Deep Learning-Based, Real-Time, False-Pick Filter for an Onsite Earthquake Early Warning (EEW) System

Seo, JeongBeom; Lee, JinKoo; Lee, Woodong; Lee, SeokTae; Lee, HoJun; Jeon, Inchan; Park, NamRyoul

doi:10.5000/eesk.2021.25.2.071

Cited by 3 publications

(6 citation statements)

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“…2.1 Korean on-site earthquake early warning (Seo et al, 2021b). To avoid triggering an alarm in response to a non-earthquake signal or weak seismic intensity, the system includes four filtering steps.…”

Section: Methodsmentioning

confidence: 99%

“…To develop MLF (Lee, 2020;Seo et al, 2021a), the following steps were systematically performed: 1) 3-component waveforms of 4 s were collected based on the detected signal from Korean Peninsula. The time at which the Frontiers in Earth Science frontiersin.org initial P-wave was detected was called the picking time.…”

Section: Design Of Machine Learning Filtermentioning

confidence: 99%

“…To verify the performance of the model, the dataset was divided into training sets (1,826,357) and demonstrated test sets (1,363,226) (Seo et al, 2021a). However, models trained directly with this dataset showed an exponential decrease in P-wave detection accuracy with longer learning times.…”

Section: Design Of Machine Learning Filtermentioning

confidence: 99%

“…This helps to reduce the false alarm rate even if ML-based seismic detection misinterprets a nonearthquake signal as an earthquake, by relying on detection at a nearby observatory. The resulting technology is called the Korean on-site (KOS) EEW (Seo et al, 2021a). KOS combines a deep learning model with the initial P-wave detector after the filter- Frontiers in Earth Science frontiersin.org 03 picking step to increase the accuracy of event detection in a single station.…”

Section: Introductionmentioning

confidence: 99%

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Stable operation process of earthquake early warning system based on machine learning: trial test and management perspective

et al. 2023

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Earthquake Early Warning (EEW) is an alert system, based on seismic wave propagation theory, to reduce human casualties. EEW systems mainly utilize technologies through both network-based and on-site methods. The network-based method estimates the hypocenter and magnitude of an earthquake using data from multiple seismic stations, while the on-site method predicts the intensity measures from a single seismic station. Therefore, the on-site method reduces the lead time compared to the network-based method but is less accurate. To increase the accuracy of on-site EEW, our system was designed with a hybrid method, which included machine learning algorithms. At this time, machine learning was used to increase the accuracy of the initial P-wave identification rate. Additionally, a new approach using a nearby seismic station, called the 1+ α method, was proposed to reduce false alarms. In this study, an on-site EEW trial operation was performed to evaluate its performance. The warning cases for small and large events were reviewed and the possibility of stable alert decisions was confirmed.

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Section: Methodsmentioning

confidence: 99%

Section: Design Of Machine Learning Filtermentioning

confidence: 99%

Section: Design Of Machine Learning Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stable operation process of earthquake early warning system based on machine learning: trial test and management perspective

et al. 2023

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“…Korean on-site EEW 25 applied a deep learning model to the initial P-wave detector. The deep learning model was combined with the P-wave detector developed after the filter-picker step to increase the accuracy of event detection in a single station.…”

Section: Introductionmentioning

confidence: 99%

Stable Alert Decision Method and Machine Learning based P-Wave Detection for On-Site Earthquake Early Warning

Park

Ahn

Hwang

et al. 2022

Preprint

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Proactively responding to earthquakes is challenging. Researchers have developed methods to reduce structural damage from earthquakes. The Earthquake Early Warning (EEW) service is more effective than seismic design at reducing human casualties. We developed an on-site EEW that can provide alerts faster than the network-based EEW. However, on-site EEW is not used as a public service due to the low alarm accuracy of the existing on-site alarm. Therefore, we applied both methods to improve the alarming accuracy of on-site EEW. First, the developed system increased the accuracy of the identification rate for the initial P-wave using deep learning. Second, the method utilizing a nearby seismometer was added to improve the accuracy of the alarm. We conducted an on-site EEW trial operation and checked the performance from 2020.10.08 to 2022.03.31. We reviewed the warning cases for small and large events during the trial and confirmed the possibility of automated alert decisions.

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Determination of High-pass Filter Frequency with Deep Learning for Ground Motion

Lee,

Seo,

Jeon

2024

J. Earthq. Eng. Soc. Korea

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Deep Learning-Based, Real-Time, False-Pick Filter for an Onsite Earthquake Early Warning (EEW) System

Cited by 3 publications

References 0 publications

Stable operation process of earthquake early warning system based on machine learning: trial test and management perspective

Stable operation process of earthquake early warning system based on machine learning: trial test and management perspective

Stable Alert Decision Method and Machine Learning based P-Wave Detection for On-Site Earthquake Early Warning

Determination of High-pass Filter Frequency with Deep Learning for Ground Motion

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