2013; Wu & Zhao, 2006) calculated from the initial 1–3 s of P waves have been shown to provide reliable estimates of (body wave) magnitudes through empirical relations. Such methods have been applied to Earthquake Early Warning (EEW) systems in Japan, California, Taiwan, etc.…”

Section: Introductionmentioning

confidence: 99%

“…, effective average period τ c (Jin et al, 2013;Kanamori, 2005;Kuyuk & Allen, 2013) and amplitude domain parameters such as peak displacement (P d ) (Jin et al, 2013;Kuyuk & Allen, 2013;Wu & Zhao, 2006) calculated from the initial 1-3 s of P waves have been shown to provide reliable estimates of (body wave) magnitudes through empirical relations. Such methods have been applied to Earthquake Early Warning (EEW) systems in Japan, California, Taiwan, etc.…”

mentioning

confidence: 99%

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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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{\tau }_{p}^{\mathrm{max}\,}

Section: Introductionmentioning

confidence: 99%

mentioning

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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“…According to its original definition [1] the magnitude of an earthquake is the logarithm of the maximum trace amplitude expressed in microns measured by a standard short-period torsion seismometer at an epicentral distance of 100km. It is one of "the most important and also the most difficult parameters" involved in real-time seismology [2] particularly since most magnitude scales such as local magnitude (m L ), body wave magnitude (m B ), surface wave magnitude (m S ) are empirical and saturate at different magnitude ranges [3,4]. This, coupled with the complexity of the nature of the geophysical processes affecting earthquakes, makes it very difficult to have a single reliable measure for the size of an earthquake [5].…”

Section: Introductionmentioning

confidence: 99%

“…Even for the same magnitude scale, values reported by different agencies may differ by up to 0.5 units [10]. Traditionally, frequency-domain parameters such as predominant period τ max p [11,12], effective average period τ c [2,13,14] and amplitude domain parameters such as peak displacement (P d ) [2,14,15] calculated from the initial 1-3 seconds of P-waves have been shown to provide reliable estimates of (body wave) magnitudes through empirical relations. Such methods have been applied to Earthquake Early Warning (EEW) systems in Japan, California, Taiwan etc.…”

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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“…Owing to above-mentioned reasons and the empirical nature of majority of the magnitude scales, it is one of the most difficult parameters to estimate [5,6]. Some of the classical approaches to obtain first estimates of earthquake magnitude have used empirical relations for parameters such as predominant period τ max p [7,8], effective average period τ c [9,10] in the frequency domain and parameters such as peak displacement (P d ) [10,11] in the amplitude domain calculated from the initial 1-3 seconds of P-waves. These relations form the basis of existing Earthquake Early Warning (EEW) systems in Japan, California, Taiwan etc.…”

Section: Introductionmentioning

confidence: 99%

A study on the effect of input data length on deep learning based magnitude classifier

Chakraborty¹,

Li²,

Faber³

et al. 2021

Preprint

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The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep learning based magnitude classifier and, further we investigate the effect of using five different durations of seismic waveform data after first P-wave arrival-1s, 3s, 10s, 20s and 30s. This is accomplished by testing the performance of the proposed model that combines Convolution and Bidirectional Long-Short Term Memory units to classify waveforms based on their magnitude into three classes-"noise", "low-magnitude events" and "high-magnitude events". Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as high-magnitude. We show that the variation in the results produced by changing the length of the data, is no more than the inherent randomness in the trained models, due to their initialisation.

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Earthquake magnitude estimation using the τc and Pd method for earthquake early warning systems

Cited by 11 publications

References 18 publications

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

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

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

A study on the effect of input data length on deep learning based magnitude classifier

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