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

Chakraborty, Megha; Li, Wei; Faber, Johannes; Rümpker, Georg; Stoecker, Horst; Srivastava, Nishtha

doi:10.48550/arxiv.2112.07551

Cited by 5 publications

(5 citation statements)

References 41 publications

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“…Comparing the performance of the CREIME model with our observations in Chakraborty et al (2021), we find that providing data labels in the form of a series and including the first P-arrival information is beneficial for the model, in estimating the earthquake magnitude. We also test CREIME on data windows which have less than 1 s or more than 2 s of P wave data, and hence does not comply with the design of our trained data and find the error in magnitude estimation to be much higher (refer to Appendix D).…”

Section: Discussionmentioning

confidence: 91%

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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Section: Discussionmentioning

confidence: 91%

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

Chakraborty

Fenner

et al. 2022

JGR Solid Earth

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“…This means that our model can be applied to the incoming seismogram in real time for rapid characterisation. Comparing the performance of the CREIME model with our observations in [66], we find that providing data labels in the form of a series and including the first P-arrival information is beneficial for the model, in estimating the earthquake magnitude.…”

Section: Effect Of Using Different Types Of Ground Motion Data As Inputmentioning

confidence: 91%

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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“…Therefore, the recent advances in the field of computer vision have great potential in seismological applications. Recently, deep learning has been widely used to detect earthquakes or identify seismic phases Ross et al (2018); Chen (2020, 2021); Chakraborty, Li, Faber, Ruempker, Stoecker and Srivastava (2021). For example, in Ross et al (2018), a convolutional neural network was trained on the huge amount of labeled seismic data to classify seismic body wave phase.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Small Magnitude Earthquake Detection and Seismic Phase Classification

Li¹,

Yu²,

Zhou³

et al. 2022

Preprint

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Reliable earthquake detection and seismic phase classification is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data is witnessed. This makes the handling of the seismic data rather daunting based on traditional approaches and therefore fuels the need for a more robust and reliable method. In this study, we investigate two deep learningbased models, termed 1D Residual Neural Network (ResNet) and multi-branch ResNet, for tackling the problem of seismic signal detection and phase identification, especially the later can be used in the case where multiple classes is organized in the hierarchical format. These methods are trained and tested on the dataset of the Southern California Seismic Network. Results demonstrate that the proposed methods can achieve robust performance for the detection of seismic signals, and the identification of seismic phases, even when the seismic events are of small magnitude and are masked by noise. Compared with previously proposed deep learning methods, the introduced frameworks achieve 4% improvement in earthquake monitoring, and a slight enhancement in seismic phase classification.

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

Cited by 5 publications

References 41 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

Deep Learning-based Small Magnitude Earthquake Detection and Seismic Phase Classification

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